风控模型开发验证报告

实验 ID: DEV-mw05x91a-1345 · 生成时间: 2026-11-03 12:04 (GMT+8) · 报告结构：标准14节模型开发验证模板

00 模型概要信息

项目内容
模型名称exp_DEV-mw05p6yt-b571
开发完成日期2026-11-03
模型开发人员（人工填写）
模型开发人员邮件地址（人工填写）
模型验证人员（人工填写）
模型验证人员邮件地址（人工填写）
模型应用环节collection_scorecard
模型应用频率（人工填写）
模型开发背景及业务用途描述Develop run with tuning
模型适用产品/客户描述（人工填写）
模型重开发原因（如有）—
模型拟监控频率（人工填写）
模型路线LightGBM
目标变量target
实验 IDDEV-mw05x91a-1345
待补充：模型开发/验证人员、应用频率、产品描述等字段需人工补填。

01 模型设计

建模数据样本窗口

项目内容
切分方式外部 data_flag 划分（TRAIN 内 6/4 随机开发/测试，VALI 跨时间，PSI 时点）
开发样本(Train)窗口—
预留验证样本窗口—
跨时间验证样本窗口—
PSI时点样本窗口—

模型好坏定义

项目内容
目标变量target
坏样本(BAD=1)占比89.72%
好样本(BAD=0)占比—
不确定样本本流程要求目标变量已预计算为 0/1 二值，不确定样本请在上游合并口径

样本排除规则

序号排除规则说明
1id_no主键/时间/口径字段，不入模
2data_mon主键/时间/口径字段，不入模
3data_flag主键/时间/口径字段，不入模
4target 为空排除 0 行

滚动率分析

待补充：滚动率分析（M0→M6+ 状态迁移矩阵）需要逾期状态口径数据，当前流程目标变量为上游已计算的二值标签，暂未生成。
待补充：表现窗口及坏账率分析、模型细分定义需结合业务口径人工补充。

02 数据流

注：中文字典已加载（dictionary.csv，匹配 1 个字段）；以下字段未在字典中、报告显示英文原名，请补充字典：id_no、loan_no、data_mon、target、last_con_amt、sum_apply_amt、max_apply_amt、min_apply_amt、avg_apply_amt、valid_sum_apply_amt、last_apply_amt、f_apply_amt、apply_num、max_apprv_amt、min_apprv_amt、avg_apprv_amt、last_apprv_amt、f_apprv_amt、apprv_num、sum_orig_prcp、max_orig_prcp、min_orig_prcp、avg_orig_prcp、last_orig_prcp、f_orig_prcp、loan_num、loan_os_prcp、pp_num、apply_amt_day、apprv_amt_day 等共 584 个。

开发时点 — 样本排除

类别样本数
全部样本13187
排除：目标变量为空0
排除后样本13187

注：按照01模型设计的排除规则统计；当前流程的排除项为目标变量缺失。

总体样本概况

数据集样本量好样本坏样本坏样本率占比
全部样本1318713551183289.72%100.00%
开发样本6858710614889.65%52.01%
测试样本4570472409889.67%34.66%
训练+测试样本1142811821024689.66%86.66%
跨时间验证样本106514092586.85%8.08%
PSI时点样本69433661—5.26%

开发/预留验证样本交叉统计

类别开发+预留样本数开发样本数开发占比预留验证样本数预留验证占比
好118271060.07%47239.93%
坏10246614860.00%409840.00%
合计11428685860.01%457039.99%

细致变量分析（预分箱口径，开发样本）

变量IV缺失率取值集中度最大相关系数
odgt0_m12_num0.34260.00%43.53%0.9372
odgt0_m6_num0.33710.00%45.16%0.9330
odgt0_conti_max_mons_m120.33300.00%50.82%0.9372
ovd_days_d3_month_m60.31920.00%60.22%0.9566
ovd_days_d3_month_m120.31920.00%60.22%0.9566
odgt0_m3_num0.30630.00%48.47%0.9553
odgt0_m6_mob_pc0.30000.00%31.76%0.9052
odgt0_m3_mob_pc0.29050.00%44.91%0.9553
ovd_days_d3_month_m30.28180.00%60.22%0.9102
odgt0_m12_mob_pc0.24340.00%12.72%0.9052
m_c_over_due_cnt0.24270.00%66.64%0.7705
acc_ps_perd_no0.18660.00%25.28%0.8474
ovd_days_d3_count_m120.17490.00%36.56%0.9967
last_od_num_m120.17210.00%37.27%0.9967
ovd_days_d3_count_m60.16890.00%36.66%0.9962
last_od_num_m60.16610.00%37.36%0.9962
ovd_days_d3_count_m30.13090.00%37.49%0.9951
last_od_num_m30.12780.00%38.20%0.9951
all_repay_amount_m3_shd_amt_pc0.12340.00%31.12%0.8383
od_amt_m3_loan_os_prcp_m3_pc0.11570.00%2.04%0.8345
loan_os_prcp0.11460.00%0.38%1.0000
loan_os_prcp_claim0.11460.00%0.38%1.0000
max_loan_os_prcp_m10.11340.00%0.38%0.9998
setl_prcp_ratio0.11200.00%5.29%0.9318
loan_os_prcp_orig_prcp_pc0.10860.00%4.20%0.9318
orig_prcp_avg_m120.10760.00%7.55%0.9497
repay_amount_last_month_loan_os_prcp_pc0.10270.00%65.62%0.5754
balance_avg_m30.10070.00%0.15%1.0000
balance_avg_m3_claim0.10070.00%0.15%1.0000
od_amt_m6_loan_os_prcp_m6_pc0.09960.00%1.95%0.8345
repay_amount_m3_loan_os_prcp_pc0.09690.00%15.16%0.9214
last_loan_setl_tnr0.08930.00%10.73%0.8697
od_amt_loan_os_prcp_pc0.08690.00%8.71%0.9881
ovd_days_max_m120.08650.00%4.78%1.0000
max_od_days_m120.08650.00%4.80%1.0000
max_loan_os_prcp_m30.08640.00%0.41%0.9942
max_od_days0.08490.00%4.77%0.8966
ovd_days_max_m60.08470.00%4.78%1.0000
max_od_days_m60.08470.00%4.80%1.0000
repay_amount_all_shd_amt_pc0.08330.00%63.34%0.7687
ps_od_norm_int_amt0.08220.00%0.09%0.8449
balance_avg_m60.07960.00%0.13%1.0000
balance_avg_m6_claim0.07960.00%0.13%1.0000
avg_od_amt_m3_orig_prcp_pc0.07570.00%26.82%0.9596
ovd_days_max_m30.07510.00%4.84%1.0000
max_od_days_m30.07510.00%4.86%1.0000
max_od_due_m120.07380.00%90.93%0.9982
max_od_due_m60.07200.00%90.94%0.9982
max_od_due0.07180.00%90.86%0.8966
p_mob3_neverdelq_total0.07005.88%65.41%0.4265
apply_amt_avg_m60.06520.00%68.87%0.9529
max_loan_os_prcp_m60.06510.00%1.06%0.9812
apply_amt_avg_m120.06450.00%24.21%0.8431
max_od_due_m30.05970.00%91.34%0.9892
cur_od_loan_amt0.05810.00%1.63%0.8744
avg_od_amt_m6_orig_prcp_pc0.05730.00%21.30%0.9729
all_repay_amount_m6_shd_amt_pc0.05710.00%37.15%0.9488
orig_prcp_avg_m60.05570.00%65.70%0.9529
last_con_amt0.05560.00%6.84%0.9966
max_setl_rem_prcp_paym_m3_last_month_loan_os_prcp_pc0.05500.00%69.55%0.9318
ovd_days_d30_num_m60.05500.00%76.13%0.7081
last_orig_prcp0.05470.00%6.85%0.9966
max_orig_prcp0.05190.00%8.65%0.9185
avg_orig_prcp0.05140.00%2.73%0.9624
last_ovd_to_last_setl_dt_freq0.05111.14%2.35%1.0000
sum_month3_new_loan0.05095.88%21.94%0.9893
all_repay_amount_shd_amt_pc0.05080.00%60.59%0.7687
useratemore90_m6_num0.05020.00%64.25%0.9405
repay_amount_m6_loan_os_prcp_pc0.04950.00%21.77%0.9845
sum_loan_mob24_neverdur0.04935.88%3.86%0.9333
loan_os_prcp_credit_amount_m120.04900.00%1.91%0.3077
loan_os_prcp_credit_amount0.04900.00%15.94%0.9835
od_amt_orig_prcp_pc0.04860.00%31.60%0.8810
loan_os_prcp_credit_amount_m30.04830.00%15.54%0.9835
useratemore50_m3_num_pc0.04790.00%53.31%0.9967
od_amt_m12_loan_os_prcp_m12_pc0.04780.00%2.32%0.8597
loannum0.04785.88%2.22%0.9333
useratemore50monthsubcurmonth0.04760.00%38.54%0.7885
useratemore50_m6_num_pc0.04720.00%38.10%0.9576
useratemore70_m6_num_pc0.04680.00%50.58%0.9529
loan_os_prcp_credit_amount_m60.04680.00%14.63%0.9632
all_repay_amount_m12_shd_amt_pc0.04580.00%44.12%0.9488
balance_avg_m120.04520.00%0.12%1.0000
balance_avg_m12_claim0.04520.00%0.12%1.0000
ps_int_amt_avg0.04480.00%0.04%0.8468
end_loan_to_cur_freq0.04390.00%32.46%0.8135
total_997_cnt_3m0.04374.64%20.67%0.9893
nosvaing_bank_cnt_1m0.04364.64%65.57%0.9962
loanosprcpgt0m12maxcontimons0.04190.00%32.97%0.9924
account_age_loan_day0.04150.00%7.60%0.6156
grxfloanclearednum0.04106.11%4.05%0.8768
max_loan_os_prcp_m120.04000.00%2.61%0.9635
nosvaing_bank_org_1m0.03994.64%65.57%0.9962
nosvaing_bank_rate_1_30.03984.64%34.22%1.0000
useratemore90monthsubcurmonth0.03940.00%40.54%0.7689
account_age0.03840.00%8.15%0.6156
sum_open_loan_mob24_neverdur0.03795.86%17.29%0.6557
useratemore70monthsubcurmonth0.03770.00%28.73%0.7885
t_ps_od_days_max0.03700.00%73.43%0.7435
useratemore50_m12_num_pc0.03640.00%23.78%0.9127

注：共 581 个候选变量，按 IV 降序展示前 100 个；集中度 = 最大频次取值样本数 ÷ 非空样本数；相关性为皮尔逊（仅数值变量），完整数据见 variable_detail.json。

