风控模型开发验证报告

实验 ID: DEV-mvuuzzng-3d16 · 生成时间: 2026-10-30 19:04 (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-mvuuzzng-3d16
开发完成日期2026-10-30
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preloan_approval
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LR + WOE 评分卡
目标变量gbflag
实验 IDDEV-mvuuzzng-3d16
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2021-10-17 ~ 2026-04-30
预留验证样本窗口2024-04-30 ~ 2026-04-30
跨时间验证样本窗口2021-12-31 ~ 2026-06-30
PSI时点样本窗口2023-11-18 ~ 2026-07-31

模型好坏定义

项目内容
目标变量gbflag
坏样本(BAD=1)占比8.03%
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
2create_time主键/时间/口径字段,不入模
3data_flag主键/时间/口径字段,不入模
4gbflag 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

开发时点 — 样本排除

类别样本数
全部样本115682
排除:目标变量为空0
排除后样本115682

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11568210638992938.03%100.00%
开发样本380883493731518.27%32.92%
测试样本253912329121008.27%21.95%
训练+测试样本634795822852518.27%54.87%
跨时间验证样本347303229824327.00%30.02%
PSI时点样本17473158631610—15.10%

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582283493760.00%2329140.00%
坏5251315160.01%210039.99%
合计634793808860.00%2539140.00%

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
due_day_my11.61160.00%46.32%0.0972
last_od_to_cur_freq0.19280.02%70.20%0.4978
max_od_days0.18700.02%25.81%0.5557
last_loan_setl_tnr0.16690.02%18.96%0.3034
end_loan_to_cur_freq0.14950.02%6.32%0.6425
apprv_amt_day0.14300.02%79.67%0.6396
cre_l60_used_limit_rate_min0.12720.06%10.21%0.7274
ps_perd_no0.12440.00%13.10%0.6425
loan_os_prcp_credit_amount_m30.12220.07%9.66%0.5204
cre_l90_used_limit_amt_min0.11600.06%14.62%0.7390
shd_pre_repay_due_rate0.11041.11%16.57%0.5562
odgt0_m12_subcurmonth0.10940.07%34.86%0.7415
cre_now_l60_used_amt_m_min0.10490.06%74.02%0.6396
cre_now_l60_limit_left_d_max0.10040.48%67.92%0.7660
loan_os_prcp_credit_amount_m60.09440.07%2.98%0.5204
f_ps_od_days_max0.09320.02%53.63%0.5202
suc_apprv_num_m10.09090.02%79.23%0.6800
acc_ps_perd_no0.08750.02%25.81%0.4281
last_od_num_m60.08590.02%78.72%0.6476
od_amt_m6_loan_os_prcp_m6_pc0.07980.07%93.14%0.6644
loan_os_prcp_credit_amount_m120.07950.07%0.23%0.2371
ps_int_amt_mon0.07750.02%5.50%0.7503
cre_l30_l90_used_amt_d_max0.07720.34%21.39%0.6593
available_limit_amt0.07710.02%32.48%0.8089
setl_prcp_ratio0.07680.02%21.83%0.6593
orig_prcp_avg_m30.07630.02%60.04%0.5989
cre_l30_l90_used_amt_m_max0.07310.06%21.48%0.6810
cre_now_l30_used_amt_d_min0.07105.71%86.91%0.9733
shd_max_dpd0.06901.05%74.62%0.3801
cre_l60_used_limit_rate_max0.06900.06%6.42%0.7274
od_amt_m12_loan_os_prcp_m12_pc0.06490.07%92.39%0.6644
repay_amount_m3_loan_os_prcp_pc0.06340.07%38.42%0.6366
t_ps_od_days_max0.06070.02%54.18%0.5455
apprv_num_m10.05950.02%73.93%0.7533
s_ps_od_days_max0.05940.02%54.12%0.5455
shd_l30_max_dpd0.05762.96%71.65%0.7038
apprv_num_m30.05720.02%52.89%0.7533
useratemore50maxcontimonsm120.05300.07%10.71%0.6543
cre_l30_limit_left_amt_min0.05040.06%4.18%0.7875
ps_int_amt_sum0.04650.02%1.21%0.7396
cre_now_l60_limit_left_d_min0.04565.74%11.24%0.8178
avg_tnr0.04560.02%30.34%0.6841
odgt0_m12_mob_pc0.04470.07%33.87%0.6158
cre_now_l60_used_amt_d_min0.042910.25%81.68%0.7521
max_loan_os_prcp_m120.04110.07%4.04%0.7503
odgt0_m12_num0.04070.07%34.86%0.7141
useratemore70_m6_num0.04050.07%28.42%0.6941
cre_now_l60_used_amt_m_max0.04000.06%11.21%0.6810
suc_apprv_num_m60.03950.02%34.85%0.7214
cre_now_p1m_used_rate_d0.03915.64%4.54%0.9733
useratemore70monthsubcurmonth0.03840.07%35.85%0.6077
orig_prcp_avg_m120.03720.02%3.24%0.6695
cre_l30_l90_used_amt_m_min0.03690.06%78.40%0.5989
shd_pre_repay_amt_rate0.03380.08%1.29%0.6789
cre_usedrate_u30_daydelta_max0.033012.49%83.72%0.6366
repay_amount_m6_all_shd_amt_pc0.03210.07%3.66%0.7885
ps_int_amt_avg0.03100.02%1.21%0.6095
cre_now_l30_used_amt_d_max0.02990.52%14.27%0.6000
cre_now_l30_limit_left_d_min0.02894.23%14.95%0.3058
repay_amount_m6_loan_os_prcp_pc0.02820.07%54.31%0.5912
shd_l60_min_dpd0.02782.50%51.07%0.7466
min_apply_amt0.02730.02%10.57%0.4938
useratemore90maxcontimonsm120.02690.07%24.43%0.6895
cre_now_l90_limit_left_d_min0.02556.91%9.77%0.8178
repay_amount_all_shd_amt_pc0.02540.07%5.67%0.6536
shd_instm_amt_sum0.02480.08%0.03%0.7396
useratemore90_m3_num0.02400.07%61.13%0.6941
cre_l30_l90_usedrate_m_max0.02280.06%26.85%0.7107
odgt0_m3_num0.02270.07%53.85%0.7141
cre_now_l60_usedrate_m_max0.02260.06%13.38%0.7107
m_s_ps_instm_amt0.02160.02%70.25%0.4301
min_tnr0.02150.02%53.18%0.6841
cre_now_p3m_used_rate_d0.021211.82%1.60%0.7521
setl_rem_prcp_paym_last_month_lo0.02030.07%87.85%0.6536
shd_l180_instm_amt_sum0.02000.08%0.11%0.7458
last_clear_num_m30.02000.02%57.66%0.7214
loan_os_prcp_gt0_m12_num_pc0.01960.07%61.32%0.5912
ps_prcp_amt_mon0.01830.02%4.60%0.7458
cre_now_p1m_used_rate_m0.01740.93%4.84%0.7553
cre_now_p2m_used_rate_m0.01581.66%2.23%0.7553
shd_l30_pre_repay_due_rate0.01533.11%48.56%0.7855
end_loan_to_rep_loan_freq0.01330.02%6.97%0.4280
shd_pre_repay_cnt0.01330.08%1.22%0.5711
shd_remain_actual_cnt0.01250.08%7.33%0.6008
account_age0.01240.07%4.74%0.7206
shd_l90_ps_due_cnt0.01180.08%17.68%0.6782
cre_now_l30_limit_left_d_max0.01160.72%79.30%0.7660
min_apprv_amt0.01100.02%29.17%0.5699
last_clear_num_m10.01000.02%79.20%0.7141
shd_min_dpd0.00941.06%26.93%0.7113
sum_apply_amt0.00870.02%10.45%0.4938
account_age_loan_day0.00840.07%2.11%0.7206
shd_remain_actual_rate0.00820.41%66.46%0.7855
max_int_rat0.00800.02%34.15%0.4054
last_apply_freq0.00800.02%87.14%0.6518
max_od_due0.00690.07%74.02%0.7415
setl_prcp_mon_ratio0.00610.07%6.05%0.4346
grade_limit_amount0.00510.02%38.22%0.4065
shd_l180_max_dpd0.00431.69%57.75%0.7885
cre_usedrate_u30_daydelta_min0.004012.49%81.81%0.4730