03 单变量分析（细分箱）

odgt0_m12_num — IV 0.3489，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]3004169283594.37%-0.6613
2(1.5, 2.5]1471126134591.43%-0.2092
3(2.5, 3.5]81611070686.52%0.2995
4(3.5, 6.5]114719994882.65%0.5976
5(6.5, inf]42010631474.76%1.0727
odgt0_m6_num — IV 0.3408，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]3117178293994.29%-0.6454
2(1.5, 2.5]1515133138291.22%-0.1823
3(2.5, 3.5]84212272085.51%0.3834
4(3.5, 4.5]56210046282.21%0.6282
5(4.5, inf]82217764578.47%0.8655
odgt0_conti_max_mons_m12 — IV 0.3367，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]3504207329794.09%-0.6094
2(1.5, 2.5]1479150132989.86%-0.0229
3(2.5, 3.5]72412360183.01%0.5722
4(3.5, 4.5]4517237984.04%0.4977
5(4.5, inf]70015854277.43%0.9259
ovd_days_d3_month_m6 — IV 0.3198，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]4427292413593.40%-0.4919
2(1.5, 2.5]92811980987.18%0.2419
3(2.5, 3.5]4998141883.77%0.5176
4(3.5, 5.5]55511344279.64%0.7947
5(5.5, inf]44910534476.61%0.9719
ovd_days_d3_month_m12 — IV 0.3198，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]4427292413593.40%-0.4919
2(1.5, 2.5]92811980987.18%0.2419
3(2.5, 3.5]4998141883.77%0.5176
4(3.5, 5.5]55511344279.64%0.7947
5(5.5, inf]44910534476.61%0.9719
odgt0_m6_mob_pc — IV 0.3159，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 29.17]2641148249394.40%-0.6654
2(29.17, 36.67]1449109134092.48%-0.3505
3(36.67, 63.33]114715299586.75%0.2798
4(63.33, 77.5]62010052083.87%0.5100
5(77.5, inf]100120180079.92%0.7773
ovd_days_d3_month_m3 — IV 0.3097，3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]4427292413593.40%-0.4919
2(1.5, 2.5]92811980987.18%0.2419
3(2.5, inf]1503299120480.11%0.7657
odgt0_m3_num — IV 0.3063，3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]3344194315094.20%-0.6287
2(1.5, 2.5]1749188156189.25%0.0420
3(2.5, inf]1765328143781.42%0.6813
odgt0_m3_mob_pc — IV 0.2926，3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 58.33]3252187306594.25%-0.6381
2(58.33, 83.33]1724186153889.21%0.0461
3(83.33, inf]1882337154582.09%0.6359
odgt0_m12_mob_pc — IV 0.2573，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 23.61]3102188291493.94%-0.5822
2(23.61, 39.75]1652165148790.01%-0.0400
3(39.75, 58.79]119520499182.93%0.5780
4(58.79, 73.87]3945134387.06%0.2527
5(73.87, inf]51510241380.19%0.7601
m_c_over_due_cnt — IV 0.2452，4 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]4729343438692.75%-0.3898
2(1.5, 2.5]1179172100785.41%0.3914
3(2.5, 3.5]4518436781.37%0.6841
4(3.5, inf]49911138877.76%0.9071
acc_ps_perd_no — IV 0.2055，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 2.5]3044205283993.27%-0.4696
2(2.5, 5.5]1812197161589.13%0.0547
3(5.5, 6.5]3512332893.45%-0.4989
4(6.5, 16.5]115518397284.16%0.4887
5(16.5, inf]49610239479.44%0.8072
tothismonthodcontiaddnumm12 — IV 0.1837，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]2992445254785.13%0.4140
2(0.5, inf]3866265360193.15%-0.4506
ovd_days_d3_count_m12 — IV 0.1807，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]2805180262593.58%-0.5213
2(1.5, 2.5]1408145126389.70%-0.0059
3(2.5, 3.5]6958261388.20%0.1470
4(3.5, 7.5]1186159102786.59%0.2931
5(7.5, inf]76414462081.15%0.6987
last_od_num_m12 — IV 0.1785，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]2715174254193.59%-0.5226
2(1.5, 2.5]1430144128689.93%-0.0309
3(2.5, 4.5]1236147108988.11%0.1560
4(4.5, 6.5]5537647786.26%0.3218
5(6.5, inf]92416975581.71%0.6618
ovd_days_d3_count_m6 — IV 0.1752，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]2814181263393.57%-0.5188
2(1.5, 2.5]1418149126989.49%0.0166
3(2.5, 5.5]1507202130586.60%0.2929
4(5.5, 7.5]3784133789.15%0.0521
5(7.5, inf]74113760481.51%0.6750
last_od_num_m6 — IV 0.1688，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]2723175254893.57%-0.5197
2(1.5, 2.5]1441147129489.80%-0.0164
3(2.5, 7.5]1934250168487.07%0.2512
4(7.5, 11.5]3667129580.60%0.7343
5(11.5, inf]3946732782.99%0.5734
loan_os_prcp — IV 0.1534，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 396.11]5249542981.87%0.6510
2(396.11, 2274.05]1844217162788.23%0.1440
3(2274.05, 2988.27]4327335983.10%0.5658
4(2988.27, 18390.08]2943269267490.86%-0.1380
5(18390.08, inf]111556105994.98%-0.7811
loan_os_prcp_claim — IV 0.1534，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 396.11]5249542981.87%0.6510
2(396.11, 2274.05]1844217162788.23%0.1440
3(2274.05, 2988.27]4327335983.10%0.5658
4(2988.27, 18390.08]2943269267490.86%-0.1380
5(18390.08, inf]111556105994.98%-0.7811
max_loan_os_prcp_m1 — IV 0.1465，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 396.11]5249542981.87%0.6510
2(396.11, 3956.77]2736338239887.65%0.1993
3(3956.77, 11249.56]1728142158691.78%-0.2545
4(11249.56, 18390.08]7557967689.54%0.0119
5(18390.08, inf]111556105994.98%-0.7811
loan_os_prcp_orig_prcp_pc — IV 0.1437，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 13.04]55011543579.09%0.8282
2(13.04, 53.4]3341372296988.87%0.0815
3(53.4, 67.97]10609796390.85%-0.1367
4(67.97, 76.46]5592853194.99%-0.7839
5(76.46, inf]134898125092.73%-0.3873
ovd_days_d3_count_m3 — IV 0.1350，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]2875191268493.36%-0.4842
2(1.5, 2.5]1510176133488.34%0.1332
3(2.5, 5.5]1507195131287.06%0.2523
4(5.5, 8.5]4847141385.33%0.3978
5(8.5, inf]4827740584.02%0.4985
all_repay_amount_m3_shd_amt_pc — IV 0.1342，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 66.67]3136229290792.70%-0.3825
2(66.67, 66.73]3773833989.92%-0.0298
3(66.73, 69.51]6178453386.39%0.3109
4(69.51, 99.99]69013056081.16%0.6982
5(99.99, inf]2038229180988.76%0.0918
last_od_num_m3 — IV 0.1321，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]2783184259993.39%-0.4893
2(1.5, 2.5]1528175135388.55%0.1133
3(2.5, 4.5]1301172112986.78%0.2770
4(4.5, 6.5]4986243687.55%0.2081
5(6.5, inf]74811763184.36%0.4735
ovd_days_d30_num_m6 — IV 0.1315，3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]5221437478491.63%-0.2345
2(0.5, 1.5]112817695284.40%0.4705
3(1.5, inf]5099741280.94%0.7123
od_amt_m3_loan_os_prcp_m3_pc — IV 0.1301，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 10.09]4903415448891.54%-0.2223
2(10.09, 14.35]7168063688.83%0.0854
3(14.35, 24.15]5439345082.87%0.5820
4(24.15, 25.53]3544530987.29%0.2319
5(25.53, inf]3427726577.49%0.9227
repay_amount_last_month_loan_os_prcp_pc — IV 0.1273，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.79]4558382417691.62%-0.2331
2(0.79, 8.66]3445029485.47%0.3871
3(8.66, 20.06]8198073990.23%-0.0647
4(20.06, 49.49]6028851485.38%0.3937
5(49.49, inf]53511042579.44%0.8070
setl_prcp_ratio — IV 0.1266，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.32]1812119169393.43%-0.4965
2(0.32, 0.42]9338285191.21%-0.1811
3(0.42, 0.87]3256354290289.13%0.0548
4(0.87, 1]85715570281.91%0.6481
5(1, inf]0000.00%2.1586
orig_prcp_avg_m12 — IV 0.1218，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 732.05]77914463581.51%0.6748
2(732.05, 1484.13]5987552387.46%0.2165
3(1484.13, 11908.33]3690367332390.05%-0.0446
4(11908.33, 24925]9117383891.99%-0.2819
5(24925, inf]8805182994.20%-0.6298
max_od_days_m12 — IV 0.1213，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4.5]4542842693.83%-0.5636
2(4.5, 7.5]5356646987.66%0.1977
3(7.5, 9.5]4082238694.61%-0.7062
4(9.5, 29.5]4579433414690.54%-0.1005
5(29.5, inf]88216172181.75%0.6594
ovd_days_max_m12 — IV 0.1204，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4.5]4532842593.82%-0.5613
2(4.5, 7.5]5366647087.69%0.1955
3(7.5, 9.5]4062238494.58%-0.7010
4(9.5, 29.5]4580433414790.55%-0.1008
5(29.5, inf]88316172281.77%0.6580
balance_avg_m3 — IV 0.1204，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 380.38]3525929383.24%0.5560
2(380.38, 2752.2]2049244180588.09%0.1575
3(2752.2, 4498.71]87712375485.97%0.3454
4(4498.71, 20051.68]2482228225490.81%-0.1325
5(20051.68, inf]109856104294.90%-0.7649
balance_avg_m3_claim — IV 0.1204，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 380.38]3525929383.24%0.5560
2(380.38, 2752.2]2049244180588.09%0.1575
3(2752.2, 4498.71]87712375485.97%0.3454
4(4498.71, 20051.68]2482228225490.81%-0.1325
5(20051.68, inf]109856104294.90%-0.7649
max_od_days_m6 — IV 0.1199，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4.5]4562842893.86%-0.5683
2(4.5, 7.5]5346646887.64%0.1998
3(7.5, 9.5]4072238594.59%-0.7036
4(9.5, 29.5]4581434414790.53%-0.0985
5(29.5, inf]88016072081.82%0.6545
ovd_days_max_m6 — IV 0.1190，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4.5]4552842793.85%-0.5660
2(4.5, 7.5]5356646987.66%0.1977
3(7.5, 9.5]4052238394.57%-0.6984
4(9.5, 29.5]4582434414890.53%-0.0987
5(29.5, inf]88116072181.84%0.6532
max_od_days — IV 0.1186，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4.5]4512842393.79%-0.5566
2(4.5, 7.5]5376647187.71%0.1934
3(7.5, 9.5]4062238494.58%-0.7010
4(9.5, 29.5]4576433414390.54%-0.0998
5(29.5, inf]88816172781.87%0.6511
ps_od_norm_int_amt — IV 0.1186，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 10.25]4818939281.50%0.6760
2(10.25, 44.34]1489162132789.12%0.0555
3(44.34, 107.69]1553195135887.44%0.2178
4(107.69, 757.16]2829243258691.41%-0.2062
5(757.16, inf]5062148595.85%-0.9810
max_loan_os_prcp_m3 — IV 0.1181，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 533.83]4066733983.50%0.5373
2(533.83, 8748.09]3764442332288.26%0.1416
3(8748.09, 24410.56]1674149152591.10%-0.1672
4(24410.56, 49979.18]6174057793.52%-0.5103
5(49979.18, inf]3971238596.98%-1.3097
od_amt_loan_os_prcp_pc — IV 0.1174，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 8.27]8378275590.20%-0.0614
2(8.27, 12.49]120780112793.37%-0.4867
3(12.49, 47.78]3558362319689.83%-0.0194
4(47.78, 50.31]4002837293.00%-0.4281
5(50.31, inf]85615869881.54%0.6730
od_amt_m6_loan_os_prcp_m6_pc — IV 0.1159，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.36]7667569190.21%-0.0620
2(1.36, 3.57]3875318355791.79%-0.2560
3(3.57, 4.62]7558267389.14%0.0536
4(4.62, 9.86]96313283186.29%0.3188
5(9.86, inf]49910339679.36%0.8119
balance_avg_m6 — IV 0.1135，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 2044.23]1439204123585.82%0.3579
2(2044.23, 9679.95]2833313252088.95%0.0728
3(9679.95, 17757.73]10838599892.15%-0.3045
4(17757.73, 23714.46]4385338587.90%0.1757
5(23714.46, inf]106555101094.84%-0.7518
balance_avg_m6_claim — IV 0.1135，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 2044.23]1439204123585.82%0.3579
2(2044.23, 9679.95]2833313252088.95%0.0728
3(9679.95, 17757.73]10838599892.15%-0.3045
4(17757.73, 23714.46]4385338587.90%0.1757
5(23714.46, inf]106555101094.84%-0.7518
repay_amount_m3_loan_os_prcp_pc — IV 0.1131，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 22.59]1883127175693.26%-0.4680
2(22.59, 51.76]3007296271190.16%-0.0561
3(51.76, 66.23]4406737384.77%0.4417
4(66.23, 99.16]4878839981.93%0.6470
5(99.16, inf]104113290987.32%0.2291
repay_amount_all_shd_amt_pc — IV 0.1084，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 33.16]4535379415691.64%-0.2362
2(33.16, 84.39]3665531184.97%0.4262
3(84.39, 90.67]7709867287.27%0.2333
4(90.67, 96.6]6767859888.46%0.1217
5(96.6, inf]51110041180.43%0.7452
max_od_days_m3 — IV 0.1068，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4.5]4613043193.49%-0.5063
2(4.5, 7.5]5356447188.04%0.1626
3(7.5, 9.5]4082238694.61%-0.7062
4(9.5, 29.5]4603442416190.40%-0.0836
5(29.5, inf]85115269982.14%0.6328
ovd_days_max_m3 — IV 0.1060，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4.5]4603043093.48%-0.5040
2(4.5, 7.5]5366447288.06%0.1605
3(7.5, 9.5]4062238494.58%-0.7010
4(9.5, 29.5]4604442416290.40%-0.0838
5(29.5, inf]85215270082.16%0.6314
end_loan_to_cur_freq — IV 0.1019，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, -362.5]4208433680.00%0.7723
2(-362.5, -248.5]1766199156788.73%0.0950
3(-248.5, -109.5]1877159171891.53%-0.2214
4(-109.5, -26.5]5622853495.02%-0.7896
5(-26.5, inf]2233240199389.25%0.0419
all_repay_amount_shd_amt_pc — IV 0.0980，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 3.05]4206353385391.61%-0.2315
2(3.05, 43.26]4746740785.87%0.3545
3(43.26, 50.34]4554940689.23%0.0441
4(50.34, 99.97]3477427378.67%0.8532
5(99.97, inf]1376167120987.86%0.1791
last_loan_setl_tnr — IV 0.0978，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]6454160493.64%-0.5314
2(1.5, 5.5]2418209220991.36%-0.1993
3(5.5, 7.5]1393158123588.66%0.1024
4(7.5, 10.5]1736177155989.80%-0.0170
5(10.5, inf]66612554181.23%0.6935
p_mob3_neverdelq_total — IV 0.0971，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 89.93]5979550284.09%0.4977
2(89.93, 94.01]3687229680.43%0.7487
3(94.01, 96.6]4485139788.62%0.1103
4(96.6, 97.99]3562333393.54%-0.5102
5(97.99, inf]4686425426190.93%-0.1427
max_loan_os_prcp_m6 — IV 0.0965，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 916.64]3495729283.67%0.5249
2(916.64, 1666.65]4053836790.62%-0.1092
3(1666.65, 6861.94]2100271182987.10%0.2492
4(6861.94, 52296.75]3459321313890.72%-0.1213
5(52296.75, inf]5452352295.78%-0.9636
cur_od_loan_amt — IV 0.0936，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 124.01]5728648684.97%0.4268
2(124.01, 558.05]1958227173188.41%0.1271
3(558.05, 725.59]4456837784.72%0.4459
4(725.59, 3545.66]3012283272990.60%-0.1076
5(3545.66, inf]8714682594.72%-0.7281
odduecontireducenumm12 — IV 0.0908，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]5102440466291.38%-0.2018
2(0.5, inf]1756270148684.62%0.4532
odmaxreduceduem12 — IV 0.0908，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]5102440466291.38%-0.2018
2(0.5, inf]1756270148684.62%0.4532
loan_os_prcp_credit_amount — IV 0.0907，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, -4998.92]109312097389.02%0.0657
2(-4998.92, 9.88]77212964383.29%0.5523
3(9.88, 17.78]5526948387.50%0.2127
4(17.78, 52.45]1949208174189.33%0.0339
5(52.45, inf]2492184230892.62%-0.3706
max_setl_rem_prcp_paym_m3_last_month_loan_os_prcp_pc — IV 0.0888，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.88]4795544425188.65%0.1027
2(1.88, 11.68]5442052496.32%-1.1071
3(11.68, 20.03]6655860791.28%-0.1895
4(20.03, 33.46]3582433493.30%-0.4745
5(33.46, inf]4966443287.10%0.2491
avg_od_amt_m3_orig_prcp_pc — IV 0.0878，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 2.22]5507048087.27%0.2333
2(2.22, 3.04]4069337373291.72%-0.2460
3(3.04, 5.76]1271148112388.36%0.1321
4(5.76, 9.35]5599946082.29%0.6225
5(9.35, inf]4095635386.31%0.3175
all_repay_amount_m6_shd_amt_pc — IV 0.0875，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 75.01]8946183393.18%-0.4555
2(75.01, 83.35]2432210222291.37%-0.2004
3(83.35, 83.65]4556539085.71%0.3669
4(83.65, 99.56]5249443082.06%0.6381
5(99.56, inf]2553280227389.03%0.0646
all_repay_amount_m12_shd_amt_pc — IV 0.0849，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 83.35]1564107145793.16%-0.4527
2(83.35, 89.74]1218140107888.51%0.1174
3(89.74, 91.06]5615051191.09%-0.1657
4(91.06, 99.75]4858939681.65%0.6658
5(99.75, inf]3030324270689.31%0.0361
loan_os_prcp_credit_amount_m6 — IV 0.0834，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 2.45]104911593489.04%0.0641
2(2.45, 24.34]106115690585.30%0.4005
3(24.34, 65.7]2221248197388.83%0.0847
4(65.7, 113.44]2140148199293.08%-0.4411
5(113.44, inf]3874334488.89%0.0792
max_orig_prcp — IV 0.0800，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1850]6759757885.63%0.3738
2(1850, 11050]3580406317488.66%0.1022
3(11050, 30650]1619128149192.09%-0.2966
4(30650, 47050]3704732387.30%0.2311
5(47050, inf]6143258294.79%-0.7421
balance_avg_m12 — IV 0.0799，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4286.8]2115275184087.00%0.2579
2(4286.8, 5786.94]6335957490.68%-0.1165
3(5786.94, 6764.49]3685031886.41%0.3086
4(6764.49, 48615.01]3314309300590.68%-0.1161
5(48615.01, inf]4281741196.03%-1.0268
balance_avg_m12_claim — IV 0.0799，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4286.8]2115275184087.00%0.2579
2(4286.8, 5786.94]6335957490.68%-0.1165
3(5786.94, 6764.49]3685031886.41%0.3086
4(6764.49, 48615.01]3314309300590.68%-0.1161
5(48615.01, inf]4281741196.03%-1.0268
apply_amt_avg_m12 — IV 0.0793，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4525]3004381262387.32%0.2293
2(4525, 10810]1167124104389.37%0.0290
3(10810, 32366.67]1481110137192.57%-0.3642
4(32366.67, 48025]5025344989.44%0.0219
5(48025, inf]7044266294.03%-0.5990
ovd_days_d30_count_m12 — IV 0.0772，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]6258591566790.56%-0.1020
2(0.5, inf]60011948180.17%0.7619
orig_prcp_avg_m6 — IV 0.0769，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 636.67]4606542406488.23%0.1440
2(636.67, 2158.33]4704642490.21%-0.0625
3(2158.33, 5016.67]5274748091.08%-0.1650
4(5016.67, 15012.5]6314658592.71%-0.3844
5(15012.5, inf]6242959595.35%-0.8626
ps_int_amt_avg — IV 0.0765，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 12.24]6468656086.69%0.2850
2(12.24, 17.12]4647439084.05%0.4965
3(17.12, 22.93]4573442392.56%-0.3624
4(22.93, 114.22]3253366288788.75%0.0933
5(114.22, inf]2038150188892.64%-0.3740
apply_amt_avg_m6 — IV 0.0759，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 636.67]4787562422588.26%0.1413
2(636.67, 4525]6566259490.55%-0.1011
3(4525, 15100]6654562093.23%-0.4644
4(15100, 32314.29]3581634295.53%-0.9036
5(32314.29, inf]3922536793.62%-0.5279
m1_ovd_days_num — IV 0.0755，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]6253591566290.55%-0.1011
2(0.5, inf]60511948680.33%0.7515
ovd_days_d30_month_m3 — IV 0.0755，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]6253591566290.55%-0.1011
2(0.5, inf]60511948680.33%0.7515
ovd_days_d30_month_m6 — IV 0.0755，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]6253591566290.55%-0.1011
2(0.5, inf]60511948680.33%0.7515
ovd_days_d30_month_m12 — IV 0.0755，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]6253591566290.55%-0.1011
2(0.5, inf]60511948680.33%0.7515
ovd_days_d30_count_m6 — IV 0.0754，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]6260592566890.54%-0.1005
2(0.5, inf]59811848080.27%0.7555
avg_od_amt_m6_orig_prcp_pc — IV 0.0751，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.3]5746650888.50%0.1178
2(1.3, 2.9]4994449454591.01%-0.1561
3(2.9, 4.18]5947951586.70%0.2839
4(4.18, 6.97]3467227479.19%0.8222
5(6.97, inf]3504430687.43%0.2192
odgt1_m12_mob_pc — IV 0.0738，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4.16]6255592566390.54%-0.0996
2(4.16, inf]60311848580.43%0.7452
odgt1_m12_num — IV 0.0738，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]6255592566390.54%-0.0996
2(0.5, inf]60311848580.43%0.7452
odgt1_m12_subcurmonth — IV 0.0738，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 50003]60311848580.43%0.7452
2(50003, inf]6255592566390.54%-0.0996
max_od_due_m12 — IV 0.0738，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]6255592566390.54%-0.0996
2(1.5, inf]60311848580.43%0.7452
odgt1_conti_max_mons_m12 — IV 0.0738，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]6255592566390.54%-0.0996
2(0.5, inf]60311848580.43%0.7452
account_age_loan_day — IV 0.0736，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.52]1645118152792.83%-0.4018
2(0.52, 0.96]2199240195989.09%0.0591
3(0.96, 1.19]64610753983.44%0.5417
4(1.19, 4.62]1453137131690.57%-0.1038
5(4.62, inf]91510880788.20%0.1474
repay_amount_m6_loan_os_prcp_pc — IV 0.0729，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 42.01]2194173202192.11%-0.2994
2(42.01, 70.26]2298228207090.08%-0.0473
3(70.26, 82.59]5236945486.81%0.2746
4(82.59, 99.67]3496828180.52%0.7398
5(99.67, inf]1494172132288.49%0.1192
sum_orig_prcp — IV 0.0720，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 10450]2505314219187.47%0.2159
2(10450, 12554]3552133494.08%-0.6080
3(12554, 19150]8259672988.36%0.1313
4(19150, 110100]2809263254690.64%-0.1115
5(110100, inf]3641634895.60%-0.9210
odgt1_m6_mob_pc — IV 0.0720，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 8.34]6257593566490.52%-0.0981
2(8.34, inf]60111748480.53%0.7387
odgt1_m6_num — IV 0.0720，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]6257593566490.52%-0.0981
2(0.5, inf]60111748480.53%0.7387
max_od_due_m6 — IV 0.0720，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]6257593566490.52%-0.0981
2(1.5, inf]60111748480.53%0.7387
max_od_due — IV 0.0718，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]6249592565790.53%-0.0985
2(1.5, inf]60911849180.62%0.7329
loan_os_prcp_credit_amount_m3 — IV 0.0716，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.22]109811997989.16%0.0512
2(1.22, 8.26]4317535682.60%0.6012
3(8.26, 18.51]6708958186.72%0.2825
4(18.51, 51.44]1860206165488.92%0.0755
5(51.44, inf]2799221257892.10%-0.2980
loannum — IV 0.0712，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 9.5]4357036583.91%0.5110
2(9.5, 27.5]2020242177888.02%0.1681
3(27.5, 39.5]10388795191.62%-0.2292
4(39.5, 61.5]1212137107588.70%0.1023
5(61.5, inf]1750130162092.57%-0.3602
sum_loan_mob24_neverdur — IV 0.0705，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 10.5]2290296199487.07%0.2549
2(10.5, 22.5]1485155133089.56%0.0129
3(22.5, 27.5]3712135094.34%-0.6510
4(27.5, 43.5]8959080589.94%-0.0286
5(43.5, inf]1414104131092.64%-0.3710
last_ovd_to_last_setl_dt_freq — IV 0.0662，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 22.5]4842945594.01%-0.5929
2(22.5, 201.5]2476281219588.65%0.1045
3(201.5, 352.5]2532221231191.27%-0.1872
4(352.5, 390.5]92110481788.71%0.0988
5(390.5, inf]3676630182.02%0.6426
ovd_days_d30_count_m3 — IV 0.0659，2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]6289600568990.46%-0.0907
2(0.5, inf]56911045980.67%0.7300
sum_card_mob12_neverdur — IV 0.0657，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]2098252184687.99%0.1711
2(0.5, 1.5]8347775790.77%-0.1231
3(1.5, 6.5]1857214164388.48%0.1241
4(6.5, 8.5]3351332296.12%-1.0472
5(8.5, inf]1331110122191.74%-0.2445
last_con_amt — IV 0.0656，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1150]114915999086.16%0.3298
2(1150, 2950]10809798391.02%-0.1573
3(2950, 7850]1739211152887.87%0.1788
4(7850, 47850]2435218221791.05%-0.1608
5(47850, inf]4552543094.51%-0.6863
od_amt_m12_loan_os_prcp_m12_pc — IV 0.0654，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.91]1241129111289.61%0.0045
2(0.91, 1.09]3855133486.75%0.2793
3(1.09, 2.17]3540299324191.55%-0.2246
4(2.17, 3.17]7828969388.62%0.1062
5(3.17, inf]91014276884.40%0.4707
setl_int_mon — IV 0.0652，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 3.64]5009457455290.88%-0.1400
2(3.64, 17.71]3445928582.85%0.5837
3(17.71, 42.33]3704632487.57%0.2065
4(42.33, 90.6]3776731082.23%0.6267
5(90.6, inf]7588167789.31%0.0354
last_orig_prcp — IV 0.0651，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1150]115015999186.17%0.3288
2(1150, 2950]10779798090.99%-0.1542
3(2950, 7850]1739211152887.87%0.1788
4(7850, 47850]2439218222191.06%-0.1626
5(47850, inf]4532542894.48%-0.6816
repay_amount_m3_all_shd_amt_pc — IV 0.0651，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 82.04]4552942693.63%-0.5285
2(82.04, 86.14]5445349190.26%-0.0675
3(86.14, 87.85]3634831586.78%0.2772
4(87.85, 94.69]3819345347490.97%-0.1509
5(94.69, inf]1677235144285.99%0.3444
setl_rem_prcp_paym_m3_loan_os_prcp_pc — IV 0.0649，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, -4999]3472232593.66%-0.5342
2(-4999, 3.21]4506525398188.35%0.1327
3(3.21, 19.05]8575080794.17%-0.6227
4(19.05, 25.49]3654232388.49%0.1186
5(25.49, inf]7837171290.93%-0.1468
avg_orig_prcp — IV 0.0647，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 876.79]3585830083.80%0.5153
2(876.79, 1493.1]5978151686.43%0.3070
3(1493.1, 10007.14]3924419350589.32%0.0345
4(10007.14, 42550]1566131143591.63%-0.2351
5(42550, inf]4132139294.92%-0.7681
max_loan_os_prcp_m12 — IV 0.0642，5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1305.9]3435029385.42%0.3905
2(1305.9, 5100.68]1326155117188.31%0.1364
3(5100.68, 7500.04]5558047585.59%0.3773
4(7500.04, 63002.38]4096398369890.28%-0.0705
5(63002.38, inf]5382751194.98%-0.7819

注：统计全部候选变量的自动分箱明细（初分组），按 IV 降序展示前 100 个变量，共 568 个；完整数据见 feature_report.json。

04 单变量分析（粗分箱）

odgt0_m12_num — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]3004169283594.37%-0.6613
2(1.5, 2.5]1471126134591.43%-0.2092
3(2.5, 3.5]81611070686.52%0.2995
4(3.5, 6.5]114719994882.65%0.5976
5(6.5, inf]42010631474.76%1.0727
2026-11-03T12:04:07.393477 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调下降（94.37% → 74.76%），WOE 单调上升（-0.6613 → 1.0727），符合风控分箱预期。IV=0.3489，该变量区分度尚可。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 1 箱（(-inf, 1.5]）为极高风险区间：坏率高达 94.37%，策略上应重点管控

odgt0_m12_mob_pc — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 23.61]3102188291493.94%-0.5822
2(23.61, 39.75]1652165148790.01%-0.0400
3(39.75, 58.79]119520499182.93%0.5780
4(58.79, 73.87]3945134387.06%0.2527
5(73.87, inf]51510241380.19%0.7601
2026-11-03T12:04:07.530816 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（93.94% → 93.94% → 80.19%），如需严格单调可考虑调整切点或合并分箱。IV=0.2573，该变量区分度尚可。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 3→4 箱有局部反弹：Bad Rate 由 82.93% 回升至 87.06%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 23.61]）为极高风险区间：坏率高达 93.94%，策略上应重点管控

m_c_over_due_cnt — 4 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]4729343438692.75%-0.3898
2(1.5, 2.5]1179172100785.41%0.3914
3(2.5, 3.5]4518436781.37%0.6841
4(3.5, inf]49911138877.76%0.9071
2026-11-03T12:04:07.702984 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调下降（92.75% → 77.76%），WOE 单调上升（-0.3898 → 0.9071），符合风控分箱预期。IV=0.2452，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 1 箱（(-inf, 1.5]）为极高风险区间：坏率高达 92.75%，策略上应重点管控

acc_ps_perd_no — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 2.5]3044205283993.27%-0.4696
2(2.5, 5.5]1812197161589.13%0.0547
3(5.5, 6.5]3512332893.45%-0.4989
4(6.5, 16.5]115518397284.16%0.4887
5(16.5, inf]49610239479.44%0.8072
2026-11-03T12:04:07.832383 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（93.27% → 93.45% → 79.44%），如需严格单调可考虑调整切点或合并分箱。IV=0.2055，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 89.13% 回升至 93.45%，如需严格单调可考虑合并这两箱

第 3 箱（(5.5, 6.5]）为极高风险区间：坏率高达 93.45%，策略上应重点管控

tothismonthodcontiaddnumm12 — 2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]2992445254785.13%0.4140
2(0.5, inf]3866265360193.15%-0.4506
2026-11-03T12:04:07.952306 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调上升（85.13% → 93.15%），WOE 单调下降（0.4140 → -0.4506），符合风控分箱预期。IV=0.1837，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 1 箱 WOE<0（风险高于平均）

第 2 箱（(0.5, inf]）为极高风险区间：坏率高达 93.15%，策略上应重点管控

loan_os_prcp_orig_prcp_pc — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 13.04]55011543579.09%0.8282
2(13.04, 53.4]3341372296988.87%0.0815
3(53.4, 67.97]10609796390.85%-0.1367
4(67.97, 76.46]5592853194.99%-0.7839
5(76.46, inf]134898125092.73%-0.3873
2026-11-03T12:04:08.078736 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（79.09% → 94.99% → 92.73%），如需严格单调可考虑调整切点或合并分箱。IV=0.1437，该变量区分度尚可。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 4→5 箱有局部反弹：Bad Rate 由 94.99% 回落至 92.73%，如需严格单调可考虑合并这两箱

第 4 箱（(67.97, 76.46]）为极高风险区间：坏率高达 94.99%，策略上应重点管控

all_repay_amount_m3_shd_amt_pc — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 66.67]3136229290792.70%-0.3825
2(66.67, 66.73]3773833989.92%-0.0298
3(66.73, 69.51]6178453386.39%0.3109
4(69.51, 99.99]69013056081.16%0.6982
5(99.99, inf]2038229180988.76%0.0918
2026-11-03T12:04:08.208041 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（92.70% → 92.70% → 88.76%），如需严格单调可考虑调整切点或合并分箱。IV=0.1342，该变量区分度尚可。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 4→5 箱有局部反弹：Bad Rate 由 81.16% 回升至 88.76%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 66.67]）为极高风险区间：坏率高达 92.70%，策略上应重点管控

ovd_days_d30_num_m6 — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]5221437478491.63%-0.2345
2(0.5, 1.5]112817695284.40%0.4705
3(1.5, inf]5099741280.94%0.7123
2026-11-03T12:04:08.323570 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调下降（91.63% → 80.94%），WOE 单调上升（-0.2345 → 0.7123），符合风控分箱预期。IV=0.1315，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 2 箱 WOE<0（风险高于平均）

第 1 箱（(-inf, 0.5]）为极高风险区间：坏率高达 91.63%，策略上应重点管控

od_amt_m3_loan_os_prcp_m3_pc — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 10.09]4903415448891.54%-0.2223
2(10.09, 14.35]7168063688.83%0.0854
3(14.35, 24.15]5439345082.87%0.5820
4(24.15, 25.53]3544530987.29%0.2319
5(25.53, inf]3427726577.49%0.9227
2026-11-03T12:04:08.443680 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（91.54% → 91.54% → 77.49%），如需严格单调可考虑调整切点或合并分箱。IV=0.1301，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 3→4 箱有局部反弹：Bad Rate 由 82.87% 回升至 87.29%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 10.09]）为极高风险区间：坏率高达 91.54%，策略上应重点管控

repay_amount_last_month_loan_os_prcp_pc — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.79]4558382417691.62%-0.2331
2(0.79, 8.66]3445029485.47%0.3871
3(8.66, 20.06]8198073990.23%-0.0647
4(20.06, 49.49]6028851485.38%0.3937
5(49.49, inf]53511042579.44%0.8070
2026-11-03T12:04:08.571705 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（91.62% → 91.62% → 79.44%），如需严格单调可考虑调整切点或合并分箱。IV=0.1273，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 85.47% 回升至 90.23%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 0.79]）为极高风险区间：坏率高达 91.62%，策略上应重点管控

max_od_days_m12 — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4.5]4542842693.83%-0.5636
2(4.5, 7.5]5356646987.66%0.1977
3(7.5, 9.5]4082238694.61%-0.7062
4(9.5, 29.5]4579433414690.54%-0.1005
5(29.5, inf]88216172181.75%0.6594
2026-11-03T12:04:08.699736 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（93.83% → 94.61% → 81.75%），如需严格单调可考虑调整切点或合并分箱。IV=0.1213，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 87.66% 回升至 94.61%，如需严格单调可考虑合并这两箱