注:共 125 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)

due_day_my — IV 19.9031,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 30.5]349373493700.00%8.7486
2(30.5, inf]315103151100.00%-11.1544
last_od_to_cur_freq — IV 0.2002,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]104878126725.48%-1.3326
2(0.5, 1.5]2963243552817.82%-0.8773
3(1.5, inf]340693171423556.91%0.1943
f_ps_od_days_max — IV 0.1798,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]2042218144227811.15%-0.3309
2(0.5, inf]17658167868724.94%0.5516
ovd_days_d3_num_m1 — IV 0.1789,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]346763222824487.06%0.1716
2(0.5, inf]3404270270220.62%-1.0581
suc_apprv_num_m1 — IV 0.1594,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]301692728728829.55%-0.1580
2(0.5, 1.5]478745911964.09%0.7478
3(1.5, inf]31243052722.30%1.3409
last_loan_setl_tnr — IV 0.1487,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]723969942453.38%0.9456
2(0.5, 3.5]11453105429117.95%0.0426
3(3.5, inf]1938817394199410.28%-0.2400
apprv_amt_day — IV 0.1475,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 11.0]303442745228929.53%-0.1555
2(11.0, inf]773674782583.34%0.9608
ps_perd_no — IV 0.1423,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]499148451462.93%1.0963
2(1.5, 3.5]639560033926.13%0.3229
3(3.5, inf]267022408926139.79%-0.1846
cre_now_l60_used_amt_m_min — IV 0.1300,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.7799999713897705]282012545827439.73%-0.1775
2(1.7799999713897705, inf]986494574074.13%0.7402
apprv_num_m1 — IV 0.1224,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]281522542527279.69%-0.1734
2(0.5, 1.5]535051072434.54%0.6394
3(1.5, inf]457843981803.93%0.7900
orig_prcp_avg_m3 — IV 0.1156,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 23.449999809265137]2286420513235110.28%-0.2397
2(23.449999809265137, inf]15216144177995.25%0.4869
end_loan_to_cur_freq — IV 0.1126,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, -68.5]2363521233240210.16%-0.2267
2(-68.5, inf]14445136977485.18%0.5016
last_od_num_m6 — IV 0.1092,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]299772791920586.87%0.2016
2(0.5, inf]81037011109213.48%-0.5465
cre_l60_used_limit_rate_min — IV 0.1055,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.2787500023841858]14577137698085.54%0.4301
2(0.2787500023841858, 0.9161500036716461]188991715717429.22%-0.1181
3(0.9161500036716461, inf]4589398960013.07%-0.5111
max_od_days — IV 0.1040,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 12.5]359353322427117.54%0.1000
2(12.5, inf]2145170643920.47%-1.0485
cre_l90_used_limit_amt_min — IV 0.1036,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 748.7099914550781]13694129347605.55%0.4288
2(748.7099914550781, 8516.22998046875]201721835418189.01%-0.0934
3(8516.22998046875, inf]4199362757213.62%-0.5585
cre_now_l30_used_amt_d_min — IV 0.1036,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.0000500082969666]312112827329389.41%-0.0980
2(1.0000500082969666, 2.482349991798401]363535041313.60%0.9242
3(2.482349991798401, inf]10661043232.16%1.4521
available_limit_amt — IV 0.1036,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.03499999921768904]1238510918146711.85%-0.3987
2(0.03499999921768904, 27316.265625]237712218315886.68%0.2309
3(27316.265625, inf]19241829954.94%0.5517
apprv_num_m3 — IV 0.0951,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]2013918040209910.42%-0.2548
2(0.5, 2.5]1020095756256.13%0.3232
3(2.5, inf]774173154265.50%0.4373
setl_prcp_ratio — IV 0.0909,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.035099999979138374]832579603654.38%0.6763
2(0.035099999979138374, inf]297552697027859.36%-0.1355
loan_os_prcp_credit_amount_m6 — IV 0.0893,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 48.94499969482422]12564118756895.48%0.4412
2(48.94499969482422, 107.59499740600586]187351707416618.87%-0.0756
3(107.59499740600586, inf]6761596279911.82%-0.3959
acc_ps_perd_no — IV 0.0875,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]98278680114711.67%-0.3821
2(0.5, 7.5]132211211111108.40%-0.0162
3(7.5, inf]15032141398935.94%0.3562
ps_int_amt_mon — IV 0.0837,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.475000023841858]23542275793.36%0.9543
2(1.475000023841858, 132.375]287512653222197.72%0.0754
3(132.375, inf]6975612385212.22%-0.4337
cre_l30_l90_used_amt_m_max — IV 0.0833,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 35.010000228881836]863382194144.80%0.5828
2(35.010000228881836, 1951.3800048828125]216251973518908.74%-0.0597
3(1951.3800048828125, inf]7807696184610.84%-0.2980
shd_l30_max_dpd — IV 0.0827,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, -0.5]13761338382.76%1.1741
2(-0.5, 0.5]264822448519977.54%0.1191
3(0.5, inf]91018026107511.81%-0.3769