第 3 箱（(7.5, 9.5]）为极高风险区间：坏率高达 94.61%，策略上应重点管控

ps_od_norm_int_amt — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 10.25]4818939281.50%0.6760
2(10.25, 44.34]1489162132789.12%0.0555
3(44.34, 107.69]1553195135887.44%0.2178
4(107.69, 757.16]2829243258691.41%-0.2062
5(757.16, inf]5062148595.85%-0.9810
2026-11-03T12:04:08.830959 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（81.50% → 95.85% → 95.85%），如需严格单调可考虑调整切点或合并分箱。IV=0.1186，该变量区分度尚可。

WOE 在第 3、4 箱之间穿越 0 线：前 3 箱 WOE>0（客户质量优于整体），后 2 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 89.12% 回落至 87.44%，如需严格单调可考虑合并这两箱

第 5 箱（(757.16, inf]）为极高风险区间：坏率高达 95.85%，策略上应重点管控

repay_amount_all_shd_amt_pc — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 33.16]4535379415691.64%-0.2362
2(33.16, 84.39]3665531184.97%0.4262
3(84.39, 90.67]7709867287.27%0.2333
4(90.67, 96.6]6767859888.46%0.1217
5(96.6, inf]51110041180.43%0.7452
2026-11-03T12:04:09.024568 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（91.64% → 91.64% → 80.43%），如需严格单调可考虑调整切点或合并分箱。IV=0.1084，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 84.97% 回升至 87.27%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 33.16]）为极高风险区间：坏率高达 91.64%，策略上应重点管控

end_loan_to_cur_freq — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, -362.5]4208433680.00%0.7723
2(-362.5, -248.5]1766199156788.73%0.0950
3(-248.5, -109.5]1877159171891.53%-0.2214
4(-109.5, -26.5]5622853495.02%-0.7896
5(-26.5, inf]2233240199389.25%0.0419
2026-11-03T12:04:09.155812 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（80.00% → 95.02% → 89.25%），如需严格单调可考虑调整切点或合并分箱。IV=0.1019，该变量区分度尚可。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 4→5 箱有局部反弹：Bad Rate 由 95.02% 回落至 89.25%，如需严格单调可考虑合并这两箱

第 4 箱（(-109.5, -26.5]）为极高风险区间：坏率高达 95.02%，策略上应重点管控

cur_od_loan_amt — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 124.01]5728648684.97%0.4268
2(124.01, 558.05]1958227173188.41%0.1271
3(558.05, 725.59]4456837784.72%0.4459
4(725.59, 3545.66]3012283272990.60%-0.1076
5(3545.66, inf]8714682594.72%-0.7281
2026-11-03T12:04:09.282454 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（84.97% → 94.72% → 94.72%），如需严格单调可考虑调整切点或合并分箱。IV=0.0936，该变量区分度偏弱但可用。

WOE 在第 3、4 箱之间穿越 0 线：前 3 箱 WOE>0（客户质量优于整体），后 2 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 88.41% 回落至 84.72%，如需严格单调可考虑合并这两箱

第 5 箱（(3545.66, inf]）为极高风险区间：坏率高达 94.72%，策略上应重点管控

odduecontireducenumm12 — 2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]5102440466291.38%-0.2018
2(0.5, inf]1756270148684.62%0.4532
2026-11-03T12:04:09.397442 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调下降（91.38% → 84.62%），WOE 单调上升（-0.2018 → 0.4532），符合风控分箱预期。IV=0.0908，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 1 箱 WOE<0（风险高于平均）

第 1 箱（(-inf, 0.5]）为极高风险区间：坏率高达 91.38%，策略上应重点管控

max_setl_rem_prcp_paym_m3_last_month_loan_os_prcp_pc — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.88]4795544425188.65%0.1027
2(1.88, 11.68]5442052496.32%-1.1071
3(11.68, 20.03]6655860791.28%-0.1895
4(20.03, 33.46]3582433493.30%-0.4745
5(33.46, inf]4966443287.10%0.2491
2026-11-03T12:04:09.513452 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（88.65% → 96.32% → 87.10%），如需严格单调可考虑调整切点或合并分箱。IV=0.0888，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 1→2 箱有局部反弹：Bad Rate 由 88.65% 回升至 96.32%，如需严格单调可考虑合并这两箱

第 2 箱（(1.88, 11.68]）为极高风险区间：坏率高达 96.32%，策略上应重点管控

apply_amt_avg_m12 — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4525]3004381262387.32%0.2293
2(4525, 10810]1167124104389.37%0.0290
3(10810, 32366.67]1481110137192.57%-0.3642
4(32366.67, 48025]5025344989.44%0.0219
5(48025, inf]7044266294.03%-0.5990
2026-11-03T12:04:09.639071 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（87.32% → 94.03% → 94.03%），如需严格单调可考虑调整切点或合并分箱。IV=0.0793，该变量区分度偏弱但可用。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 3→4 箱有局部反弹：Bad Rate 由 92.57% 回落至 89.44%，如需严格单调可考虑合并这两箱

第 5 箱（(48025, inf]）为极高风险区间：坏率高达 94.03%，策略上应重点管控

orig_prcp_avg_m6 — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 636.67]4606542406488.23%0.1440
2(636.67, 2158.33]4704642490.21%-0.0625
3(2158.33, 5016.67]5274748091.08%-0.1650
4(5016.67, 15012.5]6314658592.71%-0.3844
5(15012.5, inf]6242959595.35%-0.8626
2026-11-03T12:04:09.765979 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调上升（88.23% → 95.35%），WOE 单调下降（0.1440 → -0.8626），符合风控分箱预期。IV=0.0769，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 5 箱（(15012.5, inf]）为极高风险区间：坏率高达 95.35%，策略上应重点管控

odgt1_m12_subcurmonth — 2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 50003]60311848580.43%0.7452
2(50003, inf]6255592566390.54%-0.0996
2026-11-03T12:04:09.876736 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调上升（80.43% → 90.54%），WOE 单调下降（0.7452 → -0.0996），符合风控分箱预期。IV=0.0738，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 1 箱 WOE<0（风险高于平均）

第 2 箱（(50003, inf]）为极高风险区间：坏率高达 90.54%，策略上应重点管控

account_age_loan_day — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.52]1645118152792.83%-0.4018
2(0.52, 0.96]2199240195989.09%0.0591
3(0.96, 1.19]64610753983.44%0.5417
4(1.19, 4.62]1453137131690.57%-0.1038
5(4.62, inf]91510880788.20%0.1474
2026-11-03T12:04:09.985273 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（92.83% → 92.83% → 88.20%），如需严格单调可考虑调整切点或合并分箱。IV=0.0736，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 3→4 箱有局部反弹：Bad Rate 由 83.44% 回升至 90.57%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 0.52]）为极高风险区间：坏率高达 92.83%，策略上应重点管控

repay_amount_m6_loan_os_prcp_pc — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 42.01]2194173202192.11%-0.2994
2(42.01, 70.26]2298228207090.08%-0.0473
3(70.26, 82.59]5236945486.81%0.2746
4(82.59, 99.67]3496828180.52%0.7398
5(99.67, inf]1494172132288.49%0.1192
2026-11-03T12:04:10.105199 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（92.11% → 92.11% → 88.49%），如需严格单调可考虑调整切点或合并分箱。IV=0.0729，该变量区分度偏弱但可用。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 4→5 箱有局部反弹：Bad Rate 由 80.52% 回升至 88.49%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 42.01]）为极高风险区间：坏率高达 92.11%，策略上应重点管控

sum_orig_prcp — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 10450]2505314219187.47%0.2159
2(10450, 12554]3552133494.08%-0.6080
3(12554, 19150]8259672988.36%0.1313
4(19150, 110100]2809263254690.64%-0.1115
5(110100, inf]3641634895.60%-0.9210
2026-11-03T12:04:10.232898 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（87.47% → 95.60% → 95.60%），如需严格单调可考虑调整切点或合并分箱。IV=0.0720，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 94.08% 回落至 88.36%，如需严格单调可考虑合并这两箱

第 5 箱（(110100, inf]）为极高风险区间：坏率高达 95.60%，策略上应重点管控

loannum — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 9.5]4357036583.91%0.5110
2(9.5, 27.5]2020242177888.02%0.1681
3(27.5, 39.5]10388795191.62%-0.2292
4(39.5, 61.5]1212137107588.70%0.1023
5(61.5, inf]1750130162092.57%-0.3602
2026-11-03T12:04:10.438454 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（83.91% → 92.57% → 92.57%），如需严格单调可考虑调整切点或合并分箱。IV=0.0712，该变量区分度偏弱但可用。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 3→4 箱有局部反弹：Bad Rate 由 91.62% 回落至 88.70%，如需严格单调可考虑合并这两箱

第 5 箱（(61.5, inf]）为极高风险区间：坏率高达 92.57%，策略上应重点管控

sum_card_mob12_neverdur — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]2098252184687.99%0.1711
2(0.5, 1.5]8347775790.77%-0.1231
3(1.5, 6.5]1857214164388.48%0.1241
4(6.5, 8.5]3351332296.12%-1.0472
5(8.5, inf]1331110122191.74%-0.2445
2026-11-03T12:04:10.564366 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（87.99% → 96.12% → 91.74%），如需严格单调可考虑调整切点或合并分箱。IV=0.0657，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 90.77% 回落至 88.48%，如需严格单调可考虑合并这两箱

第 4 箱（(6.5, 8.5]）为极高风险区间：坏率高达 96.12%，策略上应重点管控

od_amt_m12_loan_os_prcp_m12_pc — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.91]1241129111289.61%0.0045
2(0.91, 1.09]3855133486.75%0.2793
3(1.09, 2.17]3540299324191.55%-0.2246
4(2.17, 3.17]7828969388.62%0.1062
5(3.17, inf]91014276884.40%0.4707
2026-11-03T12:04:10.696065 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（89.61% → 91.55% → 84.40%），如需严格单调可考虑调整切点或合并分箱。IV=0.0654，该变量区分度偏弱但可用。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 86.75% 回升至 91.55%，如需严格单调可考虑合并这两箱

第 3 箱（(1.09, 2.17]）为极高风险区间：坏率高达 91.55%，策略上应重点管控

setl_int_mon — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 3.64]5009457455290.88%-0.1400
2(3.64, 17.71]3445928582.85%0.5837
3(17.71, 42.33]3704632487.57%0.2065
4(42.33, 90.6]3776731082.23%0.6267
5(90.6, inf]7588167789.31%0.0354
2026-11-03T12:04:10.824221 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（90.88% → 90.88% → 89.31%），如需严格单调可考虑调整切点或合并分箱。IV=0.0652，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 82.85% 回升至 87.57%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 3.64]）为极高风险区间：坏率高达 90.88%，策略上应重点管控

setl_rem_prcp_paym_m3_loan_os_prcp_pc — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, -4999]3472232593.66%-0.5342
2(-4999, 3.21]4506525398188.35%0.1327
3(3.21, 19.05]8575080794.17%-0.6227
4(19.05, 25.49]3654232388.49%0.1186
5(25.49, inf]7837171290.93%-0.1468
2026-11-03T12:04:10.951306 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（93.66% → 94.17% → 90.93%），如需严格单调可考虑调整切点或合并分箱。IV=0.0649，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 88.35% 回升至 94.17%，如需严格单调可考虑合并这两箱

第 3 箱（(3.21, 19.05]）为极高风险区间：坏率高达 94.17%，策略上应重点管控

sum_month3_new_loan — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]1416182123487.15%0.2484
2(0.5, 1.5]1328142118689.31%0.0399
3(1.5, 2.5]108813695287.50%0.2165
4(2.5, 3.5]7565270493.12%-0.4431
5(3.5, inf]1867154171391.75%-0.2466
2026-11-03T12:04:11.080321 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（87.15% → 93.12% → 91.75%），如需严格单调可考虑调整切点或合并分箱。IV=0.0591，该变量区分度偏弱但可用。

WOE 在第 3、4 箱之间穿越 0 线：前 3 箱 WOE>0（客户质量优于整体），后 2 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 89.31% 回落至 87.50%，如需严格单调可考虑合并这两箱

第 4 箱（(2.5, 3.5]）为极高风险区间：坏率高达 93.12%，策略上应重点管控

sum_apply_amt — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 2050]5137543885.38%0.3939
2(2050, 9625]1776187158989.47%0.0189
3(9625, 11585]3906332783.85%0.5118
4(11585, 130400]3817366345190.41%-0.0852
5(130400, inf]3621934394.75%-0.7347
2026-11-03T12:04:11.205411 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（85.38% → 94.75% → 94.75%），如需严格单调可考虑调整切点或合并分箱。IV=0.0571，该变量区分度偏弱但可用。

WOE 在第 3、4 箱之间穿越 0 线：前 3 箱 WOE>0（客户质量优于整体），后 2 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 89.47% 回落至 83.85%，如需严格单调可考虑合并这两箱

第 5 箱（(130400, inf]）为极高风险区间：坏率高达 94.75%，策略上应重点管控

loan_reject_rate_3m — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, -0.15]8256576092.12%-0.2966
2(-0.15, 0.02]1520172134888.68%0.1034
3(0.02, 0.28]4632843593.95%-0.5808
4(0.28, 0.58]1341115122691.42%-0.2043
5(0.58, inf]2286284200287.58%0.2094
2026-11-03T12:04:11.334377 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（92.12% → 93.95% → 87.58%），如需严格单调可考虑调整切点或合并分箱。IV=0.0569，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 88.68% 回升至 93.95%，如需严格单调可考虑合并这两箱

第 3 箱（(0.02, 0.28]）为极高风险区间：坏率高达 93.95%，策略上应重点管控

setl_prcp_mon_ratio — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0]4990458453290.82%-0.1334
2(0, 0.01]5096944086.44%0.3059
3(0.01, 0.02]3733334091.15%-0.1738
4(0.02, 0.04]4727639683.90%0.5079
5(0.04, inf]5147444085.60%0.3759
2026-11-03T12:04:11.468203 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（90.82% → 91.15% → 85.60%），如需严格单调可考虑调整切点或合并分箱。IV=0.0556，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 86.44% 回升至 91.15%，如需严格单调可考虑合并这两箱

第 3 箱（(0.01, 0.02]）为极高风险区间：坏率高达 91.15%，策略上应重点管控

useratemore90_m6_num — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]4406515389188.31%0.1364
2(0.5, 1.5]5705351790.70%-0.1191
3(1.5, 2.5]8605380793.84%-0.5644
4(2.5, 3.5]4413141092.97%-0.4236
5(3.5, inf]5815852390.02%-0.0405
2026-11-03T12:04:11.599701 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（88.31% → 93.84% → 90.02%），如需严格单调可考虑调整切点或合并分箱。IV=0.0556，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 3→4 箱有局部反弹：Bad Rate 由 93.84% 回落至 92.97%，如需严格单调可考虑合并这两箱

第 3 箱（(1.5, 2.5]）为极高风险区间：坏率高达 93.84%，策略上应重点管控

account_age — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 6.5]1897151174692.04%-0.2892
2(6.5, 11.5]2514270224489.26%0.0410
3(11.5, 14.5]71711360484.24%0.4824
4(14.5, 33.5]1350146120489.19%0.0488
5(33.5, inf]3803035092.11%-0.2981
2026-11-03T12:04:11.802410 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（92.04% → 92.11% → 92.11%），如需严格单调可考虑调整切点或合并分箱。IV=0.0555，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 1→2 箱有局部反弹：Bad Rate 由 92.04% 回落至 89.26%，如需严格单调可考虑合并这两箱

第 5 箱（(33.5, inf]）为极高风险区间：坏率高达 92.11%，策略上应重点管控

apply_num_m6 — 4 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]4480531394988.15%0.1522
2(0.5, 1.5]1439112132792.22%-0.3136
3(1.5, 2.5]4643742792.03%-0.2872
4(2.5, inf]4753044593.68%-0.5383
2026-11-03T12:04:11.931366 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（88.15% → 93.68% → 93.68%），如需严格单调可考虑调整切点或合并分箱。IV=0.0555，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 92.22% 回落至 92.03%，如需严格单调可考虑合并这两箱

第 4 箱（(2.5, inf]）为极高风险区间：坏率高达 93.68%，策略上应重点管控

repay_amount_m12_all_shd_amt_pc — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 85.33]1183111107290.62%-0.1091
2(85.33, 86.76]3742235294.12%-0.6140
3(86.76, 89.01]4798139883.09%0.5666
4(89.01, 99.76]4478471400789.48%0.0177
5(99.76, inf]3442531992.73%-0.3877
2026-11-03T12:04:12.054316 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（90.62% → 94.12% → 92.73%），如需严格单调可考虑调整切点或合并分箱。IV=0.0527，该变量区分度偏弱但可用。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 94.12% 回落至 83.09%，如需严格单调可考虑合并这两箱

第 2 箱（(85.33, 86.76]）为极高风险区间：坏率高达 94.12%，策略上应重点管控

t_ps_od_days_max — 4 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]5036466457090.75%-0.1245
2(0.5, 3.5]77110966285.86%0.3547
3(3.5, 11.5]4327335983.10%0.5658
4(11.5, inf]6196255789.98%-0.0368
2026-11-03T12:04:12.184379 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（90.75% → 90.75% → 89.98%），如需严格单调可考虑调整切点或合并分箱。IV=0.0523，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 3→4 箱有局部反弹：Bad Rate 由 83.10% 回升至 89.98%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 0.5]）为极高风险区间：坏率高达 90.75%，策略上应重点管控

nosvaing_bank_cnt_1m — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]4288492379688.53%0.1188
2(0.5, 1.5]1421135128690.50%-0.0920
3(1.5, inf]8314878394.22%-0.6299
2026-11-03T12:04:12.305675 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调上升（88.53% → 94.22%），WOE 单调下降（0.1188 → -0.6299），符合风控分箱预期。IV=0.0508，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 2 箱 WOE<0（风险高于平均）

第 3 箱（(1.5, inf]）为极高风险区间：坏率高达 94.22%，策略上应重点管控

last6average — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 128.5]1493158133589.42%0.0278
2(128.5, 977.5]1289166112387.12%0.2502
3(977.5, 2255.5]1162113104990.28%-0.0663
4(2255.5, 3242.5]6498756286.59%0.2963
5(3242.5, inf]1956152180492.23%-0.3120
2026-11-03T12:04:12.426946 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（89.42% → 92.23% → 92.23%），如需严格单调可考虑调整切点或合并分箱。IV=0.0500，该变量区分度偏弱但可用。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 1→2 箱有局部反弹：Bad Rate 由 89.42% 回落至 87.12%，如需严格单调可考虑合并这两箱

第 5 箱（(3242.5, inf]）为极高风险区间：坏率高达 92.23%，策略上应重点管控

useratemore90monthsubcurmonth — 5 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]8937681791.49%-0.2163
2(1.5, 3.5]7634771693.84%-0.5649
3(3.5, 5.5]7967272490.95%-0.1495
4(5.5, 8.5]97612085687.70%0.1938
5(8.5, inf]3430395303588.48%0.1195
2026-11-03T12:04:12.556620 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（91.49% → 93.84% → 88.48%），如需严格单调可考虑调整切点或合并分箱。IV=0.0497，该变量区分度偏弱但可用。

WOE 在第 3、4 箱之间穿越 0 线：前 3 箱 WOE>0（客户质量优于整体），后 2 箱 WOE<0（风险高于平均）

第 1→2 箱有局部反弹：Bad Rate 由 91.49% 回升至 93.84%，如需严格单调可考虑合并这两箱

第 2 箱（(1.5, 3.5]）为极高风险区间：坏率高达 93.84%，策略上应重点管控

注：统计入模变量经合并调整后的最终分箱明细（开发样本口径）；本次无变量发生单调性合并，细/粗分箱一致。

05 变量筛选

项目内容
筛选优先级黑名单 > 白名单 > 自动规则
IV 下限0.02
缺失率上限0.8
集中度上限0.95
相关性上限(皮尔逊)0.7
PSI 上限0.25
重要性 Top-K40
黑名单无
白名单无

候选变量 581，成功分箱 568，入模 40

变量白名单入模IV缺失率集中度IV门槛相关性PSI剔除原因
loan_no—N-0.0002PASSPASSFAIL——IV<0.02
last_con_amt—N0.0656PASSPASSPASS—0.3826PSI>0.25
sum_apply_amt—Y0.0571PASSPASSPASSPASS0.2029
max_apply_amt—N0.0308PASSPASSPASSFAIL0.1254与 sum_apply_amt 相关>0.7
min_apply_amt—N0.0175PASSPASSFAIL——IV<0.02
avg_apply_amt—N0.0306PASSPASSPASSFAIL0.1460与 apply_amt_avg_m12 相关>0.7
valid_sum_apply_amt—N0.0481PASSPASSPASSFAIL0.1739与 sum_apply_amt 相关>0.7
last_apply_amt—N0.0326PASSPASSPASSFAIL0.1318与 apply_amt_avg_m12 相关>0.7
f_apply_amt—N0.0340PASSPASSPASSFAIL0.0982与 sum_apply_amt 相关>0.7
apply_num—N0.0163PASSPASSFAIL——IV<0.02
max_apprv_amt—N0.0319PASSPASSPASS—1.4350PSI>0.25
min_apprv_amt—N0.0542PASSPASSPASS—1.3329PSI>0.25
avg_apprv_amt—N0.0287PASSPASSPASS—1.3944PSI>0.25
last_apprv_amt—N0.0352PASSPASSPASS—1.4248PSI>0.25
f_apprv_amt—N0.0397PASSPASSPASS—1.3992PSI>0.25
apprv_num—N0.0116PASSPASSFAIL——IV<0.02
sum_orig_prcp—Y0.0720PASSPASSPASSPASS0.2245
max_orig_prcp—N0.0800PASSPASSPASS—0.3803PSI>0.25
min_orig_prcp—N0.0371PASSPASSPASS—0.4179PSI>0.25
avg_orig_prcp—N0.0647PASSPASSPASS—0.4117PSI>0.25
last_orig_prcp—N0.0651PASSPASSPASS—0.3879PSI>0.25
f_orig_prcp—N0.0606PASSPASSPASS—0.4090PSI>0.25
loan_num—N0.0121PASSPASSFAIL——IV<0.02
loan_os_prcp—N0.1534PASSPASSPASS—0.3137PSI>0.25
pp_num—N0.0226PASSPASSPASSFAIL0.1118与 normal_repay_num 相关>0.7
apply_amt_day—N0.0000PASSFAIL———集中度>0.95
apprv_amt_day—N0.0000PASSFAIL———集中度>0.95
f_loan_ps_od_ind—N0.0055PASSPASSFAIL——IV<0.02
f_loan_orig_prcp—N0.0606PASSPASSPASS—0.4090PSI>0.25
rep_loan_num—N0.0121PASSPASSFAIL——IV<0.02
rep_loan_orig_prcp—N0.0324PASSPASSPASSFAIL0.1589与 sum_orig_prcp 相关>0.7
rep_loan_freq_avg—N0.0405PASSPASSPASSFAIL0.1792与 end_loan_to_cur_freq 相关>0.7
end_loan_to_rep_loan_freq—N0.0242PASSPASSPASSFAIL0.2141与 end_loan_to_cur_freq 相关>0.7
end_loan_to_cur_freq—Y0.1019PASSPASSPASSPASS0.2156
f_rep_loan_to_f_apply_dt_freq—N0.0262PASSPASSPASSPASS0.0958重要性排名超出Top-40
ps_prcp_amt_sum—N0.0352PASSPASSPASSFAIL0.0338与 sum_orig_prcp 相关>0.7
ps_int_amt_sum—N0.0241PASSPASS———唯一率过高(ID列)
ps_fee_amt_sum—N0.0000PASSFAIL———集中度>0.95
ps_prcp_amt_avg—N0.0586PASSPASSPASS—0.2583PSI>0.25
ps_int_amt_avg—N0.0765PASSPASS———唯一率过高(ID列)
ps_fee_amt_avg—N0.0000PASSFAIL———集中度>0.95
setl_prcp_mon—N0.0490PASSPASSPASSFAIL0.0465与 setl_prcp_mon_ratio 相关>0.7
setl_int_mon—Y0.0652PASSPASSPASSPASS0.0322
setl_fee_amt_mon—N0.0000PASSFAIL———集中度>0.95
setl_prcp_sum—N0.0308PASSPASSPASSFAIL0.0701与 sum_orig_prcp 相关>0.7
setl_int_sum—N0.0221PASSPASSPASSFAIL0.0714与 sum_orig_prcp 相关>0.7
setl_fee_amt_sum—N0.0000PASSFAIL———集中度>0.95
setl_prcp_avg—N0.0429PASSPASSPASSPASS0.1761重要性排名超出Top-40
setl_int_avg—N0.0497PASSPASSPASSPASS0.2302重要性排名超出Top-40
setl_fee_amt_avg—N0.0000PASSFAIL———集中度>0.95
setl_prcp_mon_ratio—Y0.0556PASSPASSPASSPASS0.0399
acc_ps_perd_no—Y0.2055PASSPASSPASSPASS0.1017
m_c_over_due_cnt—Y0.2452PASSPASSPASSPASS0.0162
cur_max_od_days—N0.0252PASSPASSPASSPASS0.0635重要性排名超出Top-40
max_od_days—N0.1186PASSPASSPASSFAIL0.0291与 max_od_days_m12 相关>0.7
m_s_ps_instm_amt—N0.0366PASSPASSPASSFAIL0.1871与 cur_od_loan_amt 相关>0.7
cur_od_loan_num—N0.0077PASSPASSFAIL——IV<0.02
cur_od_loan_amt—Y0.0936PASSPASSPASSPASS0.1478
f_ps_od_days_max—N0.0454PASSPASSPASSPASS0.0471重要性排名超出Top-40
s_ps_od_days_max—N0.0439PASSPASSPASSFAIL0.0105与 f_ps_od_days_max 相关>0.7
t_ps_od_days_max—Y0.0523PASSPASSPASSPASS0.0110
m1_ovd_days_num—N0.0755PASSPASSPASSFAIL0.0029与 ovd_days_d30_num_m6 相关>0.7
normal_repay_num—N0.0363PASSPASSPASSPASS0.1011重要性排名超出Top-40
numberresolve—N0.0002PASSPASSFAIL——IV<0.02
pboc_quiet_ind—N0.0000PASSFAIL———集中度>0.95
pboc_b_white_ind—N0.0000PASSFAIL———集中度>0.95
pboc_s_white_ind—N0.0000PASSFAIL———集中度>0.95
credit_ability—N-0.0002PASSFAIL———集中度>0.95
pboc_max—N0.0185PASSPASSFAIL——IV<0.02
pboc_min—N0.0002PASSPASSFAIL——IV<0.02
f_pboc—N0.0150PASSPASSFAIL——IV<0.02
last_pboc—N0.0002PASSPASSFAIL——IV<0.02
mobile_ovd_d30_idno_num—N0.0000PASSFAIL———集中度>0.95
ovd_days_max_m1—N0.0491PASSPASSPASSFAIL0.0531与 max_od_days_m12 相关>0.7
ovd_days_max_m3—N0.1060PASSPASSPASSFAIL0.0256与 max_od_days_m12 相关>0.7
ovd_days_max_m6—N0.1190PASSPASSPASSFAIL0.0280与 max_od_days_m12 相关>0.7
ovd_days_d3_num_m1—N0.0121PASSPASSFAIL——IV<0.02
ovd_days_d15_num_m3—N0.0480PASSPASSPASSPASS0.0731重要性排名超出Top-40
ovd_days_d30_num_m6—Y0.1315PASSPASSPASSPASS0.0296
last_ovd_to_last_applydt_freq—N0.0386PASSPASSPASS—0.7877PSI>0.25
last_ovd_to_last_setl_dt_freq—N0.0662PASSPASSPASS—7.4210PSI>0.25
login_num_d7—N0.0000PASSFAIL———集中度>0.95
login_num_d30—N0.0000PASSFAIL———集中度>0.95
login_num_d90—N0.0000PASSFAIL———集中度>0.95
balance_avg_m3—N0.1204PASSPASSPASS—0.3097PSI>0.25
balance_avg_m6—N0.1135PASSPASSPASS—0.2797PSI>0.25
balance_avg_m12—N0.0799PASSPASSPASS—0.2558PSI>0.25
rpym_fail_num_m3—N0.0000PASSFAIL———集中度>0.95
rpym_fail_num_m6—N0.0000PASSFAIL———集中度>0.95
rpym_fail_num_m12—N0.0000PASSFAIL———集中度>0.95
orig_prcp_avg_m1—N0.0000PASSFAIL———集中度>0.95
apply_amt_avg_m1—N0.0000PASSFAIL———集中度>0.95
orig_prcp_avg_m3—N0.0429PASSPASSPASSPASS0.0511重要性排名超出Top-40
apply_amt_avg_m3—N0.0420PASSPASSPASSFAIL0.0519与 orig_prcp_avg_m3 相关>0.7
orig_prcp_avg_m6—Y0.0769PASSPASSPASSPASS0.0921
apply_amt_avg_m6—N0.0759PASSPASSPASSFAIL0.0570与 orig_prcp_avg_m6 相关>0.7
orig_prcp_avg_m12—N0.1218PASSPASSPASS—0.3549PSI>0.25
apply_amt_avg_m12—Y0.0793PASSPASSPASSPASS0.1669
suc_apply_trench_num—N0.0039PASSPASSFAIL——IV<0.02
suc_apply_top_pc_code_num—N0.0000PASSFAIL———集中度>0.95
suc_apply_sec_pc_code_num—N0.0000PASSFAIL———集中度>0.95
apply_num_d30—N0.0000PASSFAIL———集中度>0.95
apply_top_pc_code_num_d30—N0.0000PASSFAIL———集中度>0.95
apply_sec_pc_code_num_d30—N0.0000PASSFAIL———集中度>0.95
suc_apply_num_m1—N0.0000PASSFAIL———集中度>0.95
suc_apprv_num_m1—N0.0000PASSFAIL———集中度>0.95
apply_num_m1—N0.0000PASSFAIL———集中度>0.95
apprv_num_m1—N0.0000PASSFAIL———集中度>0.95
suc_apply_num_m3—N0.0260PASSPASSPASSPASS0.0828重要性排名超出Top-40
suc_apprv_num_m3—N0.0000PASSFAIL———集中度>0.95
apply_num_m3—N0.0258PASSPASSPASSFAIL0.0634与 suc_apply_num_m3 相关>0.7
apprv_num_m3—N0.0000PASSFAIL———集中度>0.95
suc_apply_num_m6—N0.0485PASSPASSPASSFAIL0.0380与 apply_num_m6 相关>0.7
suc_apprv_num_m6—N0.0049PASSPASSFAIL——IV<0.02
apply_num_m6—Y0.0555PASSPASSPASSPASS0.0347
apprv_num_m6—N0.0028PASSPASSFAIL——IV<0.02
suc_apply_num_m12—N0.0358PASSPASSPASSFAIL0.1874与 apply_num_m12 相关>0.7
suc_apprv_num_m12—N0.0162PASSPASSFAIL——IV<0.02
apply_num_m12—N0.0370PASSPASSPASSPASS0.2194重要性排名超出Top-40
apprv_num_m12—N0.0126PASSPASSFAIL——IV<0.02
last_apply_od_num_m12—N0.0062PASSPASSFAIL——IV<0.02
last_apply_od_num_m1—N0.0070PASSPASSFAIL——IV<0.02
last_apply_od_num_m3—N0.0067PASSPASSFAIL——IV<0.02
last_apply_od_num_m6—N0.0062PASSPASSFAIL——IV<0.02
apply_trench_num—N0.0021PASSPASSFAIL——IV<0.02
night_apply_num—N—FAILPASS———分箱失败(单侧样本)
last_apply_freq—N0.0211PASSPASSPASSPASS0.0246重要性排名超出Top-40
red_envelope_num—N0.0000PASSFAIL———集中度>0.95
edu_loan_num—N0.0000PASSFAIL———集中度>0.95
tra_loan_num—N0.0000PASSFAIL———集中度>0.95
oth_loan_num—N0.0000PASSFAIL———集中度>0.95
gex_loan_num—N0.0135PASSPASSFAIL——IV<0.02
cash_loan_num—N0.0136PASSPASSFAIL——IV<0.02
med_loan_num—N0.0000PASSFAIL———集中度>0.95
trans_loan_num—N0.0000PASSFAIL———集中度>0.95
dec_loan_num—N0.0000PASSFAIL———集中度>0.95
ren_loan_num—N0.0000PASSFAIL———集中度>0.95
court_black_list—N—FAILPASS———分箱失败(单侧样本)
red_list—N—FAILPASS———分箱失败(单侧样本)
pengyuan_black_list—N—FAILPASS———分箱失败(单侧样本)
samoye_black_list—N—FAILPASS———分箱失败(单侧样本)
breach_of_person—N—FAILPASS———分箱失败(单侧样本)
tongdun_black_list—N—FAILPASS———分箱失败(单侧样本)
bh_cur_bad_num—N—FAILPASS———分箱失败(单侧样本)
bh_cur_od_num—N0.0163PASSPASSFAIL——IV<0.02
hj_cur_bad_num—N0.0055PASSPASSFAIL——IV<0.02
hj_cur_od_num—N0.0055PASSPASSFAIL——IV<0.02
sub3_mobile—N0.0275PASSPASSPASSPASS0.0070重要性排名超出Top-40
mobile_idno_num—N—FAILPASS———分箱失败(单侧样本)
work_phone_idno_num—N—FAILPASS———分箱失败(单侧样本)
mobile_idno_num_m1—N0.0000PASSFAIL———集中度>0.95
mobile_idno_num_m3—N0.0256PASSPASSPASSPASS0.0636重要性排名超出Top-40
mobile_idno_num_m6—N0.0530PASSPASSPASSFAIL0.0309与 repay_amount_m6_loan_os_prcp_pc 相关>0.7
mobile_idno_num_m12—N0.0228PASSPASSPASSPASS0.1885重要性排名超出Top-40
setl_prcp_ratio—N0.1266PASSPASSPASSFAIL0.1736与 loan_os_prcp_orig_prcp_pc 相关>0.7
last_loan_setl_tnr—N0.0978PASSPASSPASSFAIL0.1032与 loan_os_prcp_orig_prcp_pc 相关>0.7
last_od_num_m3—N0.1321PASSPASSPASSFAIL0.1340与 acc_ps_perd_no 相关>0.7
last_od_num_m6—N0.1688PASSPASSPASSFAIL0.1228与 acc_ps_perd_no 相关>0.7
last_od_num_m12—N0.1785PASSPASSPASSFAIL0.1180与 acc_ps_perd_no 相关>0.7
last_od_to_cur_freq—N0.0000PASSFAIL———集中度>0.95
last_clear_num_m1—N0.0000PASSFAIL———集中度>0.95
last_clear_num_m3—N0.0108PASSPASSFAIL——IV<0.02
last_clear_num_m6—N0.0034PASSPASSFAIL——IV<0.02
last_clear_num_m12—N0.0071PASSPASSFAIL——IV<0.02
ovd_days_max_m12—N0.1204PASSPASSPASSFAIL0.0283与 max_od_days_m12 相关>0.7
ovd_days_d3_count_m3—N0.1350PASSPASSPASSFAIL0.1124与 acc_ps_perd_no 相关>0.7
ovd_days_d3_count_m6—N0.1752PASSPASSPASSFAIL0.1086与 acc_ps_perd_no 相关>0.7
ovd_days_d3_count_m12—N0.1807PASSPASSPASSFAIL0.1067与 acc_ps_perd_no 相关>0.7
ovd_days_d30_count_m3—N0.0659PASSPASSPASSFAIL0.0022与 ovd_days_d30_num_m6 相关>0.7
ovd_days_d30_count_m6—N0.0754PASSPASSPASSFAIL0.0031与 ovd_days_d30_num_m6 相关>0.7
ovd_days_d30_count_m12—N0.0772PASSPASSPASSFAIL0.0027与 ovd_days_d30_num_m6 相关>0.7
ovd_days_d3_month_m3—N0.3097PASSPASSPASSFAIL0.0273与 odgt0_m12_num 相关>0.7
ovd_days_d3_month_m6—N0.3198PASSPASSPASSFAIL0.0395与 odgt0_m12_num 相关>0.7
ovd_days_d3_month_m12—N0.3198PASSPASSPASSFAIL0.0395与 odgt0_m12_num 相关>0.7
ovd_days_d30_month_m3—N0.0755PASSPASSPASSFAIL0.0029与 max_od_days_m12 相关>0.7
ovd_days_d30_month_m6—N0.0755PASSPASSPASSFAIL0.0029与 max_od_days_m12 相关>0.7
ovd_days_d30_month_m12—N0.0755PASSPASSPASSFAIL0.0029与 max_od_days_m12 相关>0.7
loan_os_prcp_claim—N0.1534PASSPASSPASS—0.3137PSI>0.25
balance_avg_m3_claim—N0.1204PASSPASSPASS—0.3097PSI>0.25
balance_avg_m6_claim—N0.1135PASSPASSPASS—0.2797PSI>0.25
balance_avg_m12_claim—N0.0799PASSPASSPASS—0.2558PSI>0.25
account_age—Y0.0555PASSPASSPASSPASS0.0525
account_age_loan_day—Y0.0736PASSPASSPASSPASS0.1271
max_od_due—N0.0718PASSPASSPASSFAIL0.0032与 max_od_days_m12 相关>0.7
odgt0_m3_mob_pc—N0.2926PASSPASSPASSFAIL0.0162与 odgt0_m12_num 相关>0.7
odgt0_m6_mob_pc—N0.3159PASSPASSPASSFAIL0.0716与 odgt0_m12_num 相关>0.7
odgt0_m12_mob_pc—Y0.2573PASSPASSPASSPASS0.0678
odgt0_m3_num—N0.3063PASSPASSPASSFAIL0.0479与 odgt0_m12_num 相关>0.7
odgt0_m6_num—N0.3408PASSPASSPASSFAIL0.0604与 odgt0_m12_num 相关>0.7
odgt0_m12_num—Y0.3489PASSPASSPASSPASS0.0597
odgt1_m3_mob_pc—N0.0594PASSPASSPASSFAIL0.0025与 odgt1_m12_subcurmonth 相关>0.7
odgt1_m6_mob_pc—N0.0720PASSPASSPASSFAIL0.0033与 odgt1_m12_subcurmonth 相关>0.7
odgt1_m12_mob_pc—N0.0738PASSPASSPASSFAIL0.0028与 max_od_days_m12 相关>0.7
odgt1_m3_num—N0.0594PASSPASSPASSFAIL0.0025与 odgt1_m12_subcurmonth 相关>0.7
odgt1_m6_num—N0.0720PASSPASSPASSFAIL0.0033与 max_od_days_m12 相关>0.7
odgt1_m12_num—N0.0738PASSPASSPASSFAIL0.0028与 max_od_days_m12 相关>0.7
odgt2_m3_mob_pc—N0.0000PASSFAIL———集中度>0.95
odgt2_m6_mob_pc—N0.0000PASSFAIL———集中度>0.95
odgt2_m12_mob_pc—N0.0000PASSFAIL———集中度>0.95
odgt2_m3_num—N0.0000PASSFAIL———集中度>0.95
odgt2_m6_num—N0.0000PASSFAIL———集中度>0.95
odgt2_m12_num—N0.0000PASSFAIL———集中度>0.95
odgt3_m3_mob_pc—N0.0000PASSFAIL———集中度>0.95
odgt3_m6_mob_pc—N0.0000PASSFAIL———集中度>0.95
odgt3_m12_mob_pc—N0.0000PASSFAIL———集中度>0.95
odgt3_m3_num—N0.0000PASSFAIL———集中度>0.95
odgt3_m6_num—N0.0000PASSFAIL———集中度>0.95
odgt3_m12_num—N0.0000PASSFAIL———集中度>0.95
odgt0_m12_subcurmonth—N0.0000PASSFAIL———集中度>0.95
odgt1_m12_subcurmonth—Y0.0738PASSPASSPASSPASS0.0028
odgt2_m12_subcurmonth—N0.0000PASSFAIL———集中度>0.95
odgt3_m12_subcurmonth—N0.0000PASSFAIL———集中度>0.95
max_od_due_m3—N0.0597PASSPASSPASSFAIL0.0024与 max_od_days_m12 相关>0.7
max_od_due_m6—N0.0720PASSPASSPASSFAIL0.0033与 max_od_days_m12 相关>0.7
max_od_due_m12—N0.0738PASSPASSPASSFAIL0.0028与 max_od_days_m12 相关>0.7
max_od_days_m3—N0.1068PASSPASSPASSFAIL0.0251与 max_od_days_m12 相关>0.7
max_od_days_m6—N0.1199PASSPASSPASSFAIL0.0275与 max_od_days_m12 相关>0.7
max_od_days_m12—Y0.1213PASSPASSPASSPASS0.0278
odgt0_conti_max_mons_m12—N0.3367PASSPASSPASSFAIL0.0499与 odgt0_m12_num 相关>0.7
odgt1_conti_max_mons_m12—N0.0738PASSPASSPASSFAIL0.0028与 max_od_days_m12 相关>0.7
odgt2_conti_max_mons_m12—N0.0000PASSFAIL———集中度>0.95
odgt3_conti_max_mons_m12—N0.0000PASSFAIL———集中度>0.95
tothismonthodcontiaddnumm12—Y0.1837PASSPASSPASSPASS0.0301
tothismonthodcontireducenumm12—N0.0000PASSFAIL———集中度>0.95
odduecontiaddnumm12—N0.0348PASSPASSPASSFAIL0.0001与 max_od_days_m12 相关>0.7
odduecontireducenumm12—Y0.0908PASSPASSPASSPASS0.0040
odmaxreduceduem12—N0.0908PASSPASSPASSFAIL0.0040与 odduecontireducenumm12 相关>0.7
loan_os_prcp_orig_prcp_pc—Y0.1437PASSPASSPASSPASS0.1869
od_amt_loan_os_prcp_pc—N0.1174PASSPASSPASS—0.5406PSI>0.25
od_amt_m3_loan_os_prcp_m3_pc—Y0.1301PASSPASSPASSPASS0.0184
od_amt_m6_loan_os_prcp_m6_pc—N0.1159PASSPASSPASS—0.3258PSI>0.25
od_amt_m12_loan_os_prcp_m12_pc—Y0.0654PASSPASSPASSPASS0.2101
od_amt_orig_prcp_pc—N0.0610PASSPASSPASS—0.6222PSI>0.25
avg_od_amt_m3_orig_prcp_pc—N0.0878PASSPASSPASS—0.4432PSI>0.25
avg_od_amt_m6_orig_prcp_pc—N0.0751PASSPASSPASS—0.5300PSI>0.25
avg_od_amt_m12_orig_prcp_pc—N0.0441PASSPASSPASS—0.3028PSI>0.25
ps_od_norm_int_amt—Y0.1186PASSPASSPASSPASS0.2487
max_loan_os_prcp_m3—N0.1181PASSPASSPASS—0.3345PSI>0.25
max_loan_os_prcp_m6—N0.0965PASSPASSPASS—0.2701PSI>0.25
max_loan_os_prcp_m12—N0.0642PASSPASSPASSFAIL0.2439与 sum_orig_prcp 相关>0.7
loan_os_prcp_gt0_m3_num—N0.0000PASSFAIL———集中度>0.95
loan_os_prcp_gt0_m6_num—N0.0251PASSPASSPASSFAIL0.0655与 setl_rem_prcp_paym_m3_loan_os_prcp_pc 相关>0.7
loan_os_prcp_gt0_m12_num—N0.0461PASSPASSPASSFAIL0.0761与 repay_amount_m6_loan_os_prcp_pc 相关>0.7
loan_os_prcp_gt0_m3_num_pc—N0.0000PASSFAIL———集中度>0.95
loan_os_prcp_gt0_m6_num_pc—N0.0010PASSFAIL———集中度>0.95
loan_os_prcp_gt0_m12_num_pc—N0.0186PASSPASSFAIL——IV<0.02
loanosprcpgt0subcurmonth—N0.0000PASSFAIL———集中度>0.95
loanosprcpgt0m12maxcontimons—N0.0454PASSPASSPASSFAIL0.0670与 repay_amount_m6_loan_os_prcp_pc 相关>0.7
repay_amount_last_month_loan_os_prcp_pc—Y0.1273PASSPASSPASSPASS0.1697
repay_amount_m3_loan_os_prcp_pc—N0.1131PASSPASSPASS—0.2789PSI>0.25
repay_amount_m6_loan_os_prcp_pc—Y0.0729PASSPASSPASSPASS0.2336
repay_amount_m12_loan_os_prcp_pc—N0.0318PASSPASSPASSFAIL0.0507与 setl_rem_prcp_paym_m12_loan_os_prcp_pc 相关>0.7
all_repay_amount_shd_amt_pc—N0.0980PASSPASSPASSFAIL0.1052与 repay_amount_all_shd_amt_pc 相关>0.7
all_repay_amount_m3_shd_amt_pc—Y0.1342PASSPASSPASSPASS0.1558
all_repay_amount_m6_shd_amt_pc—N0.0875PASSPASSPASSFAIL0.1764与 all_repay_amount_m3_shd_amt_pc 相关>0.7
all_repay_amount_m12_shd_amt_pc—N0.0849PASSPASSPASSFAIL0.1476与 all_repay_amount_m3_shd_amt_pc 相关>0.7
repay_amount_all_shd_amt_pc—Y0.1084PASSPASSPASSPASS0.1174
repay_amount_m3_all_shd_amt_pc—N0.0651PASSPASSPASS—0.3780PSI>0.25
repay_amount_m6_all_shd_amt_pc—N0.0497PASSPASSPASS—0.2841PSI>0.25
repay_amount_m12_all_shd_amt_pc—Y0.0527PASSPASSPASSPASS0.1581
setl_rem_prcp_paym_last_month_loan_os_prcp_pc—N0.0013PASSFAIL———集中度>0.95
setl_rem_prcp_paym_m3_loan_os_prcp_pc—Y0.0649PASSPASSPASSPASS0.1715
setl_rem_prcp_paym_m6_loan_os_prcp_pc—N0.0432PASSPASSPASSFAIL0.1261与 repay_amount_m6_loan_os_prcp_pc 相关>0.7
setl_rem_prcp_paym_m12_loan_os_prcp_pc—N0.0453PASSPASSPASSPASS0.0344重要性排名超出Top-40
max_setl_rem_prcp_paym_m3_last_month_loan_os_prcp_pc—Y0.0888PASSPASSPASSPASS0.1148
max_setl_rem_prcp_paym_m6_last_month_loan_os_prcp_pc—N0.0404PASSPASSPASSPASS0.1923重要性排名超出Top-40
max_setl_rem_prcp_paym_m12_last_month_loan_os_prcp_pc—N0.0129PASSPASSFAIL——IV<0.02
setl_rem_prcp_paym_m3_num—N0.0357PASSPASSPASSPASS0.0322重要性排名超出Top-40
setl_rem_prcp_paym_m6_num—N0.0293PASSPASSPASSFAIL0.0748与 setl_rem_prcp_paym_m3_num 相关>0.7
setl_rem_prcp_paym_m12_num—N0.0037PASSPASSFAIL——IV<0.02
loan_os_prcp_credit_amount—N0.0907PASSPASSPASS—0.6048PSI>0.25
loan_os_prcp_credit_amount_m3—N0.0716PASSPASSPASS—0.6533PSI>0.25
loan_os_prcp_credit_amount_m6—N0.0834PASSPASSPASS—0.7686PSI>0.25
loan_os_prcp_credit_amount_m12—N0.0638PASSPASSPASS—0.6855PSI>0.25
useratemore50_m3_num—N0.0511PASSPASSPASSFAIL0.1620与 loan_os_prcp_orig_prcp_pc 相关>0.7
useratemore50_m6_num—N0.0275PASSPASSPASS—0.3968PSI>0.25
useratemore50_m12_num—N0.0188PASSPASSFAIL——IV<0.02
useratemore50_m3_num_pc—N0.0511PASSPASSPASSFAIL0.1629与 loan_os_prcp_orig_prcp_pc 相关>0.7
useratemore50_m6_num_pc—N0.0481PASSPASSPASSFAIL0.2056与 useratemore90monthsubcurmonth 相关>0.7
useratemore50_m12_num_pc—N0.0445PASSPASSPASS—0.6259PSI>0.25
useratemore50monthsubcurmonth—N0.0575PASSPASSPASS—0.5423PSI>0.25
useratemore50maxcontimonsm12—N0.0178PASSPASSFAIL——IV<0.02
useratemore70_m3_num—N0.0448PASSPASSPASSFAIL0.1751与 useratemore90_m6_num 相关>0.7
useratemore70_m6_num—N0.0482PASSPASSPASSFAIL0.1940与 useratemore90_m6_num 相关>0.7
useratemore70_m12_num—N0.0139PASSPASSFAIL——IV<0.02
useratemore70_m3_num_pc—N0.0467PASSPASSPASSFAIL0.1793与 useratemore90_m6_num 相关>0.7
useratemore70_m6_num_pc—N0.0566PASSPASSPASSFAIL0.1790与 loan_os_prcp_orig_prcp_pc 相关>0.7
useratemore70_m12_num_pc—N0.0463PASSPASSPASSFAIL0.1994与 useratemore90monthsubcurmonth 相关>0.7
useratemore70monthsubcurmonth—N0.0571PASSPASSPASS—0.4424PSI>0.25
useratemore70maxcontimonsm12—N0.0148PASSPASSFAIL——IV<0.02
useratemore90_m3_num—N0.0144PASSPASSFAIL——IV<0.02
useratemore90_m6_num—Y0.0556PASSPASSPASSPASS0.0585
useratemore90_m12_num—N0.0320PASSPASSPASS—0.7297PSI>0.25
useratemore90_m3_num_pc—N0.0152PASSPASSFAIL——IV<0.02