avg_od_amt_m6_orig_prcp_pc — IV 0.0815,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5699999928474426]355683286627027.60%0.0927
2(0.5699999928474426, inf]2492204544717.94%-0.8851
od_amt_m6_loan_os_prcp_m6_pc — IV 0.0804,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.38500000536441803]355693286527047.60%0.0920
2(0.38500000536441803, inf]2491204644517.86%-0.8801
cre_now_l60_used_amt_d_min — IV 0.0761,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.000950038433075]279432520527389.80%-0.1106
2(1.000950038433075, 1.4549500346183777]235622351215.14%0.5858
3(1.4549500346183777, inf]388637151714.40%0.7481
loan_os_prcp_credit_amount_m12 — IV 0.0725,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 44.21000099182129]919787294685.09%0.5202
2(44.21000099182129, 95.76499938964844]210881923118578.81%-0.0682
3(95.76499938964844, inf]7775695182410.60%-0.2732
cre_l30_l90_used_amt_d_max — IV 0.0705,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.9735499918460846]291702645527159.31%-0.1269
2(0.9735499918460846, inf]879083584324.91%0.5590
shd_max_dpd — IV 0.0690,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]95658427113811.90%-0.4008
2(0.5, inf]281222613419887.07%0.1731
loan_os_prcp_gt0_m3_num_pc — IV 0.0685,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 58.334999084472656]13791341382.76%1.1579
2(58.334999084472656, 68.16500091552734]19751911643.24%0.9908
3(68.16500091552734, inf]347063165930478.78%-0.0649
shd_pre_repay_due_rate — IV 0.0672,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.9498499929904938]170221597210506.17%0.3197
2(0.9498499929904938, inf]2064218566207610.06%-0.2114
od_amt_m12_loan_os_prcp_m12_pc — IV 0.0658,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.0950000025331974]352223253226907.64%0.0870
2(0.0950000025331974, inf]2838237945916.17%-0.7603
loan_os_prcp_credit_amount_m3 — IV 0.0657,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 100.47500228881836]311082884222667.28%0.1381
2(100.47500228881836, inf]6952606988312.70%-0.4781
max_od_due — IV 0.0638,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]98078661114611.69%-0.3832
2(0.5, inf]282532625020037.09%0.1673
odgt0_m12_subcurmonth — IV 0.0635,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]98048659114511.68%-0.3825
2(0.5, inf]282562625220047.09%0.1669
cre_l60_used_limit_rate_max — IV 0.0614,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.22405000030994415]426540422235.23%0.4918
2(0.22405000030994415, 1.2138000130653381]265202445720637.78%0.0672
3(1.2138000130653381, inf]7280641686411.87%-0.4005
useratemore50maxcontimonsm12 — IV 0.0530,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 2.5]836579244415.27%0.4829
2(2.5, 4.5]589354014928.35%-0.0099
3(4.5, inf]238022158622169.31%-0.1294
cre_now_l60_used_amt_m_max — IV 0.0511,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 83.30500030517578]514849042444.74%0.5951
2(83.30500030517578, 2003.010009765625]240882208320058.32%-0.0063
3(2003.010009765625, inf]8829792890110.21%-0.2309
shd_l30_pre_repay_due_rate — IV 0.0506,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.8838499784469604]6969614482511.84%-0.3781
2(0.8838499784469604, 4.9175498485565186]279212573621857.83%0.0803
3(4.9175498485565186, inf]20131914994.92%0.5758
ps_int_amt_sum — IV 0.0471,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1891.7449951171875]239582223217267.20%0.1498
2(1891.7449951171875, 10533.93994140625]113161027610409.19%-0.1154
3(10533.93994140625, inf]2806242238413.69%-0.5642
cre_usedrate_u30_daydelta_max — IV 0.0468,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 57.5]282927001294.56%0.6900
2(57.5, 85.5]211219921205.68%0.4582
3(85.5, inf]283912574126509.33%-0.0776
cre_l30_limit_left_amt_min — IV 0.0459,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, -132.05500030517578]7225636985611.85%-0.3986
2(-132.05500030517578, inf]308402854622947.44%0.1157
setl_rem_prcp_paym_m3_num — IV 0.0433,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]320942924028548.89%-0.0789
2(0.5, 1.5]415939522074.98%0.5435
3(1.5, inf]18071719884.87%0.5664
odgt0_m12_num — IV 0.0425,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]1326811926134210.11%-0.2212
2(0.5, 5.5]215921994516477.63%0.0883
3(5.5, inf]320030401605.00%0.5387
useratemore70monthsubcurmonth — IV 0.0416,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4.5]254292310223279.15%-0.1104
2(4.5, 9.5]603755644737.83%0.0593
3(9.5, inf]659462453495.29%0.4787
max_loan_os_prcp_m12 — IV 0.0415,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1747.2849731445312]410138602415.88%0.3679
2(1747.2849731445312, 20848.58984375]289052660622997.95%0.0429
3(20848.58984375, inf]5054444560912.05%-0.4180
avg_tnr — IV 0.0412,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 6.5]876882545145.86%0.3703
2(6.5, 10.5]126711164510268.10%0.0233
3(10.5, inf]166411503116109.67%-0.1721