useratemore90_m6_num_pc—N0.0548PASSPASSPASSFAIL0.0523与 useratemore90_m6_num 相关>0.7
useratemore90_m12_num_pc—N0.0446PASSPASSPASS—0.4660PSI>0.25
useratemore90monthsubcurmonth—Y0.0497PASSPASSPASSPASS0.1954
useratemore90maxcontimonsm12—N0.0292PASSPASSPASS—0.7186PSI>0.25
max_loan_os_prcp_m1—N0.1465PASSPASSPASS—0.3175PSI>0.25
orgsum1—N0.0279PASSPASSPASSFAIL0.0627与 nosvaing_bank_cnt_1m 相关>0.7
orgsum2—N0.0000PASSFAIL———集中度>0.95
recordsum1—N0.0316PASSPASSPASSFAIL0.0670与 nosvaing_bank_cnt_1m 相关>0.7
recordsum2—N0.0000PASSFAIL———集中度>0.95
recordsum3—N0.0042PASSPASSFAIL——IV<0.02
cardsum—N0.0345PASSPASSPASSFAIL0.0761与 sum_card_mob12_neverdur 相关>0.7
loanlaworg—N0.0239PASSPASSPASSPASS0.1436重要性排名超出Top-40
loanorg—N0.0000PASSFAIL———集中度>0.95
loancount—N0.0239PASSPASSPASSFAIL0.0382与 loanlaworg 相关>0.7
last6average—Y0.0500PASSPASSPASSPASS0.0458
cardlaworg—N0.0197PASSPASSFAIL——IV<0.02
cardorg—N0.0000PASSFAIL———集中度>0.95
cardaccountcount—N0.0396PASSPASSPASSFAIL0.0187与 sum_card_mob12_neverdur 相关>0.7
cyc_orgcount—N0.0282PASSPASSPASSFAIL0.0046与 cyc_accountcount 相关>0.7
cyc_accountcount—N0.0379PASSPASSPASSPASS0.0049重要性排名超出Top-40
mostcarduserate—N0.0385PASSPASSPASSPASS0.1039重要性排名超出Top-40
allcarduserate—N0.0000PASSFAIL———集中度>0.95
creditcardduetimes12m—N0.0044PASSPASSFAIL——IV<0.02
cardnumbers—N0.0336PASSPASSPASSFAIL0.0839与 sum_card_mob12_neverdur 相关>0.7
houseloannum—N0.0029PASSPASSFAIL——IV<0.02
loannum—Y0.0712PASSPASSPASSPASS0.0509
no_query_6m_cca—N0.0038PASSPASSFAIL——IV<0.02
p_mob3_neverdelq_total—N0.0971PASSPASSPASS—0.2649PSI>0.25
loanearliestmonthsubcurmonth—N0.0369PASSPASSPASSPASS0.2133重要性排名超出Top-40
cardearliestmonthsubcurmonth—N0.0334PASSPASSPASSFAIL0.1678与 cardaveragemob 相关>0.7
normaldjcardnumber—N0.0286PASSPASSPASSFAIL0.0814与 sum_card_mob12_neverdur 相关>0.7
creditquerytimes—N0.0132PASSPASSFAIL——IV<0.02
cardquerytimes—N0.0090PASSPASSFAIL——IV<0.02
loanquerytimes—N0.0196PASSPASSFAIL——IV<0.02
cardquerytimescur3month—N0.0036PASSPASSFAIL——IV<0.02
loanquerytimescur3month—N0.0229PASSPASSPASSFAIL0.0252与 bank_cnt_3m 相关>0.7
loanquery4in1monstring—N0.0304PASSPASSPASSFAIL0.0661与 nosvaing_bank_cnt_1m 相关>0.7
djcardquery4in1monstring—N0.0000PASSFAIL———集中度>0.95
allcardsoverdraftrate6avg—N0.0457PASSPASSPASSPASS0.1372重要性排名超出Top-40
sumcarduseratemore50—N0.0168PASSPASSFAIL——IV<0.02
sum_pcard_mob12_neverdur—N0.0000PASSFAIL———集中度>0.95
sum_card_mob12_neverdur—Y0.0657PASSPASSPASSPASS0.0898
sum_card_month6_m1—N0.0000PASSFAIL———集中度>0.95
sum_pcard_month6_m1—N0.0000PASSFAIL———集中度>0.95
sum_querytimes_month6—N0.0137PASSPASSFAIL——IV<0.02
cardquerytimescur12month—N0.0032PASSPASSFAIL——IV<0.02
sum_loan_month24_m2—N0.0000PASSFAIL———集中度>0.95
sum_card_month24_m2—N0.0000PASSFAIL———集中度>0.95
loanquerytimescur6month—N0.0099PASSPASSFAIL——IV<0.02
sum_card_min_pay—N0.0097PASSPASSFAIL——IV<0.02
sum_month3_new_loan—Y0.0591PASSPASSPASSPASS0.0811
sum_uncleared_loan—N0.0232PASSPASSPASSFAIL0.1120与 cyc_accountcount 相关>0.7
loanrecentlymonthsubcurmonth—N0.0361PASSPASSPASSPASS0.1078重要性排名超出Top-40
houseloannumst—N0.0027PASSPASSFAIL——IV<0.02
sum_loan_mob24_neverdur—N0.0705PASSPASSPASSFAIL0.1152与 loannum 相关>0.7
sum_card_mob24_neverdur—N0.0408PASSPASSPASSFAIL0.0913与 sum_card_mob12_neverdur 相关>0.7
sum_querytimes_month6st—N0.0129PASSPASSFAIL——IV<0.02
cardaveragemob—N0.0445PASSPASSPASSPASS0.1555重要性排名超出Top-40
sumcarduseratemore80—N0.0197PASSPASSFAIL——IV<0.02
loanquerytimescur2month—N0.0139PASSPASSFAIL——IV<0.02
cardquerytimescur2month—N0.0022PASSPASSFAIL——IV<0.02
sum_open_loan_mob24_neverdur—N0.0445PASSPASSPASSPASS0.1749重要性排名超出Top-40
sum_open_card_mob24_neverdur—N0.0348PASSPASSPASSFAIL0.0615与 sum_card_mob12_neverdur 相关>0.7
sum_open_pcard_mob24_neverdur—N0.0000PASSFAIL———集中度>0.95
cardrecentlymonthsubcurmonth—N0.0414PASSPASSPASSFAIL0.0565与 cardaveragemob 相关>0.7
loan_m12_query_num—N—FAILPASS———分箱失败(单侧样本)
card_m6_query_num—N—FAILPASS———分箱失败(单侧样本)
card_m12_query_num—N—FAILPASS———分箱失败(单侧样本)
rh_consln_clear_num_m12—N0.0059PASSPASSFAIL——IV<0.02
m1orgloan—N0.0277PASSPASSPASSFAIL0.0612与 nosvaing_bank_cnt_1m 相关>0.7
grxfloanclearednum—N0.0534PASSPASSPASSFAIL0.0639与 loannum 相关>0.7
ncarloanbalancerate6month—N0.0444PASSPASSPASSPASS0.0213重要性排名超出Top-40
mean_numl_rrp—N0.0128PASSPASSFAIL——IV<0.02
rh_cr_bal_pro—N0.0418PASSPASSPASSPASS0.0065重要性排名超出Top-40
rh_crc_uc_max_age—N0.0406PASSPASSPASSFAIL0.1521与 cardaveragemob 相关>0.7
rh_ln_qy_fst_inte—N0.0331PASSPASSPASSPASS0.0118重要性排名超出Top-40
rh_ln_y2_qyf_org_pro—N0.0066PASSPASSFAIL——IV<0.02
loan24mquerycnt—N0.0000PASSFAIL———集中度>0.95
m1orgcard—N0.0000PASSFAIL———集中度>0.95
loanquerytimescur2month_no_by—N0.0149PASSPASSFAIL——IV<0.02
no_bank_org_3m—N0.0217PASSPASSPASSFAIL0.0215与 nosvaing_bank_org_3m 相关>0.7
no_bank_org_1m—N0.0222PASSPASSPASSFAIL0.0862与 nosvaing_bank_cnt_1m 相关>0.7
no_bank_org_12m—N0.0150PASSPASSFAIL——IV<0.02
no_bank_reject_rate_6m—N0.0101PASSPASSFAIL——IV<0.02
loan_reject_rate_6m—N0.0405PASSPASSPASSPASS0.0550重要性排名超出Top-40
loan_reject_rate_3m—Y0.0569PASSPASSPASSPASS0.0683
loan_rate_3m_12m—N0.0364PASSPASSPASSPASS0.1493重要性排名超出Top-40
loan_org_1m—N0.0252PASSPASSPASSFAIL0.0697与 nosvaing_bank_cnt_1m 相关>0.7
loan_reject_rate_1m—N0.0243PASSPASSPASSPASS0.0318重要性排名超出Top-40
no_bank_org_6m—N0.0066PASSPASSFAIL——IV<0.02
query_success_rate_6m—N0.0338PASSPASSPASSPASS0.0489重要性排名超出Top-40
no_org_12m_cca—N0.0071PASSPASSFAIL——IV<0.02
loan_pass_rate_6m—N0.0299PASSPASSPASSFAIL0.0494与 loan_reject_rate_6m 相关>0.7
cardqueryatotalorg6month—N0.0047PASSPASSFAIL——IV<0.02
no_query_6m—N0.0109PASSPASSFAIL——IV<0.02
loanqueryatotalorg6month—N0.0124PASSPASSFAIL——IV<0.02
loanqueryatotalorg3month—N0.0235PASSPASSPASSFAIL0.0549与 bank_cnt_3m 相关>0.7
cardqueryatotalorg3month—N0.0031PASSPASSFAIL——IV<0.02
loan_cnt_12m—N0.0205PASSPASSPASSFAIL0.1969与 loanlaworg 相关>0.7
credithistorynum—N0.0436PASSPASSPASSFAIL0.2252与 cardaveragemob 相关>0.7
c_bank_org_1m—N0.0087PASSPASSFAIL——IV<0.02
c_bank_org_3m—N0.0166PASSPASSFAIL——IV<0.02
c_bank_org_6m—N0.0071PASSPASSFAIL——IV<0.02
c_bank_org_12m—N0.0200PASSPASSPASSFAIL0.1453与 loanlaworg 相关>0.7
bank_org_1m—N0.0096PASSPASSFAIL——IV<0.02
bank_org_3m—N0.0233PASSPASSPASSFAIL0.0512与 bank_cnt_3m 相关>0.7
bank_org_6m—N0.0081PASSPASSFAIL——IV<0.02
bank_org_12m—N0.0116PASSPASSFAIL——IV<0.02
total_org_1m—N0.0264PASSPASSPASSFAIL0.0717与 nosvaing_bank_cnt_1m 相关>0.7
total_org_3m—N0.0265PASSPASSPASSFAIL0.0687与 nosvaing_bank_org_3m 相关>0.7
total_org_6m—N0.0091PASSPASSFAIL——IV<0.02
total_org_12m—N0.0092PASSPASSFAIL——IV<0.02
nosvaing_bank_org_1m—N0.0481PASSPASSPASSFAIL0.1889与 nosvaing_bank_cnt_1m 相关>0.7
nosvaing_bank_org_3m—N0.0300PASSPASSPASSPASS0.1033重要性排名超出Top-40
nosvaing_bank_org_6m—N0.0107PASSPASSFAIL——IV<0.02
nosvaing_bank_org_12m—N0.0160PASSPASSFAIL——IV<0.02
mini_loan_org_1m—N0.0003PASSPASSFAIL——IV<0.02
mini_loan_org_3m—N0.0035PASSPASSFAIL——IV<0.02
mini_loan_org_6m—N0.0079PASSPASSFAIL——IV<0.02
mini_loan_org_12m—N0.0323PASSPASSPASS—0.5131PSI>0.25
no_bank_cnt_1m—N0.0215PASSPASSPASSFAIL0.0968与 nosvaing_bank_cnt_1m 相关>0.7
no_bank_cnt_3m—N0.0214PASSPASSPASSFAIL0.0252与 nosvaing_bank_org_3m 相关>0.7
no_bank_cnt_6m—N0.0078PASSPASSFAIL——IV<0.02
no_bank_cnt_12m—N0.0158PASSPASSFAIL——IV<0.02
bank_cnt_1m—N0.0078PASSPASSFAIL——IV<0.02
bank_cnt_3m—N0.0260PASSPASSPASSPASS0.0482重要性排名超出Top-40
bank_cnt_6m—N0.0129PASSPASSFAIL——IV<0.02
bank_cnt_12m—N0.0208PASSPASSPASSFAIL0.0758与 loanlaworg 相关>0.7
c_bank_cnt_1m—N0.0079PASSPASSFAIL——IV<0.02
c_bank_cnt_3m—N0.0199PASSPASSFAIL——IV<0.02
c_bank_cnt_6m—N0.0130PASSPASSFAIL——IV<0.02
c_bank_cnt_12m—N0.0208PASSPASSPASSFAIL0.0791与 loanlaworg 相关>0.7
total_cnt_1m—N0.0208PASSPASSPASSFAIL0.0715与 nosvaing_bank_cnt_1m 相关>0.7
total_cnt_3m—N0.0279PASSPASSPASSFAIL0.0646与 nosvaing_bank_org_3m 相关>0.7
total_cnt_6m—N0.0108PASSPASSFAIL——IV<0.02
total_cnt_12m—N0.0196PASSPASSFAIL——IV<0.02
nosvaing_bank_cnt_1m—Y0.0508PASSPASSPASSPASS0.1895
nosvaing_bank_cnt_3m—N0.0287PASSPASSPASSFAIL0.1069与 nosvaing_bank_org_3m 相关>0.7
nosvaing_bank_cnt_6m—N0.0108PASSPASSFAIL——IV<0.02
nosvaing_bank_cnt_12m—N0.0150PASSPASSFAIL——IV<0.02
mini_loan_cnt_1m—N0.0003PASSPASSFAIL——IV<0.02
mini_loan_cnt_3m—N0.0022PASSPASSFAIL——IV<0.02
mini_loan_cnt_6m—N0.0105PASSPASSFAIL——IV<0.02
mini_loan_cnt_12m—N0.0220PASSPASSPASS—0.6091PSI>0.25
no_bank_997_cnt_1m—N0.0224PASSPASSPASSFAIL0.1614与 total_997_cnt_1m 相关>0.7
no_bank_997_cnt_3m—N0.0407PASSPASSPASSFAIL0.1242与 sum_month3_new_loan 相关>0.7
no_bank_997_cnt_6m—N0.0235PASSPASSPASSFAIL0.0086与 nosvaing_997_bank_cnt_6m 相关>0.7
no_bank_997_cnt_12m—N0.0109PASSPASSFAIL——IV<0.02
c_bank_997_cnt_1m—N0.0146PASSPASSFAIL——IV<0.02
c_bank_997_cnt_3m—N0.0186PASSPASSFAIL——IV<0.02
c_bank_997_cnt_6m—N0.0379PASSPASSPASSFAIL0.1196与 bank_997_cnt_6m 相关>0.7
c_bank_997_cnt_12m—N0.0237PASSPASSPASSFAIL0.1046与 bank_997_cnt_6m 相关>0.7
bank_997_cnt_1m—N0.0143PASSPASSFAIL——IV<0.02
bank_997_cnt_3m
近3月银行查询次数		—N0.0184PASSPASSFAIL——IV<0.02
bank_997_cnt_6m—N0.0484PASSPASSPASSPASS0.1155重要性排名超出Top-40
bank_997_cnt_12m—N0.0207PASSPASSPASSFAIL0.1015与 bank_997_cnt_6m 相关>0.7
total_997_cnt_1m—N0.0321PASSPASSPASSPASS0.0760重要性排名超出Top-40
total_997_cnt_3m—N0.0549PASSPASSPASSFAIL0.0718与 sum_month3_new_loan 相关>0.7
total_997_cnt_6m—N0.0377PASSPASSPASSFAIL0.0190与 bank_997_cnt_6m 相关>0.7
total_997_cnt_12m—N0.0304PASSPASSPASSFAIL0.0226与 bank_997_cnt_6m 相关>0.7
nosvaing_997_bank_cnt_1m—N0.0249PASSPASSPASSFAIL0.1870与 total_997_cnt_1m 相关>0.7
nosvaing_997_bank_cnt_3m—N0.0510PASSPASSPASSFAIL0.1890与 sum_month3_new_loan 相关>0.7
nosvaing_997_bank_cnt_6m—N0.0347PASSPASSPASSPASS0.0234重要性排名超出Top-40
nosvaing_997_bank_cnt_12m—N0.0206PASSPASSPASSFAIL0.0048与 nosvaing_997_bank_cnt_6m 相关>0.7
mini_997_loan_cnt_1m—N0.0013PASSPASSFAIL——IV<0.02
mini_997_loan_cnt_3m—N0.0007PASSPASSFAIL——IV<0.02
mini_997_loan_cnt_6m—N0.0049PASSPASSFAIL——IV<0.02
mini_997_loan_cnt_12m—N0.0092PASSPASSFAIL——IV<0.02
rh_m1_ln_qy_num—N0.0220PASSPASSPASSFAIL0.0703与 nosvaing_bank_cnt_1m 相关>0.7
rh_m1_crc_qy_num—N0.0000PASSFAIL———集中度>0.95
rh_guar_qy_num_m1—N0.0036PASSPASSFAIL——IV<0.02
c_bank_ln_uc_num—N0.0035PASSPASSFAIL——IV<0.02
bank_ln_uc_num—N0.0035PASSPASSFAIL——IV<0.02
no_bank_ln_uc_num—N0.0138PASSPASSFAIL——IV<0.02
nosvaing_bank_ln_uc_num—N0.0150PASSPASSFAIL——IV<0.02
mini_loan_ln_uc_num—N0.0000PASSFAIL———集中度>0.95
c_bank_ln_c_num—N0.0559PASSPASSPASSFAIL0.0431与 loannum 相关>0.7
bank_ln_c_num—N0.0550PASSPASSPASSFAIL0.0410与 loannum 相关>0.7
no_bank_ln_c_num—N0.0452PASSPASSPASSFAIL0.0416与 loannum 相关>0.7
nosvaing_bank_ln_c_num—N0.0315PASSPASSPASSFAIL0.0225与 loannum 相关>0.7
mini_loan_ln_c_num—N0.0272PASSPASSPASSPASS0.0617重要性排名超出Top-40
no_bank_998_rate_1m—N0.0250PASSPASSPASSFAIL0.1611与 nosvaing_bank_rate_1m 相关>0.7
no_bank_998_rate_3m—N0.0283PASSPASSPASSFAIL0.1803与 total_998_rate_3m 相关>0.7
no_bank_998_rate_6m—N0.0172PASSPASSFAIL——IV<0.02
no_bank_998_rate_12m—N0.0404PASSPASSPASSPASS0.2307重要性排名超出Top-40
c_bank_998_rate_1m—N0.0078PASSPASSFAIL——IV<0.02
c_bank_998_rate_3m—N0.0224PASSPASSPASSFAIL0.0287与 bank_rate_1_3 相关>0.7
c_bank_998_rate_6m—N0.0273PASSPASSPASSFAIL0.0486与 c_bank_rate_1_6 相关>0.7
c_bank_998_rate_12m—N0.0157PASSPASSFAIL——IV<0.02
bank_998_rate_1m—N0.0077PASSPASSFAIL——IV<0.02
bank_998_rate_3m—N0.0204PASSPASSPASSFAIL0.0255与 bank_rate_1_3 相关>0.7
bank_998_rate_6m—N0.0257PASSPASSPASSFAIL0.0455与 c_bank_rate_1_6 相关>0.7
bank_998_rate_12m—N0.0238PASSPASSPASSFAIL0.0286与 c_bank_rate_6_12 相关>0.7
total_998_rate_1m—N0.0252PASSPASSPASSFAIL0.0965与 nosvaing_bank_rate_1m 相关>0.7
total_998_rate_3m—N0.0481PASSPASSPASSPASS0.0875重要性排名超出Top-40
total_998_rate_6m—N0.0274PASSPASSPASSFAIL0.0553与 mini_loan_rate_6m 相关>0.7
total_998_rate_12m—N0.0224PASSPASSPASSFAIL0.1077与 loan_rate_1_12 相关>0.7
nosvaing_998_bank_rate_1m—N0.0333PASSPASSPASSFAIL0.1965与 nosvaing_bank_cnt_1m 相关>0.7
nosvaing_998_bank_rate_3m—N0.0411PASSPASSPASSFAIL0.1379与 nosvaing_bank_rate_1_3 相关>0.7
nosvaing_998_bank_rate_6m—N0.0330PASSPASSPASSPASS0.0619重要性排名超出Top-40
nosvaing_998_bank_rate_12m—N0.0263PASSPASSPASSPASS0.0643重要性排名超出Top-40
mini_998_loan_rate_1m—N0.0037PASSPASSFAIL——IV<0.02
mini_998_loan_rate_3m—N0.0094PASSPASSFAIL——IV<0.02
mini_998_loan_rate_6m—N0.0234PASSPASSPASS—0.3326PSI>0.25
mini_998_loan_rate_12m—N0.0261PASSPASSPASS—0.4285PSI>0.25
no_bank_rate_1m—N0.0226PASSPASSPASSFAIL0.0824与 nosvaing_bank_rate_1m 相关>0.7
no_bank_rate_3m—N0.0174PASSPASSFAIL——IV<0.02
no_bank_rate_6m—N0.0320PASSPASSPASSFAIL0.0614与 mini_loan_rate_6m 相关>0.7
no_bank_rate_12m—N0.0377PASSPASSPASSFAIL0.1365与 loan_rate_1_12 相关>0.7
c_bank_rate_1m—N0.0230PASSPASSPASSFAIL0.0784与 nosvaing_bank_rate_1m 相关>0.7
c_bank_rate_3m—N0.0169PASSPASSFAIL——IV<0.02
c_bank_rate_6m—N0.0210PASSPASSPASSFAIL0.0390与 mini_loan_rate_6m 相关>0.7
c_bank_rate_12m—N0.0168PASSPASSFAIL——IV<0.02
bank_rate_1m—N0.0226PASSPASSPASSFAIL0.0824与 nosvaing_bank_rate_1m 相关>0.7
bank_rate_3m—N0.0180PASSPASSFAIL——IV<0.02
bank_rate_6m—N0.0287PASSPASSPASSFAIL0.0593与 mini_loan_rate_6m 相关>0.7
bank_rate_12m—N0.0290PASSPASSPASSFAIL0.1339与 loan_rate_1_12 相关>0.7
nosvaing_bank_rate_1m—N0.0434PASSPASSPASSPASS0.2355重要性排名超出Top-40
nosvaing_bank_rate_3m—N0.0417PASSPASSPASSFAIL0.2003与 total_998_rate_3m 相关>0.7
nosvaing_bank_rate_6m—N0.0219PASSPASSPASSFAIL0.0715与 mini_loan_rate_6m 相关>0.7
nosvaing_bank_rate_12m—N0.0251PASSPASSPASSFAIL0.1564与 loan_rate_1_12 相关>0.7
mini_loan_rate_1m—N0.0382PASSPASSPASSFAIL0.1385与 nosvaing_bank_rate_1m 相关>0.7
mini_loan_rate_3m—N0.0340PASSPASSPASSFAIL0.1252与 total_998_rate_3m 相关>0.7
mini_loan_rate_6m—N0.0374PASSPASSPASSPASS0.2120重要性排名超出Top-40
mini_loan_rate_12m—N0.0349PASSPASSPASS—0.2933PSI>0.25
no_bank_rate_1_3—N0.0221PASSPASSPASSFAIL0.1193与 total_998_rate_3m 相关>0.7
no_bank_rate_1_6—N0.0315PASSPASSPASSFAIL0.2114与 no_bank_rate_3_6 相关>0.7
no_bank_rate_1_12—N0.0240PASSPASSPASS—0.2849PSI>0.25
no_bank_rate_3_6—N0.0318PASSPASSPASSPASS0.1758重要性排名超出Top-40
no_bank_rate_3_12—N0.0268PASSPASSPASSFAIL0.2349与 no_bank_998_rate_12m 相关>0.7
no_bank_rate_6_12—N0.0135PASSPASSFAIL——IV<0.02
c_bank_rate_1_3—N0.0201PASSPASSPASSFAIL0.0309与 bank_rate_1_3 相关>0.7
c_bank_rate_1_6—N0.0281PASSPASSPASSPASS0.0431重要性排名超出Top-40
c_bank_rate_1_12—N0.0163PASSPASSFAIL——IV<0.02
c_bank_rate_3_6—N0.0178PASSPASSFAIL——IV<0.02
c_bank_rate_3_12—N0.0112PASSPASSFAIL——IV<0.02
c_bank_rate_6_12—N0.0394PASSPASSPASSPASS0.0194重要性排名超出Top-40
bank_rate_1_3—N0.0254PASSPASSPASSPASS0.0353重要性排名超出Top-40
bank_rate_1_6—N0.0263PASSPASSPASSFAIL0.0589与 c_bank_rate_1_6 相关>0.7
bank_rate_1_12—N0.0177PASSPASSFAIL——IV<0.02
bank_rate_3_6—N0.0197PASSPASSFAIL——IV<0.02
bank_rate_3_12—N0.0144PASSPASSFAIL——IV<0.02
bank_rate_6_12—N0.0331PASSPASSPASSFAIL0.0170与 c_bank_rate_6_12 相关>0.7
nosvaing_bank_rate_1_3—N0.0466PASSPASSPASSPASS0.2110重要性排名超出Top-40
nosvaing_bank_rate_1_6—N0.0433PASSPASSPASS—0.2641PSI>0.25
nosvaing_bank_rate_1_12—N0.0401PASSPASSPASS—0.3052PSI>0.25
nosvaing_bank_rate_3_6—N0.0318PASSPASSPASSFAIL0.2487与 nosvaing_998_bank_rate_6m 相关>0.7
nosvaing_bank_rate_3_12—N0.0359PASSPASSPASS—0.3122PSI>0.25
nosvaing_bank_rate_6_12—N0.0242PASSPASSPASSFAIL0.1490与 nosvaing_998_bank_rate_12m 相关>0.7
mini_loan_rate_1_3—N0.0027PASSPASSFAIL——IV<0.02
mini_loan_rate_1_6—N0.0128PASSPASSFAIL——IV<0.02
mini_loan_rate_1_12—N0.0071PASSPASSFAIL——IV<0.02
mini_loan_rate_3_6—N0.0169PASSPASSFAIL——IV<0.02
mini_loan_rate_3_12—N0.0129PASSPASSFAIL——IV<0.02
mini_loan_rate_6_12—N0.0200PASSPASSFAIL——IV<0.02
loan_rate_1_3—N0.0299PASSPASSPASSFAIL0.1083与 total_998_rate_3m 相关>0.7
loan_rate_1_6—N0.0269PASSPASSPASSFAIL0.2047与 mini_loan_rate_6m 相关>0.7
loan_rate_1_12—N0.0479PASSPASSPASSPASS0.2032重要性排名超出Top-40
loan_rate_3_6—N0.0313PASSPASSPASSFAIL0.1544与 mini_loan_rate_6m 相关>0.7
loan_rate_3_12—N0.0404PASSPASSPASSFAIL0.1667与 loan_rate_1_12 相关>0.7
loan_rate_6_12—N0.0222PASSPASSPASSFAIL0.0419与 loan_rate_1_12 相关>0.7
card_rate_1_3—N0.0037PASSPASSFAIL——IV<0.02
card_rate_1_6—N0.0027PASSPASSFAIL——IV<0.02
card_rate_1_12—N0.0033PASSPASSFAIL——IV<0.02
card_rate_3_6—N0.0045PASSPASSFAIL——IV<0.02
card_rate_3_12—N0.0054PASSPASSFAIL——IV<0.02
card_rate_6_12—N0.0052PASSPASSFAIL——IV<0.02
loan_new_cnt_rate_3m—N0.0257PASSPASSPASSFAIL0.0734与 total_new_cnt_rate_3m 相关>0.7
loan_new_cnt_rate_6m—N0.0250PASSPASSPASSFAIL0.0730与 total_new_cnt_rate_3m 相关>0.7
loan_new_cnt_rate_12m—N0.0238PASSPASSPASSFAIL0.1673与 total_new_cnt_rate_3m 相关>0.7
card_new_cnt_rate_3m—N0.0144PASSPASSFAIL——IV<0.02
card_new_cnt_rate_6m—N0.0144PASSPASSFAIL——IV<0.02
card_new_cnt_rate_12m—N0.0267PASSPASSPASSFAIL0.0627与 c_bank_credit_used_limit 相关>0.7
total_new_cnt_rate_3m—N0.0377PASSPASSPASSPASS0.0859重要性排名超出Top-40
total_new_cnt_rate_6m—N0.0194PASSPASSFAIL——IV<0.02
total_new_cnt_rate_12m—N0.0372PASSPASSPASSFAIL0.1920与 total_new_cnt_rate_3m 相关>0.7
no_bank_cyc_used_limit—N0.0278PASSPASSPASSPASS0.1613重要性排名超出Top-40
no_bank_uncyc_used_limit—N0.0230PASSPASSPASS—0.2654PSI>0.25
no_bank_total_used_limit—N0.0393PASSPASSPASSPASS0.0505重要性排名超出Top-40
c_bank_cyc_used_limit—N0.0209PASSPASSPASSFAIL0.0075与 bank_cyc_used_limit 相关>0.7
c_bank_uncyc_used_limit—N0.0132PASSPASSFAIL——IV<0.02
c_bank_credit_used_limit—N0.0291PASSPASSPASSPASS0.0686重要性排名超出Top-40
c_bank_total_used_limit—N0.0380PASSPASSPASSFAIL0.0481与 bank_total_used_limit 相关>0.7
bank_cyc_used_limit—N0.0259PASSPASSPASSPASS0.0124重要性排名超出Top-40
bank_uncyc_used_limit—N0.0141PASSPASSFAIL——IV<0.02
bank_credit_used_limit—N0.0285PASSPASSPASSFAIL0.0679与 c_bank_credit_used_limit 相关>0.7
bank_total_used_limit—N0.0426PASSPASSPASSPASS0.0440重要性排名超出Top-40

06 回归结果

待补充：本实验未运行 LR 路线（或未产生回归结果），无回归明细。

07 VIF

待补充：本实验未运行 LR 路线，未计算 VIF/相关系数。

08 最终评分卡

待补充：本实验未生成评分卡（未运行 LR 路线）。

09 模型表现

模型区分度评估（按样本）

模型样本KSAUCGINI
LightGBM开发样本0.30410.70110.4022
LightGBM测试样本0.29360.69170.3834
LightGBM训练+测试样本0.29420.69740.3948
LightGBM跨时间验证样本0.27290.66510.3302

LightGBM — 4 样本评估表（score_cut 粒度）

开发样本（KS 0.3041，AUC 0.7011）

cutscntbadgoodbad_ratepopulation_pctcum_bad_ratecum_good_rate
[-inf, 126)6866681897.38%10.00%10.87%2.54%
[126, 130)6876592895.92%10.02%21.58%6.48%
[130, 135)6856523395.18%9.99%32.19%11.13%
[135, 141)6856374892.99%9.99%42.55%17.89%
[141, 148)6866355192.57%10.00%52.88%25.07%
[148, 158)6866295791.69%10.00%63.11%33.10%
[158, 177)6856048188.18%9.99%72.93%44.51%
[177, 197)68658610085.42%10.00%82.47%58.59%
[197, 213)68657311383.53%10.00%91.79%74.51%
[213, inf)68650518173.62%10.00%100.00%100.00%
2026-11-03T12:04:12.691509 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-11-03T12:04:12.812512 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

测试样本（KS 0.2936，AUC 0.6917）

cutscntbadgoodbad_ratepopulation_pctcum_bad_ratecum_good_rate
[-inf, 126)4704581297.45%10.28%11.18%2.54%
[126, 130)4984712794.58%10.90%22.67%8.26%
[130, 135)4184031596.41%9.15%32.50%11.44%
[135, 141)4173912693.76%9.12%42.04%16.95%
[141, 148)4514173492.46%9.87%52.22%24.15%
[148, 158)4704234790.00%10.28%62.54%34.11%
[158, 177)4263814589.44%9.32%71.84%43.64%
[177, 197)4964296786.49%10.85%82.31%57.84%
[197, 213)4553589778.68%9.96%91.04%78.39%
[213, inf)46936710278.25%10.26%100.00%100.00%
2026-11-03T12:04:12.941687 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-11-03T12:04:13.069413 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

训练+测试样本（KS 0.2942，AUC 0.6974）

cutscntbadgoodbad_ratepopulation_pctcum_bad_ratecum_good_rate
[-inf, 126)115611263097.40%10.12%10.99%2.54%
[126, 130)118511305595.36%10.37%22.02%7.19%
[130, 135)110310554895.65%9.65%32.32%11.25%
[135, 141)110210287493.28%9.64%42.35%17.51%
[141, 148)113710528592.52%9.95%52.62%24.70%
[148, 158)1156105210491.00%10.12%62.88%33.50%
[158, 177)111198512688.66%9.72%72.50%44.16%
[177, 197)1182101516785.87%10.34%82.40%58.29%
[197, 213)114193121081.60%9.98%91.49%76.06%
[213, inf)115587228375.50%10.11%100.00%100.00%
2026-11-03T12:04:13.266351 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-11-03T12:04:13.387940 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

跨时间验证样本（KS 0.2729，AUC 0.6651）

cutscntbadgoodbad_ratepopulation_pctcum_bad_ratecum_good_rate
[-inf, 126)134128695.52%12.58%13.84%4.29%
[126, 130)147139894.56%13.80%28.86%10.00%
[130, 135)105941189.52%9.86%39.03%17.86%
[135, 141)90791187.78%8.45%47.57%25.71%
[141, 148)94831188.30%8.83%56.54%33.57%
[148, 158)7772593.51%7.23%64.32%37.14%
[158, 177)102831981.37%9.58%73.30%50.71%
[177, 197)1181001884.75%11.08%84.11%63.57%
[197, 213)94702474.47%8.83%91.68%80.71%
[213, inf)104772774.04%9.77%100.00%100.00%
2026-11-03T12:04:13.514835 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-11-03T12:04:13.642059 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

跨时间验证样本结论

坏客户在分数段上的集中度一般——最低 20% 样本捕获全量 28.9% 的坏客户。

① 样本与分箱结构：共 1,065 样本 / 925 坏样本，整体坏率 86.85%，好坏比约 0.2:1；10 档等频分数分栏，每箱约 10%，分箱均匀，无空箱无过厚箱。

② 排序性与单调性：逐箱 Bad Rate 95.52% → … → 74.04%（低分 → 高分），存在局部倒挂，建议复核分箱边界，最低分段坏率是最高分段的 1.3 倍。

③ 区分度与坏客户捕获：最低 20% 样本捕获全量 28.9% 的坏客户，最低 50% 捕获 56.5%；最高 20% 样本仅含 15.9% 的坏客户，其中风险最高一箱单独占 15.0%。

（口径：分数 = 按基准分/优比/PDO 刻度由模型概率转换，分数越高风险越低；结论由程序按分箱数据自动生成。）

注：score_cut 表按开发样本（train）分数等频 10 档取边界（低分 → 高分），各样本共用同一套边界、同栏可比（20261022 口径）；分数由模型概率按基准分/优比/PDO 刻度转换，分数越高风险越低；每表附 KS/AUC（按概率计算，与分栏口径无关）；训练+测试 = TRAIN 全体。旧实验无分数转换时为 PD 等频口径（按本样本分位切分）。

10 KS和GINI

LightGBM

样本KSGINIAUC
开发样本0.30410.40220.7011
测试样本0.29360.38340.6917
训练+测试样本0.29420.39480.6974
跨时间验证样本0.27290.33020.6651

开发样本

2026-11-03T12:04:13.732973 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-11-03T12:04:13.803357 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

测试样本

2026-11-03T12:04:13.874578 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-11-03T12:04:13.945709 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

训练+测试样本

2026-11-03T12:04:14.015890 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-11-03T12:04:14.087866 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

跨时间验证样本

2026-11-03T12:04:14.159500 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-11-03T12:04:14.230055 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

注：GINI = 2×AUC−1。KS 图 X 轴为分数（升序），红虚线为最大间隔位置；ROC 图例标注 AUC。

11 PSI

模型稳定性评估

模型训练集与测试集训练集与跨时间验证训练集与psi验证集
LightGBM0.00370.03390.0764

分数段 PSI 明细（开发样本 vs 跨时间验证样本）

分数段开发样本占比跨时间验证样本占比PSI 贡献累计 PSI
[-inf, 126)10.00%12.58%0.00590.0059
[126, 130)9.64%13.43%0.01260.0185
[130, 135)10.37%10.23%0.00000.0185
[135, 141)9.99%8.45%0.00260.0211
[141, 148)10.00%8.83%0.00150.0225
[148, 158)10.00%7.23%0.00900.0315
[158, 177)9.99%9.58%0.00020.0317
[177, 197)10.00%11.08%0.00110.0328
[197, 213)10.00%8.83%0.00150.0343
[213, inf)10.00%9.77%0.00010.0343

分数段 PSI 明细（开发样本 vs PSI时点样本）

分数段开发样本占比PSI时点占比PSI 贡献累计 PSI
[-inf, 126)10.00%16.57%0.03320.0332
[126, 130)9.64%10.95%0.00170.0348
[130, 135)10.37%12.39%0.00360.0384
[135, 141)9.99%11.67%0.00260.0411
[141, 148)10.00%9.51%0.00020.0413
[148, 158)10.00%9.22%0.00060.0419
[158, 177)9.99%7.49%0.00720.0491
[177, 197)10.00%6.63%0.01390.0630
[197, 213)10.00%8.65%0.00200.0650
[213, inf)10.00%6.92%0.01140.0764
2026-11-03T12:04:14.311930 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

注：分数段 PSI 明细按分数刻度计算（概率→分数转换，20261022 口径），分栏边界取自开发样本（train）分数等频分位，各对比样本共用同一套边界。

模型样本量分布评估

评分段开发样本样本量开发样本占比测试样本样本量测试样本占比训练+测试样本样本量训练+测试样本占比跨时间验证样本样本量跨时间验证样本占比PSI时点样本样本量PSI时点样本占比
(-inf, 126]68610.00%47010.28%115610.12%13412.58%11516.57%
(126, 130]6619.64%48910.70%115010.06%14313.43%7610.95%
(130, 135]71110.37%4279.34%11389.96%10910.23%8612.39%
(135, 141]6859.99%4179.12%11029.64%908.45%8111.67%
(141, 148]68610.00%4519.87%11379.95%948.83%669.51%
(148, 158]68610.00%47010.28%115610.12%777.23%649.22%
(158, 177]6859.99%4269.32%11119.72%1029.58%527.49%
(177, 197]68610.00%49610.85%118210.34%11811.08%466.63%
(197, 213]68610.00%4559.96%11419.98%948.83%608.65%
(213, inf]68610.00%46910.26%115510.11%1049.77%486.92%
sum6858100.00%4570100.00%11428100.00%1065100.00%694100.00%