useratemore70_m6_num — IV 0.0405,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]15252142619916.50%0.2609
2(1.5, 5.5]178801623616449.19%-0.1156
3(5.5, inf]4928441451410.43%-0.2554
suc_apprv_num_m6 — IV 0.0404,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]220111992520869.48%-0.1492
2(1.5, 3.5]781272405727.32%0.1323
3(3.5, inf]825777654925.96%0.3530
odgt0_m12_mob_pc — IV 0.0402,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4.164999961853027]1327211930134210.11%-0.2208
2(4.164999961853027, 20.130000114440918]125321151210208.14%0.0179
3(20.130000114440918, inf]12256114697876.42%0.2735
m_s_ps_instm_amt — IV 0.0371,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 33.55999946594238]268902491619747.34%0.1295
2(33.55999946594238, 1767.905029296875]100729045102710.20%-0.2304
3(1767.905029296875, inf]111896914913.33%-0.5336
shd_pre_repay_amt_rate — IV 0.0360,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.6238499879837036]12637118148236.51%0.2584
2(0.6238499879837036, 0.7506499886512756]863179446877.96%0.0421
3(0.7506499886512756, inf]167911515216399.76%-0.1816
orig_prcp_avg_m12 — IV 0.0348,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 3819.857177734375]271502513320177.43%0.1166
2(3819.857177734375, 13787.5]890980418689.74%-0.1798
3(13787.5, inf]2021175626513.11%-0.5149
repay_amount_m6_all_shd_amt_pc — IV 0.0344,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 93.98500061035156]132571194613119.89%-0.1961
2(93.98500061035156, 99.48500061035156]197431819315507.85%0.0571
3(99.48500061035156, inf]506047722885.69%0.4018
useratemore90maxcontimonsm12 — IV 0.0335,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]929887235756.18%0.3136
2(0.5, 4.5]216531981018438.51%-0.0309
3(4.5, inf]7109637873110.28%-0.2395
cre_now_p1m_used_rate_m — IV 0.0308,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, -4.999999873689376e-05]728868704185.74%0.3962
2(-4.999999873689376e-05, inf]304462773527118.90%-0.0779
last_clear_num_m3 — IV 0.0302,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]219561990820489.33%-0.1317
2(0.5, 2.5]1046097027587.25%0.1435
3(2.5, inf]566453203446.07%0.3326
useratemore90_m3_num — IV 0.0302,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]293832714722367.61%0.0909
2(1.5, 2.5]397936233568.95%-0.0856
3(2.5, inf]4698414155711.86%-0.3996
loan_os_prcp_gt0_m12_num_pc — IV 0.0299,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 36.93000030517578]16671608593.54%0.8995
2(36.93000030517578, 57.73500061035156]163115301016.19%0.3122
3(57.73500061035156, inf]347623177329898.60%-0.0420
repay_amount_m3_loan_os_prcp_pc — IV 0.0283,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, -4999.0]480445542505.20%0.4966
2(-4999.0, inf]332563035728998.72%-0.0571
shd_perd_max — IV 0.0281,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4.5]10401014262.50%1.2579
2(4.5, 11.0]142813281007.00%0.1806
3(11.0, inf]355913256830238.49%-0.0286
shd_l90_ps_due_cnt — IV 0.0275,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 2.5]248623831034.14%0.7357
2(2.5, inf]355733252730468.56%-0.0374
ps_int_amt_avg — IV 0.0273,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 13.080849647521973]163101517111396.98%0.1833
2(13.080849647521973, 38.24515151977539]143511310612458.68%-0.0520
3(38.24515151977539, inf]7419665376610.32%-0.2443
min_apply_amt — IV 0.0268,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1135.0]3388301037811.16%-0.3311
2(1135.0, 7300.0]272042490323018.46%-0.0243
3(7300.0, inf]748870174716.29%0.2953
cre_now_l30_limit_left_d_min — IV 0.0262,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.997050017118454]6136544369311.29%-0.3306
2(0.997050017118454, inf]303392797523647.79%0.0793
shd_instm_amt_sum — IV 0.0258,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 17727.4052734375]154541433311217.25%0.1427
2(17727.4052734375, 134568.0234375]178261634114858.33%-0.0074
3(134568.0234375, inf]4779423654311.36%-0.3514
t_ps_od_days_max — IV 0.0245,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 7.5]368513389529568.02%0.0335
2(7.5, inf]1229103519415.79%-0.7316
setl_rem_prcp_paym_last_month_lo — IV 0.0240,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 25.880000114440918]364783338530938.48%-0.0268
2(25.880000114440918, inf]15821526563.54%0.8993
ps_prcp_amt_mon — IV 0.0232,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 38.48500061035156]206919661034.98%0.5431
2(38.48500061035156, 955.4700012207031]225732080117727.85%0.0569
3(955.4700012207031, inf]134381216312759.49%-0.1505
odgt0_m3_num — IV 0.0227,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]300542771923357.77%0.0684
2(1.5, 2.5]536648824849.02%-0.0945
3(2.5, inf]2640231033012.50%-0.4598
shd_l180_instm_amt_sum — IV 0.0217,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 5366.869873046875]147451367710687.24%0.1442
2(5366.869873046875, 34552.490234375]187101713715738.41%-0.0174
3(34552.490234375, inf]4604409650811.03%-0.3184
cre_now_l60_limit_left_d_min — IV 0.0217,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.9717499911785126]8039718685310.61%-0.2558
2(0.9717499911785126, inf]278622569321697.78%0.0850
min_tnr — IV 0.0215,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 5.0]229492126016897.36%0.1267
2(5.0, inf]151311367014619.66%-0.1699
cre_now_l60_usedrate_m_max — IV 0.0211,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.08044999837875366]10805100947116.58%0.2475
2(0.08044999837875366, inf]272602482124398.95%-0.0854
tothismonthodcontireducenumm12 — IV 0.0209,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]328993004428558.68%-0.0521
2(0.5, inf]516148672945.70%0.4009
cre_l30_l90_usedrate_m_max — IV 0.0208,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.0799499973654747]151891413310566.95%0.1885
2(0.0799499973654747, inf]228762078220949.15%-0.1105
cre_now_l90_limit_left_d_min — IV 0.0204,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.9937500059604645]9689869799210.24%-0.2183
2(0.9937500059604645, inf]257672378119867.71%0.0935
last_clear_num_m1 — IV 0.0203,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]301612750926528.79%-0.0667
2(0.5, 1.5]491645943226.55%0.2520
3(1.5, inf]300328271765.86%0.3705
cre_now_p2m_used_rate_m — IV 0.0190,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, -0.00014999999621068127]1033696486886.66%0.2388
2(-0.00014999999621068127, inf]271212469924228.93%-0.0797
shd_l60_min_dpd — IV 0.0190,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]342183146427548.05%0.0444
2(0.5, inf]2917255835912.31%-0.4278
tothismonthodcontiaddnumm12 — IV 0.0187,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]330573046325947.85%0.0576
2(0.5, inf]5003444855511.09%-0.3245
shd_pre_repay_cnt — IV 0.0175,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 25.5]956088766847.15%0.1575
2(25.5, 362.5]215941983917558.13%0.0195
3(362.5, inf]6905619571010.28%-0.2395
cre_now_p1m_used_rate_d — IV 0.0173,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.6786499917507172]654861234256.49%0.3004
2(0.6786499917507172, inf]293922673726559.03%-0.0577
last_apply_freq — IV 0.0169,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 6.5]333023067926237.88%0.0533
2(6.5, inf]4778425152711.03%-0.3182
cre_now_p3m_used_rate_d — IV 0.0161,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.40674999356269836]436340892746.28%0.3566
2(0.40674999356269836, inf]292222656226609.10%-0.0451
cre_now_l30_used_amt_d_max — IV 0.0151,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.6786499917507172]737668984786.48%0.2678
2(0.6786499917507172, inf]305152784626698.75%-0.0566
account_age — IV 0.0148,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 21.5]178221650113217.41%0.1193
2(21.5, 29.5]751468976178.21%0.0083
3(29.5, inf]127241151312119.52%-0.1537
shd_remain_actual_cnt — IV 0.0129,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 3.5]712966624676.55%0.2522
2(3.5, inf]309302824826828.67%-0.0512
setl_prcp_mon_ratio — IV 0.0125,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.0011139735579490662]2888259229610.25%-0.2359
2(0.0011139735579490662, 0.09995238855481148]333183056727518.26%0.0022
3(0.09995238855481148, inf]185417521025.50%0.4378
shd_pre_repay_cnt_rate — IV 0.0124,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]523249043286.27%0.2991
2(0.5, inf]328273000628218.59%-0.0414
shd_remain_actual_rate — IV 0.0123,3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.15245000272989273]935894414.38%0.6794
2(0.15245000272989273, 0.41544999182224274]285326571966.87%0.2041
3(0.41544999182224274, inf]341453123529108.52%-0.0294
cre_usedrate_u30_daydelta_min — IV 0.0116,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 2.5]273532486924849.08%-0.0474
2(2.5, inf]597955644156.94%0.2446
suc_apply_trench_num — IV 0.0091,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]355033264028638.06%0.0277
2(1.5, inf]2577229028711.14%-0.3291
cre_now_l60_limit_left_d_max — IV 0.0088,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, -0.013299999758601189]2253199925411.27%-0.3455
2(-0.013299999758601189, inf]356513277728748.06%0.0255
end_loan_to_rep_loan_freq — IV 0.0086,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 2399.5]365343346430708.40%-0.0172
2(2399.5, inf]15461466805.17%0.5023
max_int_rat — IV 0.0079,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.2142000049352646]372734912366.33%0.2882
2(0.2142000049352646, inf]343533143929148.48%-0.0274
grade_limit_amount — IV 0.0072,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 180500.0]197591800217578.89%-0.0791
2(180500.0, inf]183211692813937.60%0.0916
repay_amount_m6_loan_os_prcp_pc — IV 0.0070,2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, -4999.0]682863534756.96%0.1877
2(-4999.0, inf]312322855826748.56%-0.0374