变量 PSI（开发样本 vs PSI时点样本，按入模变量分箱）

特征项PSI状态
ps_od_norm_int_amt0.2487关注
repay_amount_m6_loan_os_prcp_pc0.2336关注
sum_orig_prcp0.2245关注
end_loan_to_cur_freq0.2156关注
od_amt_m12_loan_os_prcp_m12_pc0.2101关注
sum_apply_amt0.2029关注
useratemore90monthsubcurmonth0.1954关注
nosvaing_bank_cnt_1m0.1895关注
loan_os_prcp_orig_prcp_pc0.1869关注
setl_rem_prcp_paym_m3_loan_os_prcp_pc0.1715关注
repay_amount_last_month_loan_os_prcp_pc0.1697关注
apply_amt_avg_m120.1669关注
repay_amount_m12_all_shd_amt_pc0.1581关注
all_repay_amount_m3_shd_amt_pc0.1558关注
cur_od_loan_amt0.1478关注
account_age_loan_day0.1271关注
repay_amount_all_shd_amt_pc0.1174关注
max_setl_rem_prcp_paym_m3_last_month_loan_os_prcp_pc0.1148关注
acc_ps_perd_no0.1017关注
orig_prcp_avg_m60.0921稳定
sum_card_mob12_neverdur0.0898稳定
sum_month3_new_loan0.0811稳定
loan_reject_rate_3m0.0683稳定
odgt0_m12_mob_pc0.0678稳定
odgt0_m12_num0.0597稳定
useratemore90_m6_num0.0585稳定
account_age0.0525稳定
loannum0.0509稳定
last6average0.0458稳定
setl_prcp_mon_ratio0.0399稳定
apply_num_m60.0347稳定
setl_int_mon0.0322稳定
tothismonthodcontiaddnumm120.0301稳定
ovd_days_d30_num_m60.0296稳定
max_od_days_m120.0278稳定
od_amt_m3_loan_os_prcp_m3_pc0.0184稳定
m_c_over_due_cnt0.0162稳定
t_ps_od_days_max0.0110稳定
odduecontireducenumm120.0040稳定
odgt1_m12_subcurmonth0.0028稳定
odgt0_m12_num — PSI 0.0597
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 1.5]43.80%55.48%11.67%0.0276
(1.5, 2.5]21.45%16.71%-4.73%0.0118
(2.5, 3.5]11.90%9.51%-2.39%0.0054
(3.5, 6.5]16.73%12.10%-4.62%0.0149
(6.5, inf]6.12%6.20%0.07%0.0000
odgt0_m12_mob_pc — PSI 0.0678
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 23.61]45.23%51.30%6.07%0.0076
(23.61, 39.75]24.09%17.72%-6.37%0.0195
(39.75, 58.79]17.42%13.69%-3.74%0.0090
(58.79, 73.87]5.75%4.47%-1.28%0.0032
(73.87, inf]7.51%12.82%5.31%0.0284
m_c_over_due_cnt — PSI 0.0162
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 1.5]68.96%74.50%5.54%0.0043
(1.5, 2.5]17.19%13.54%-3.65%0.0087
(2.5, 3.5]6.58%5.33%-1.24%0.0026
(3.5, inf]7.28%6.63%-0.65%0.0006
acc_ps_perd_no — PSI 0.1017
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 2.5]44.39%59.37%14.98%0.0436
(2.5, 5.5]26.42%20.61%-5.82%0.0145
(5.5, 6.5]5.12%4.61%-0.51%0.0005
(6.5, 16.5]16.84%11.82%-5.03%0.0178
(16.5, inf]7.23%3.60%-3.63%0.0253
tothismonthodcontiaddnumm12 — PSI 0.0301
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.5]43.63%35.16%-8.47%0.0183
(0.5, inf]56.37%64.84%8.47%0.0119
loan_os_prcp_orig_prcp_pc — PSI 0.1869
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 13.04]8.02%5.91%-2.11%0.0065
(13.04, 53.4]48.72%35.01%-13.70%0.0453
(53.4, 67.97]15.46%10.81%-4.65%0.0166
(67.97, 76.46]8.15%11.53%3.38%0.0117
(76.46, inf]19.66%36.74%17.09%0.1069
all_repay_amount_m3_shd_amt_pc — PSI 0.1558
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 66.67]45.73%56.63%10.90%0.0233
(66.67, 66.73]5.50%5.04%-0.45%0.0004
(66.73, 69.51]9.00%10.95%1.95%0.0038
(69.51, 99.99]10.06%13.40%3.34%0.0096
(99.99, inf]29.72%13.98%-15.74%0.1187
ovd_days_d30_num_m6 — PSI 0.0296
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.5]76.13%83.00%6.87%0.0059
(0.5, 1.5]16.45%11.38%-5.06%0.0186
(1.5, inf]7.42%5.62%-1.80%0.0050
od_amt_m3_loan_os_prcp_m3_pc — PSI 0.0184
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 10.09]71.49%75.94%4.44%0.0027
(10.09, 14.35]10.44%9.51%-0.93%0.0009
(14.35, 24.15]7.92%4.90%-3.02%0.0145
(24.15, 25.53]5.16%4.75%-0.41%0.0003
(25.53, inf]4.99%4.90%-0.09%0.0000
repay_amount_last_month_loan_os_prcp_pc — PSI 0.1697
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.79]66.46%75.65%9.19%0.0119
(0.79, 8.66]5.02%10.66%5.65%0.0426
(8.66, 20.06]11.94%4.61%-7.33%0.0698
(20.06, 49.49]8.78%4.61%-4.17%0.0268
(49.49, inf]7.80%4.47%-3.33%0.0186
max_od_days_m12 — PSI 0.0278
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 4.5]6.62%7.20%0.58%0.0005
(4.5, 7.5]7.80%8.21%0.41%0.0002
(7.5, 9.5]5.95%5.48%-0.47%0.0004
(9.5, 29.5]66.77%71.18%4.41%0.0028
(29.5, inf]12.86%7.93%-4.94%0.0239
ps_od_norm_int_amt — PSI 0.2487
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 10.25]7.01%4.75%-2.26%0.0088
(10.25, 44.34]21.71%15.42%-6.29%0.0215
(44.34, 107.69]22.65%13.40%-9.24%0.0485
(107.69, 757.16]41.25%43.95%2.70%0.0017
(757.16, inf]7.38%22.48%15.10%0.1682
repay_amount_all_shd_amt_pc — PSI 0.1174
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 33.16]66.13%75.07%8.94%0.0113
(33.16, 84.39]5.34%8.79%3.45%0.0172
(84.39, 90.67]11.23%4.32%-6.91%0.0659
(90.67, 96.6]9.86%6.05%-3.81%0.0186
(96.6, inf]7.45%5.76%-1.69%0.0043
end_loan_to_cur_freq — PSI 0.2156
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, -362.5]6.12%6.48%0.36%0.0002
(-362.5, -248.5]25.75%13.54%-12.21%0.0784
(-248.5, -109.5]27.37%17.58%-9.79%0.0433
(-109.5, -26.5]8.19%11.24%3.04%0.0096
(-26.5, inf]32.56%51.15%18.59%0.0840
cur_od_loan_amt — PSI 0.1478
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 124.01]8.34%6.77%-1.57%0.0033
(124.01, 558.05]28.55%21.18%-7.37%0.0220
(558.05, 725.59]6.49%5.19%-1.30%0.0029
(725.59, 3545.66]43.92%39.34%-4.58%0.0050
(3545.66, inf]12.70%27.52%14.82%0.1146
odduecontireducenumm12 — PSI 0.0040
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.5]74.39%77.09%2.69%0.0010
(0.5, inf]25.61%22.91%-2.69%0.0030
max_setl_rem_prcp_paym_m3_last_month_loan_os_prcp_pc — PSI 0.1148
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 1.88]69.92%77.67%7.75%0.0081
(1.88, 11.68]7.93%11.53%3.60%0.0134
(11.68, 20.03]9.70%3.46%-6.24%0.0643
(20.03, 33.46]5.22%3.60%-1.62%0.0060
(33.46, inf]7.23%3.75%-3.49%0.0229
apply_amt_avg_m12 — PSI 0.1669
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 4525]43.80%29.83%-13.98%0.0537
(4525, 10810]17.02%18.44%1.43%0.0011
(10810, 32366.67]21.60%22.19%0.60%0.0002
(32366.67, 48025]7.32%6.05%-1.27%0.0024
(48025, inf]10.27%23.49%13.22%0.1094
orig_prcp_avg_m6 — PSI 0.0921
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 636.67]67.16%61.82%-5.35%0.0044
(636.67, 2158.33]6.85%4.75%-2.10%0.0077
(2158.33, 5016.67]7.68%5.33%-2.35%0.0086
(5016.67, 15012.5]9.20%9.22%0.02%0.0000
(15012.5, inf]9.10%18.88%9.78%0.0713
odgt1_m12_subcurmonth — PSI 0.0028
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 50003]8.79%7.35%-1.44%0.0026
(50003, inf]91.21%92.65%1.44%0.0002
account_age_loan_day — PSI 0.1271
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.52]23.99%38.76%14.77%0.0709
(0.52, 0.96]32.06%29.39%-2.67%0.0023
(0.96, 1.19]9.42%9.37%-0.05%0.0000
(1.19, 4.62]21.19%12.82%-8.36%0.0420
(4.62, inf]13.34%9.65%-3.69%0.0119
repay_amount_m6_loan_os_prcp_pc — PSI 0.2336
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 42.01]31.99%54.03%22.04%0.1155
(42.01, 70.26]33.51%23.92%-9.59%0.0323
(70.26, 82.59]7.63%6.77%-0.85%0.0010
(82.59, 99.67]5.09%4.90%-0.19%0.0001
(99.67, inf]21.78%10.37%-11.41%0.0846
sum_orig_prcp — PSI 0.2245
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 10450]36.53%29.54%-6.99%0.0148
(10450, 12554]5.18%2.02%-3.16%0.0298
(12554, 19150]12.03%6.92%-5.11%0.0283
(19150, 110100]40.96%43.80%2.84%0.0019
(110100, inf]5.31%17.72%12.42%0.1497
loannum — PSI 0.0509
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 9.5]6.74%4.07%-2.67%0.0134
(9.5, 27.5]31.29%24.43%-6.86%0.0170
(27.5, 39.5]16.08%17.80%1.72%0.0017
(39.5, 61.5]18.78%19.00%0.23%0.0000
(61.5, inf]27.11%34.69%7.58%0.0187
sum_card_mob12_neverdur — PSI 0.0898
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.5]32.50%20.97%-11.54%0.0506
(0.5, 1.5]12.92%11.46%-1.46%0.0017
(1.5, 6.5]28.77%32.13%3.36%0.0037
(6.5, 8.5]5.19%5.73%0.54%0.0005
(8.5, inf]20.62%29.71%9.09%0.0332
od_amt_m12_loan_os_prcp_m12_pc — PSI 0.2101
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.91]18.10%34.44%16.34%0.1052
(0.91, 1.09]5.61%4.75%-0.86%0.0014
(1.09, 2.17]51.62%34.58%-17.04%0.0682
(2.17, 3.17]11.40%7.49%-3.91%0.0164
(3.17, inf]13.27%18.73%5.46%0.0188
setl_int_mon — PSI 0.0322
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 3.64]73.04%73.78%0.74%0.0001
(3.64, 17.71]5.02%3.46%-1.56%0.0058
(17.71, 42.33]5.40%3.75%-1.65%0.0060
(42.33, 90.6]5.50%3.75%-1.75%0.0067
(90.6, inf]11.05%15.27%4.22%0.0137
setl_rem_prcp_paym_m3_loan_os_prcp_pc — PSI 0.1715
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, -4999]5.06%15.13%10.07%0.1103
(-4999, 3.21]65.70%63.98%-1.73%0.0005
(3.21, 19.05]12.50%12.68%0.18%0.0000
(19.05, 25.49]5.32%2.59%-2.73%0.0196
(25.49, inf]11.42%5.62%-5.80%0.0411
sum_month3_new_loan — PSI 0.0811
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.5]21.94%12.67%-9.27%0.0509
(0.5, 1.5]20.57%18.55%-2.02%0.0021
(1.5, 2.5]16.86%22.47%5.62%0.0162
(2.5, 3.5]11.71%15.54%3.82%0.0108
(3.5, inf]28.92%30.77%1.85%0.0011
sum_apply_amt — PSI 0.2029
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 2050]7.48%5.48%-2.00%0.0063
(2050, 9625]25.90%15.85%-10.05%0.0493
(9625, 11585]5.69%3.89%-1.80%0.0068
(11585, 130400]55.66%57.64%1.98%0.0007
(130400, inf]5.28%17.15%11.87%0.1398
loan_reject_rate_3m — PSI 0.0683
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, -0.15]12.82%19.61%6.79%0.0288
(-0.15, 0.02]23.62%23.23%-0.39%0.0001
(0.02, 0.28]7.20%6.18%-1.01%0.0015
(0.28, 0.58]20.84%25.04%4.20%0.0077
(0.58, inf]35.52%25.94%-9.58%0.0301
setl_prcp_mon_ratio — PSI 0.0399
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0]72.76%79.83%7.07%0.0065
(0, 0.01]7.42%6.77%-0.65%0.0006
(0.01, 0.02]5.44%4.03%-1.40%0.0042
(0.02, 0.04]6.88%5.62%-1.26%0.0026
(0.04, inf]7.49%3.75%-3.75%0.0260
useratemore90_m6_num — PSI 0.0585
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.5]64.25%55.33%-8.91%0.0133
(0.5, 1.5]8.31%12.39%4.08%0.0163
(1.5, 2.5]12.54%14.55%2.01%0.0030
(2.5, 3.5]6.43%10.81%4.38%0.0227
(3.5, inf]8.47%6.92%-1.56%0.0032
account_age — PSI 0.0525
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 6.5]27.66%27.95%0.29%0.0000
(6.5, 11.5]36.66%41.35%4.70%0.0057
(11.5, 14.5]10.45%9.80%-0.66%0.0004
(14.5, 33.5]19.68%12.39%-7.29%0.0338
(33.5, inf]5.54%8.50%2.96%0.0127
apply_num_m6 — PSI 0.0347
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.5]65.33%56.77%-8.55%0.0120
(0.5, 1.5]20.98%26.80%5.82%0.0142
(1.5, 2.5]6.77%9.37%2.60%0.0085
(2.5, inf]6.93%7.06%0.13%0.0000
repay_amount_m12_all_shd_amt_pc — PSI 0.1581
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 85.33]17.25%30.69%13.44%0.0774
(85.33, 86.76]5.45%6.92%1.46%0.0035
(86.76, 89.01]6.98%10.81%3.82%0.0167
(89.01, 99.76]65.30%48.70%-16.59%0.0486
(99.76, inf]5.02%2.88%-2.13%0.0118
t_ps_od_days_max — PSI 0.0110
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.5]73.43%77.38%3.94%0.0021
(0.5, 3.5]11.24%8.79%-2.45%0.0060
(3.5, 11.5]6.30%6.34%0.04%0.0000
(11.5, inf]9.03%7.49%-1.53%0.0029
nosvaing_bank_cnt_1m — PSI 0.1895
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.5]65.57%44.67%-20.90%0.0802
(0.5, 1.5]21.73%30.84%9.11%0.0319
(1.5, inf]12.71%24.50%11.79%0.0774
last6average — PSI 0.0458
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 128.5]22.80%23.20%0.40%0.0001
(128.5, 977.5]19.68%13.26%-6.43%0.0254
(977.5, 2255.5]17.74%15.42%-2.33%0.0033
(2255.5, 3242.5]9.91%10.95%1.04%0.0010
(3242.5, inf]29.87%37.18%7.31%0.0160
useratemore90monthsubcurmonth — PSI 0.1954
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 1.5]13.02%17.72%4.70%0.0145
(1.5, 3.5]11.13%15.42%4.29%0.0140
(3.5, 5.5]11.61%11.53%-0.08%0.0000
(5.5, 8.5]14.23%25.36%11.13%0.0643
(8.5, inf]50.01%29.97%-20.04%0.1026

注：PSI ≤ 0.1 视为稳定，0.1~0.25 需关注，> 0.25 不稳定。

12 单变量分析

注：统计所有入模变量的单变量分析（开发样本口径）。预留验证/总体/OOT 口径的分组明细待扩展。

odgt0_m12_num
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 1.5]300443.80%169283594.37%0.06
2(1.5, 2.5]147165.25%126134591.43%0.09
3(2.5, 3.5]81677.15%11070686.52%0.16
4(3.5, 6.5]114793.88%19994882.65%0.21
5(6.5, inf]420100.00%10631474.76%0.34
合计6858710614889.65%0.12
2026-11-03T12:04:14.498842 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调下降（94.37% → 74.76%），WOE 单调上升（-0.6613 → 1.0727），符合风控分箱预期。IV=0.3489，该变量区分度尚可。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 1 箱（(-inf, 1.5]）为极高风险区间：坏率高达 94.37%，策略上应重点管控

odgt0_m12_mob_pc
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 23.61]310245.23%188291493.94%0.06
2(23.61, 39.75]165269.32%165148790.01%0.11
3(39.75, 58.79]119586.75%20499182.93%0.21
4(58.79, 73.87]39492.49%5134387.06%0.15
5(73.87, inf]515100.00%10241380.19%0.25
合计6858710614889.65%0.12
2026-11-03T12:04:14.632295 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（93.94% → 93.94% → 80.19%），如需严格单调可考虑调整切点或合并分箱。IV=0.2573，该变量区分度尚可。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 3→4 箱有局部反弹：Bad Rate 由 82.93% 回升至 87.06%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 23.61]）为极高风险区间：坏率高达 93.94%，策略上应重点管控

m_c_over_due_cnt
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 1.5]472968.96%343438692.75%0.08
2(1.5, 2.5]117986.15%172100785.41%0.17
3(2.5, 3.5]45192.72%8436781.37%0.23
4(3.5, inf]499100.00%11138877.76%0.29
合计6858710614889.65%0.12
2026-11-03T12:04:14.760674 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调下降（92.75% → 77.76%），WOE 单调上升（-0.3898 → 0.9071），符合风控分箱预期。IV=0.2452，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 1 箱（(-inf, 1.5]）为极高风险区间：坏率高达 92.75%，策略上应重点管控

acc_ps_perd_no
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 2.5]304444.39%205283993.27%0.07
2(2.5, 5.5]181270.81%197161589.13%0.12
3(5.5, 6.5]35175.93%2332893.45%0.07
4(6.5, 16.5]115592.77%18397284.16%0.19
5(16.5, inf]496100.00%10239479.44%0.26
合计6858710614889.65%0.12
2026-11-03T12:04:14.888216 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（93.27% → 93.45% → 79.44%），如需严格单调可考虑调整切点或合并分箱。IV=0.2055，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 89.13% 回升至 93.45%，如需严格单调可考虑合并这两箱

第 3 箱（(5.5, 6.5]）为极高风险区间：坏率高达 93.45%，策略上应重点管控

tothismonthodcontiaddnumm12
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.5]299243.63%445254785.13%0.17
2(0.5, inf]3866100.00%265360193.15%0.07
合计6858710614889.65%0.12
2026-11-03T12:04:15.006251 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调上升（85.13% → 93.15%），WOE 单调下降（0.4140 → -0.4506），符合风控分箱预期。IV=0.1837，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 1 箱 WOE<0（风险高于平均）

第 2 箱（(0.5, inf]）为极高风险区间：坏率高达 93.15%，策略上应重点管控

loan_os_prcp_orig_prcp_pc
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 13.04]5508.02%11543579.09%0.26
2(13.04, 53.4]334156.74%372296988.87%0.13
3(53.4, 67.97]106072.19%9796390.85%0.10
4(67.97, 76.46]55980.34%2853194.99%0.05
5(76.46, inf]1348100.00%98125092.73%0.08
合计6858710614889.65%0.12
2026-11-03T12:04:15.130975 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（79.09% → 94.99% → 92.73%），如需严格单调可考虑调整切点或合并分箱。IV=0.1437，该变量区分度尚可。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 4→5 箱有局部反弹：Bad Rate 由 94.99% 回落至 92.73%，如需严格单调可考虑合并这两箱

第 4 箱（(67.97, 76.46]）为极高风险区间：坏率高达 94.99%，策略上应重点管控

all_repay_amount_m3_shd_amt_pc
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 66.67]313645.73%229290792.70%0.08
2(66.67, 66.73]37751.22%3833989.92%0.11
3(66.73, 69.51]61760.22%8453386.39%0.16
4(69.51, 99.99]69070.28%13056081.16%0.23
5(99.99, inf]2038100.00%229180988.76%0.13
合计6858710614889.65%0.12
2026-11-03T12:04:15.259509 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（92.70% → 92.70% → 88.76%），如需严格单调可考虑调整切点或合并分箱。IV=0.1342，该变量区分度尚可。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 4→5 箱有局部反弹：Bad Rate 由 81.16% 回升至 88.76%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 66.67]）为极高风险区间：坏率高达 92.70%，策略上应重点管控

ovd_days_d30_num_m6
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.5]522176.13%437478491.63%0.09
2(0.5, 1.5]112892.58%17695284.40%0.18
3(1.5, inf]509100.00%9741280.94%0.24
合计6858710614889.65%0.12
2026-11-03T12:04:15.392982 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调下降（91.63% → 80.94%），WOE 单调上升（-0.2345 → 0.7123），符合风控分箱预期。IV=0.1315，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 2 箱 WOE<0（风险高于平均）

第 1 箱（(-inf, 0.5]）为极高风险区间：坏率高达 91.63%，策略上应重点管控

od_amt_m3_loan_os_prcp_m3_pc
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 10.09]490371.49%415448891.54%0.09
2(10.09, 14.35]71681.93%8063688.83%0.13
3(14.35, 24.15]54389.85%9345082.87%0.21
4(24.15, 25.53]35495.01%4530987.29%0.15
5(25.53, inf]342100.00%7726577.49%0.29
合计6858710614889.65%0.12
2026-11-03T12:04:15.513475 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（91.54% → 91.54% → 77.49%），如需严格单调可考虑调整切点或合并分箱。IV=0.1301，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 3→4 箱有局部反弹：Bad Rate 由 82.87% 回升至 87.29%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 10.09]）为极高风险区间：坏率高达 91.54%，策略上应重点管控

repay_amount_last_month_loan_os_prcp_pc
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.79]455866.46%382417691.62%0.09
2(0.79, 8.66]34471.48%5029485.47%0.17
3(8.66, 20.06]81983.42%8073990.23%0.11
4(20.06, 49.49]60292.20%8851485.38%0.17
5(49.49, inf]535100.00%11042579.44%0.26
合计6858710614889.65%0.12
2026-11-03T12:04:15.639673 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（91.62% → 91.62% → 79.44%），如需严格单调可考虑调整切点或合并分箱。IV=0.1273，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 85.47% 回升至 90.23%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 0.79]）为极高风险区间：坏率高达 91.62%，策略上应重点管控

max_od_days_m12
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 4.5]4546.62%2842693.83%0.07
2(4.5, 7.5]53514.42%6646987.66%0.14
3(7.5, 9.5]40820.37%2238694.61%0.06
4(9.5, 29.5]457987.14%433414690.54%0.10
5(29.5, inf]882100.00%16172181.75%0.22
合计6858710614889.65%0.12
2026-11-03T12:04:15.765861 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（93.83% → 94.61% → 81.75%），如需严格单调可考虑调整切点或合并分箱。IV=0.1213，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 87.66% 回升至 94.61%，如需严格单调可考虑合并这两箱

第 3 箱（(7.5, 9.5]）为极高风险区间：坏率高达 94.61%，策略上应重点管控

ps_od_norm_int_amt
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 10.25]4817.01%8939281.50%0.23
2(10.25, 44.34]148928.73%162132789.12%0.12
3(44.34, 107.69]155351.37%195135887.44%0.14
4(107.69, 757.16]282992.62%243258691.41%0.09
5(757.16, inf]506100.00%2148595.85%0.04
合计6858710614889.65%0.12
2026-11-03T12:04:15.971443 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（81.50% → 95.85% → 95.85%），如需严格单调可考虑调整切点或合并分箱。IV=0.1186，该变量区分度尚可。

WOE 在第 3、4 箱之间穿越 0 线：前 3 箱 WOE>0（客户质量优于整体），后 2 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 89.12% 回落至 87.44%，如需严格单调可考虑合并这两箱