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 125 个;完整数据见 feature_report.json。

04 单变量分析(粗分箱)

due_day_my — 2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 30.5]349373493700.00%8.7486
2(30.5, inf]315103151100.00%-11.1544
2026-10-30T19:04:18.580433 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(0.00% → 100.00%),WOE 单调下降(8.7486 → -11.1544),符合风控分箱预期。IV=19.9031,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((30.5, inf])为极高风险区间:坏率高达 100.00%,策略上应重点管控

f_ps_od_days_max — 2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]2042218144227811.15%-0.3309
2(0.5, inf]17658167868724.94%0.5516
2026-10-30T19:04:18.684438 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(11.15% → 4.94%),WOE 单调上升(-0.3309 → 0.5516),符合风控分箱预期。IV=0.1798,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 1 箱((-inf, 0.5])为坏率最高区间:坏率高达 11.15%,可作为拒绝或重点风控策略的阈值参考

suc_apprv_num_m1 — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]301692728728829.55%-0.1580
2(0.5, 1.5]478745911964.09%0.7478
3(1.5, inf]31243052722.30%1.3409
2026-10-30T19:04:18.798667 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(9.55% → 2.30%),WOE 单调上升(-0.1580 → 1.3409),符合风控分箱预期。IV=0.1594,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])为坏率最高区间:坏率高达 9.55%,可作为拒绝或重点风控策略的阈值参考

apprv_amt_day — 2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 11.0]303442745228929.53%-0.1555
2(11.0, inf]773674782583.34%0.9608
2026-10-30T19:04:18.911429 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(9.53% → 3.34%),WOE 单调上升(-0.1555 → 0.9608),符合风控分箱预期。IV=0.1475,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 1 箱((-inf, 11.0])为坏率最高区间:坏率高达 9.53%,可作为拒绝或重点风控策略的阈值参考

cre_now_l60_used_amt_m_min — 2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.7799999713897705]282012545827439.73%-0.1775
2(1.7799999713897705, inf]986494574074.13%0.7402
2026-10-30T19:04:19.016095 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(9.73% → 4.13%),WOE 单调上升(-0.1775 → 0.7402),符合风控分箱预期。IV=0.1300,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 1 箱((-inf, 1.7799999713897705])为坏率最高区间:坏率高达 9.73%,可作为拒绝或重点风控策略的阈值参考

apprv_num_m1 — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]281522542527279.69%-0.1734
2(0.5, 1.5]535051072434.54%0.6394
3(1.5, inf]457843981803.93%0.7900
2026-10-30T19:04:19.123823 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(9.69% → 3.93%),WOE 单调上升(-0.1734 → 0.7900),符合风控分箱预期。IV=0.1224,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])为坏率最高区间:坏率高达 9.69%,可作为拒绝或重点风控策略的阈值参考