第 5 箱（(757.16, inf]）为极高风险区间：坏率高达 95.85%，策略上应重点管控

repay_amount_all_shd_amt_pc
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 33.16]453566.13%379415691.64%0.09
2(33.16, 84.39]36671.46%5531184.97%0.18
3(84.39, 90.67]77082.69%9867287.27%0.15
4(90.67, 96.6]67692.55%7859888.46%0.13
5(96.6, inf]511100.00%10041180.43%0.24
合计6858710614889.65%0.12
2026-11-03T12:04:16.099018 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（91.64% → 91.64% → 80.43%），如需严格单调可考虑调整切点或合并分箱。IV=0.1084，该变量区分度尚可。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 84.97% 回升至 87.27%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 33.16]）为极高风险区间：坏率高达 91.64%，策略上应重点管控

end_loan_to_cur_freq
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, -362.5]4206.12%8433680.00%0.25
2(-362.5, -248.5]176631.88%199156788.73%0.13
3(-248.5, -109.5]187759.24%159171891.53%0.09
4(-109.5, -26.5]56267.44%2853495.02%0.05
5(-26.5, inf]2233100.00%240199389.25%0.12
合计6858710614889.65%0.12
2026-11-03T12:04:16.230202 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（80.00% → 95.02% → 89.25%），如需严格单调可考虑调整切点或合并分箱。IV=0.1019，该变量区分度尚可。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 4→5 箱有局部反弹：Bad Rate 由 95.02% 回落至 89.25%，如需严格单调可考虑合并这两箱

第 4 箱（(-109.5, -26.5]）为极高风险区间：坏率高达 95.02%，策略上应重点管控

cur_od_loan_amt
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 124.01]5728.34%8648684.97%0.18
2(124.01, 558.05]195836.89%227173188.41%0.13
3(558.05, 725.59]44543.38%6837784.72%0.18
4(725.59, 3545.66]301287.30%283272990.60%0.10
5(3545.66, inf]871100.00%4682594.72%0.06
合计6858710614889.65%0.12
2026-11-03T12:04:16.356828 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（84.97% → 94.72% → 94.72%），如需严格单调可考虑调整切点或合并分箱。IV=0.0936，该变量区分度偏弱但可用。

WOE 在第 3、4 箱之间穿越 0 线：前 3 箱 WOE>0（客户质量优于整体），后 2 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 88.41% 回落至 84.72%，如需严格单调可考虑合并这两箱

第 5 箱（(3545.66, inf]）为极高风险区间：坏率高达 94.72%，策略上应重点管控

odduecontireducenumm12
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.5]510274.39%440466291.38%0.09
2(0.5, inf]1756100.00%270148684.62%0.18
合计6858710614889.65%0.12
2026-11-03T12:04:16.472500 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调下降（91.38% → 84.62%），WOE 单调上升（-0.2018 → 0.4532），符合风控分箱预期。IV=0.0908，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 1 箱 WOE<0（风险高于平均）

第 1 箱（(-inf, 0.5]）为极高风险区间：坏率高达 91.38%，策略上应重点管控

max_setl_rem_prcp_paym_m3_last_month_loan_os_prcp_pc
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 1.88]479569.92%544425188.65%0.13
2(1.88, 11.68]54477.85%2052496.32%0.04
3(11.68, 20.03]66587.55%5860791.28%0.10
4(20.03, 33.46]35892.77%2433493.30%0.07
5(33.46, inf]496100.00%6443287.10%0.15
合计6858710614889.65%0.12
2026-11-03T12:04:16.588791 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（88.65% → 96.32% → 87.10%），如需严格单调可考虑调整切点或合并分箱。IV=0.0888，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 1→2 箱有局部反弹：Bad Rate 由 88.65% 回升至 96.32%，如需严格单调可考虑合并这两箱

第 2 箱（(1.88, 11.68]）为极高风险区间：坏率高达 96.32%，策略上应重点管控

apply_amt_avg_m12
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 4525]300443.80%381262387.32%0.15
2(4525, 10810]116760.82%124104389.37%0.12
3(10810, 32366.67]148182.41%110137192.57%0.08
4(32366.67, 48025]50289.73%5344989.44%0.12
5(48025, inf]704100.00%4266294.03%0.06
合计6858710614889.65%0.12
2026-11-03T12:04:16.715053 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（87.32% → 94.03% → 94.03%），如需严格单调可考虑调整切点或合并分箱。IV=0.0793，该变量区分度偏弱但可用。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 3→4 箱有局部反弹：Bad Rate 由 92.57% 回落至 89.44%，如需严格单调可考虑合并这两箱

第 5 箱（(48025, inf]）为极高风险区间：坏率高达 94.03%，策略上应重点管控

orig_prcp_avg_m6
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 636.67]460667.16%542406488.23%0.13
2(636.67, 2158.33]47074.02%4642490.21%0.11
3(2158.33, 5016.67]52781.70%4748091.08%0.10
4(5016.67, 15012.5]63190.90%4658592.71%0.08
5(15012.5, inf]624100.00%2959595.35%0.05
合计6858710614889.65%0.12
2026-11-03T12:04:16.841524 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调上升（88.23% → 95.35%），WOE 单调下降（0.1440 → -0.8626），符合风控分箱预期。IV=0.0769，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 5 箱（(15012.5, inf]）为极高风险区间：坏率高达 95.35%，策略上应重点管控

odgt1_m12_subcurmonth
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 50003]6038.79%11848580.43%0.24
2(50003, inf]6255100.00%592566390.54%0.10
合计6858710614889.65%0.12
2026-11-03T12:04:16.952728 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调上升（80.43% → 90.54%），WOE 单调下降（0.7452 → -0.0996），符合风控分箱预期。IV=0.0738，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 1 箱 WOE<0（风险高于平均）

第 2 箱（(50003, inf]）为极高风险区间：坏率高达 90.54%，策略上应重点管控

account_age_loan_day
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.52]164523.99%118152792.83%0.08
2(0.52, 0.96]219956.05%240195989.09%0.12
3(0.96, 1.19]64665.47%10753983.44%0.20
4(1.19, 4.62]145386.66%137131690.57%0.10
5(4.62, inf]915100.00%10880788.20%0.13
合计6858710614889.65%0.12
2026-11-03T12:04:17.061527 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（92.83% → 92.83% → 88.20%），如需严格单调可考虑调整切点或合并分箱。IV=0.0736，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 3→4 箱有局部反弹：Bad Rate 由 83.44% 回升至 90.57%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 0.52]）为极高风险区间：坏率高达 92.83%，策略上应重点管控

repay_amount_m6_loan_os_prcp_pc
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 42.01]219431.99%173202192.11%0.09
2(42.01, 70.26]229865.50%228207090.08%0.11
3(70.26, 82.59]52373.13%6945486.81%0.15
4(82.59, 99.67]34978.22%6828180.52%0.24
5(99.67, inf]1494100.00%172132288.49%0.13
合计6858710614889.65%0.12
2026-11-03T12:04:17.182633 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（92.11% → 92.11% → 88.49%），如需严格单调可考虑调整切点或合并分箱。IV=0.0729，该变量区分度偏弱但可用。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 4→5 箱有局部反弹：Bad Rate 由 80.52% 回升至 88.49%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 42.01]）为极高风险区间：坏率高达 92.11%，策略上应重点管控

sum_orig_prcp
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 10450]250536.53%314219187.47%0.14
2(10450, 12554]35541.70%2133494.08%0.06
3(12554, 19150]82553.73%9672988.36%0.13
4(19150, 110100]280994.69%263254690.64%0.10
5(110100, inf]364100.00%1634895.60%0.05
合计6858710614889.65%0.12
2026-11-03T12:04:17.386407 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（87.47% → 95.60% → 95.60%），如需严格单调可考虑调整切点或合并分箱。IV=0.0720，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 94.08% 回落至 88.36%，如需严格单调可考虑合并这两箱

第 5 箱（(110100, inf]）为极高风险区间：坏率高达 95.60%，策略上应重点管控

loannum
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 9.5]4356.74%7036583.91%0.19
2(9.5, 27.5]202038.03%242177888.02%0.14
3(27.5, 39.5]103854.11%8795191.62%0.09
4(39.5, 61.5]121272.89%137107588.70%0.13
5(61.5, inf]1750100.00%130162092.57%0.08
合计6455666578989.68%0.12
2026-11-03T12:04:17.509320 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（83.91% → 92.57% → 92.57%），如需严格单调可考虑调整切点或合并分箱。IV=0.0712，该变量区分度偏弱但可用。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 3→4 箱有局部反弹：Bad Rate 由 91.62% 回落至 88.70%，如需严格单调可考虑合并这两箱

第 5 箱（(61.5, inf]）为极高风险区间：坏率高达 92.57%，策略上应重点管控

sum_card_mob12_neverdur
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.5]209832.50%252184687.99%0.14
2(0.5, 1.5]83445.42%7775790.77%0.10
3(1.5, 6.5]185774.19%214164388.48%0.13
4(6.5, 8.5]33579.38%1332296.12%0.04
5(8.5, inf]1331100.00%110122191.74%0.09
合计6455666578989.68%0.12
2026-11-03T12:04:17.635710 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（87.99% → 96.12% → 91.74%），如需严格单调可考虑调整切点或合并分箱。IV=0.0657，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 90.77% 回落至 88.48%，如需严格单调可考虑合并这两箱

第 4 箱（(6.5, 8.5]）为极高风险区间：坏率高达 96.12%，策略上应重点管控

od_amt_m12_loan_os_prcp_m12_pc
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.91]124118.10%129111289.61%0.12
2(0.91, 1.09]38523.71%5133486.75%0.15
3(1.09, 2.17]354075.33%299324191.55%0.09
4(2.17, 3.17]78286.73%8969388.62%0.13
5(3.17, inf]910100.00%14276884.40%0.18
合计6858710614889.65%0.12
2026-11-03T12:04:17.767900 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（89.61% → 91.55% → 84.40%），如需严格单调可考虑调整切点或合并分箱。IV=0.0654，该变量区分度偏弱但可用。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 86.75% 回升至 91.55%，如需严格单调可考虑合并这两箱

第 3 箱（(1.09, 2.17]）为极高风险区间：坏率高达 91.55%，策略上应重点管控

setl_int_mon
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 3.64]500973.04%457455290.88%0.10
2(3.64, 17.71]34478.05%5928582.85%0.21
3(17.71, 42.33]37083.45%4632487.57%0.14
4(42.33, 90.6]37788.95%6731082.23%0.22
5(90.6, inf]758100.00%8167789.31%0.12
合计6858710614889.65%0.12
2026-11-03T12:04:17.896429 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（90.88% → 90.88% → 89.31%），如需严格单调可考虑调整切点或合并分箱。IV=0.0652，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 82.85% 回升至 87.57%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 3.64]）为极高风险区间：坏率高达 90.88%，策略上应重点管控

setl_rem_prcp_paym_m3_loan_os_prcp_pc
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, -4999]3475.06%2232593.66%0.07
2(-4999, 3.21]450670.76%525398188.35%0.13
3(3.21, 19.05]85783.26%5080794.17%0.06
4(19.05, 25.49]36588.58%4232388.49%0.13
5(25.49, inf]783100.00%7171290.93%0.10
合计6858710614889.65%0.12
2026-11-03T12:04:18.023860 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（93.66% → 94.17% → 90.93%），如需严格单调可考虑调整切点或合并分箱。IV=0.0649，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 88.35% 回升至 94.17%，如需严格单调可考虑合并这两箱

第 3 箱（(3.21, 19.05]）为极高风险区间：坏率高达 94.17%，策略上应重点管控

sum_month3_new_loan
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.5]141621.94%182123487.15%0.15
2(0.5, 1.5]132842.51%142118689.31%0.12
3(1.5, 2.5]108859.36%13695287.50%0.14
4(2.5, 3.5]75671.08%5270493.12%0.07
5(3.5, inf]1867100.00%154171391.75%0.09
合计6455666578989.68%0.12
2026-11-03T12:04:18.153268 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（87.15% → 93.12% → 91.75%），如需严格单调可考虑调整切点或合并分箱。IV=0.0591，该变量区分度偏弱但可用。

WOE 在第 3、4 箱之间穿越 0 线：前 3 箱 WOE>0（客户质量优于整体），后 2 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 89.31% 回落至 87.50%，如需严格单调可考虑合并这两箱

第 4 箱（(2.5, 3.5]）为极高风险区间：坏率高达 93.12%，策略上应重点管控

sum_apply_amt
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 2050]5137.48%7543885.38%0.17
2(2050, 9625]177633.38%187158989.47%0.12
3(9625, 11585]39039.06%6332783.85%0.19
4(11585, 130400]381794.72%366345190.41%0.11
5(130400, inf]362100.00%1934394.75%0.06
合计6858710614889.65%0.12
2026-11-03T12:04:18.277856 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（85.38% → 94.75% → 94.75%），如需严格单调可考虑调整切点或合并分箱。IV=0.0571，该变量区分度偏弱但可用。

WOE 在第 3、4 箱之间穿越 0 线：前 3 箱 WOE>0（客户质量优于整体），后 2 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 89.47% 回落至 83.85%，如需严格单调可考虑合并这两箱

第 5 箱（(130400, inf]）为极高风险区间：坏率高达 94.75%，策略上应重点管控

loan_reject_rate_3m
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, -0.15]82512.82%6576092.12%0.09
2(-0.15, 0.02]152036.44%172134888.68%0.13
3(0.02, 0.28]46343.64%2843593.95%0.06
4(0.28, 0.58]134164.48%115122691.42%0.09
5(0.58, inf]2286100.00%284200287.58%0.14
合计6435664577189.68%0.12
2026-11-03T12:04:18.407005 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（92.12% → 93.95% → 87.58%），如需严格单调可考虑调整切点或合并分箱。IV=0.0569，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 88.68% 回升至 93.95%，如需严格单调可考虑合并这两箱

第 3 箱（(0.02, 0.28]）为极高风险区间：坏率高达 93.95%，策略上应重点管控

setl_prcp_mon_ratio
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0]499072.76%458453290.82%0.10
2(0, 0.01]50980.18%6944086.44%0.16
3(0.01, 0.02]37385.62%3334091.15%0.10
4(0.02, 0.04]47292.51%7639683.90%0.19
5(0.04, inf]514100.00%7444085.60%0.17
合计6858710614889.65%0.12
2026-11-03T12:04:18.541031 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（90.82% → 91.15% → 85.60%），如需严格单调可考虑调整切点或合并分箱。IV=0.0556，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 86.44% 回升至 91.15%，如需严格单调可考虑合并这两箱

第 3 箱（(0.01, 0.02]）为极高风险区间：坏率高达 91.15%，策略上应重点管控

useratemore90_m6_num
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.5]440664.25%515389188.31%0.13
2(0.5, 1.5]57072.56%5351790.70%0.10
3(1.5, 2.5]86085.10%5380793.84%0.07
4(2.5, 3.5]44191.53%3141092.97%0.08
5(3.5, inf]581100.00%5852390.02%0.11
合计6858710614889.65%0.12
2026-11-03T12:04:18.669719 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（88.31% → 93.84% → 90.02%），如需严格单调可考虑调整切点或合并分箱。IV=0.0556，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 3→4 箱有局部反弹：Bad Rate 由 93.84% 回落至 92.97%，如需严格单调可考虑合并这两箱

第 3 箱（(1.5, 2.5]）为极高风险区间：坏率高达 93.84%，策略上应重点管控

account_age
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 6.5]189727.66%151174692.04%0.09
2(6.5, 11.5]251464.32%270224489.26%0.12
3(11.5, 14.5]71774.77%11360484.24%0.19
4(14.5, 33.5]135094.46%146120489.19%0.12
5(33.5, inf]380100.00%3035092.11%0.09
合计6858710614889.65%0.12
2026-11-03T12:04:18.881024 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（92.04% → 92.11% → 92.11%），如需严格单调可考虑调整切点或合并分箱。IV=0.0555，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 4 箱 WOE<0（风险高于平均）

第 1→2 箱有局部反弹：Bad Rate 由 92.04% 回落至 89.26%，如需严格单调可考虑合并这两箱

第 5 箱（(33.5, inf]）为极高风险区间：坏率高达 92.11%，策略上应重点管控

apply_num_m6
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.5]448065.33%531394988.15%0.13
2(0.5, 1.5]143986.31%112132792.22%0.08
3(1.5, 2.5]46493.07%3742792.03%0.09
4(2.5, inf]475100.00%3044593.68%0.07
合计6858710614889.65%0.12
2026-11-03T12:04:19.008046 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（88.15% → 93.68% → 93.68%），如需严格单调可考虑调整切点或合并分箱。IV=0.0555，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 92.22% 回落至 92.03%，如需严格单调可考虑合并这两箱

第 4 箱（(2.5, inf]）为极高风险区间：坏率高达 93.68%，策略上应重点管控

repay_amount_m12_all_shd_amt_pc
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 85.33]118317.25%111107290.62%0.10
2(85.33, 86.76]37422.70%2235294.12%0.06
3(86.76, 89.01]47929.69%8139883.09%0.20
4(89.01, 99.76]447894.98%471400789.48%0.12
5(99.76, inf]344100.00%2531992.73%0.08
合计6858710614889.65%0.12
2026-11-03T12:04:19.130787 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（90.62% → 94.12% → 92.73%），如需严格单调可考虑调整切点或合并分箱。IV=0.0527，该变量区分度偏弱但可用。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 2→3 箱有局部反弹：Bad Rate 由 94.12% 回落至 83.09%，如需严格单调可考虑合并这两箱

第 2 箱（(85.33, 86.76]）为极高风险区间：坏率高达 94.12%，策略上应重点管控

t_ps_od_days_max
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.5]503673.43%466457090.75%0.10
2(0.5, 3.5]77184.67%10966285.86%0.16
3(3.5, 11.5]43290.97%7335983.10%0.20
4(11.5, inf]619100.00%6255789.98%0.11
合计6858710614889.65%0.12
2026-11-03T12:04:19.260985 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（90.75% → 90.75% → 89.98%），如需严格单调可考虑调整切点或合并分箱。IV=0.0523，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 3→4 箱有局部反弹：Bad Rate 由 83.10% 回升至 89.98%，如需严格单调可考虑合并这两箱

第 1 箱（(-inf, 0.5]）为极高风险区间：坏率高达 90.75%，策略上应重点管控

nosvaing_bank_cnt_1m
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.5]428865.57%492379688.53%0.13
2(0.5, 1.5]142187.29%135128690.50%0.10
3(1.5, inf]831100.00%4878394.22%0.06
合计6540675586589.68%0.12
2026-11-03T12:04:19.383304 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性良好：随分栏增大，Bad Rate 单调上升（88.53% → 94.22%），WOE 单调下降（0.1188 → -0.6299），符合风控分箱预期。IV=0.0508，该变量区分度偏弱但可用。

WOE 在第 1、2 箱之间穿越 0 线：前 1 箱 WOE>0（客户质量优于整体），后 2 箱 WOE<0（风险高于平均）

第 3 箱（(1.5, inf]）为极高风险区间：坏率高达 94.22%，策略上应重点管控

last6average
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 128.5]149322.80%158133589.42%0.12
2(128.5, 977.5]128942.48%166112387.12%0.15
3(977.5, 2255.5]116260.22%113104990.28%0.11
4(2255.5, 3242.5]64970.13%8756286.59%0.15
5(3242.5, inf]1956100.00%152180492.23%0.08
合计6549676587389.68%0.12
2026-11-03T12:04:19.504852 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（89.42% → 92.23% → 92.23%），如需严格单调可考虑调整切点或合并分箱。IV=0.0500，该变量区分度偏弱但可用。

WOE 在第 2、3 箱之间穿越 0 线：前 2 箱 WOE>0（客户质量优于整体），后 3 箱 WOE<0（风险高于平均）

第 1→2 箱有局部反弹：Bad Rate 由 89.42% 回落至 87.12%，如需严格单调可考虑合并这两箱

第 5 箱（(3242.5, inf]）为极高风险区间：坏率高达 92.23%，策略上应重点管控

useratemore90monthsubcurmonth
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 1.5]89313.02%7681791.49%0.09
2(1.5, 3.5]76324.15%4771693.84%0.07
3(3.5, 5.5]79635.75%7272490.95%0.10
4(5.5, 8.5]97649.99%12085687.70%0.14
5(8.5, inf]3430100.00%395303588.48%0.13
合计6858710614889.65%0.12
2026-11-03T12:04:19.635913 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

关键结论

单调性欠佳：Bad Rate 随分栏存在波动（91.49% → 93.84% → 88.48%），如需严格单调可考虑调整切点或合并分箱。IV=0.0497，该变量区分度偏弱但可用。

WOE 在第 3、4 箱之间穿越 0 线：前 3 箱 WOE>0（客户质量优于整体），后 2 箱 WOE<0（风险高于平均）

第 1→2 箱有局部反弹：Bad Rate 由 91.49% 回升至 93.84%，如需严格单调可考虑合并这两箱

第 2 箱（(1.5, 3.5]）为极高风险区间：坏率高达 93.84%，策略上应重点管控

13 压力测试

注：验证压力情境下模型的有效性和区分能力稳定性：a.坏浓度更高样本 b.时间外样本 c.特定特征客群 d.极端时间样本。当前流程自动生成情景1（贡献度最高的变量调用失败，20261026 批注2）与情景2（客群资质变差——跨时间样本剔除一半好样本，批注16）。

测试情景1：贡献度最高的变量调用失败

变量贡献度

变量贡献度
odgt0_m12_num104.0000
loan_os_prcp_orig_prcp_pc45.0000
odgt0_m12_mob_pc41.0000
m_c_over_due_cnt41.0000
ovd_days_d30_num_m641.0000
setl_int_mon27.0000
repay_amount_last_month_loan_os_prcp_pc26.0000
setl_prcp_mon_ratio25.0000
loannum23.0000
cur_od_loan_amt20.0000
失效变量贡献度口径值KS（前 → 后）AUC（前 → 后）KS 衰减AUC 衰减
odgt0_m12_num4058.43040.2729 → 0.26320.6651 → 0.6673-0.00970.0022

注：失效处理口径：LR 将该变量 WOE 置 0（中性箱）后按系数重算概率；树模型将该特征置缺失重打分；评估样本 = 跨时间验证样本。衰减幅度反映模型对头部变量的依赖程度。

区分度验证 — LightGBM @ 跨时间样本

KS = 0.2729，GINI = 0.3302

2026-11-03T12:04:19.774324 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-11-03T12:04:19.902939 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

测试情景2：客群资质变差

指标数值
样本量1065 → 995（剔除好样本 70）
KS（前 → 后）0.2729 → 0.2408（Δ -0.0321）
AUC（前 → 后）0.6651 → 0.6564（Δ -0.0087）
GINI（后）0.3128
2026-11-03T12:04:20.037039 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-11-03T12:04:20.246870 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

注：情景2 模拟客群资质变差的压力情形（好客户变少、坏客户占比升高）：随机剔除一半好样本（固定 seed=42，可复现）后复算 KS/AUC。