end_loan_to_cur_freq — 2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, -68.5]2363521233240210.16%-0.2267
2(-68.5, inf]14445136977485.18%0.5016
2026-10-30T19:04:19.232805 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(10.16% → 5.18%),WOE 单调上升(-0.2267 → 0.5016),符合风控分箱预期。IV=0.1126,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 1 箱((-inf, -68.5])为坏率最高区间:坏率高达 10.16%,可作为拒绝或重点风控策略的阈值参考

cre_l60_used_limit_rate_min — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.2787500023841858]14577137698085.54%0.4301
2(0.2787500023841858, 0.9161500036716461]188991715717429.22%-0.1181
3(0.9161500036716461, inf]4589398960013.07%-0.5111
2026-10-30T19:04:19.344435 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(5.54% → 13.07%),WOE 单调下降(0.4301 → -0.5111),符合风控分箱预期。IV=0.1055,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 3 箱((0.9161500036716461, inf])为坏率最高区间:坏率高达 13.07%,可作为拒绝或重点风控策略的阈值参考

max_od_days — 2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 12.5]359353322427117.54%0.1000
2(12.5, inf]2145170643920.47%-1.0485
2026-10-30T19:04:19.455660 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(7.54% → 20.47%),WOE 单调下降(0.1000 → -1.0485),符合风控分箱预期。IV=0.1040,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((12.5, inf])为坏率最高区间:坏率高达 20.47%,可作为拒绝或重点风控策略的阈值参考

cre_l90_used_limit_amt_min — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 748.7099914550781]13694129347605.55%0.4288
2(748.7099914550781, 8516.22998046875]201721835418189.01%-0.0934
3(8516.22998046875, inf]4199362757213.62%-0.5585
2026-10-30T19:04:19.568706 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(5.55% → 13.62%),WOE 单调下降(0.4288 → -0.5585),符合风控分箱预期。IV=0.1036,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 3 箱((8516.22998046875, inf])为坏率最高区间:坏率高达 13.62%,可作为拒绝或重点风控策略的阈值参考

cre_now_l30_used_amt_d_min — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.0000500082969666]312112827329389.41%-0.0980
2(1.0000500082969666, 2.482349991798401]363535041313.60%0.9242
3(2.482349991798401, inf]10661043232.16%1.4521
2026-10-30T19:04:19.741015 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(9.41% → 2.16%),WOE 单调上升(-0.0980 → 1.4521),符合风控分箱预期。IV=0.1036,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, 1.0000500082969666])为坏率最高区间:坏率高达 9.41%,可作为拒绝或重点风控策略的阈值参考

available_limit_amt — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.03499999921768904]1238510918146711.85%-0.3987
2(0.03499999921768904, 27316.265625]237712218315886.68%0.2309
3(27316.265625, inf]19241829954.94%0.5517
2026-10-30T19:04:19.858232 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(11.85% → 4.94%),WOE 单调上升(-0.3987 → 0.5517),符合风控分箱预期。IV=0.1036,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, 0.03499999921768904])为坏率最高区间:坏率高达 11.85%,可作为拒绝或重点风控策略的阈值参考

setl_prcp_ratio — 2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.035099999979138374]832579603654.38%0.6763
2(0.035099999979138374, inf]297552697027859.36%-0.1355
2026-10-30T19:04:19.979364 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(4.38% → 9.36%),WOE 单调下降(0.6763 → -0.1355),符合风控分箱预期。IV=0.0909,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((0.035099999979138374, inf])为坏率最高区间:坏率高达 9.36%,可作为拒绝或重点风控策略的阈值参考

loan_os_prcp_credit_amount_m6 — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 48.94499969482422]12564118756895.48%0.4412
2(48.94499969482422, 107.59499740600586]187351707416618.87%-0.0756
3(107.59499740600586, inf]6761596279911.82%-0.3959
2026-10-30T19:04:20.087994 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(5.48% → 11.82%),WOE 单调下降(0.4412 → -0.3959),符合风控分箱预期。IV=0.0893,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((107.59499740600586, inf])为坏率最高区间:坏率高达 11.82%,可作为拒绝或重点风控策略的阈值参考

cre_now_l60_used_amt_d_min — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.000950038433075]279432520527389.80%-0.1106
2(1.000950038433075, 1.4549500346183777]235622351215.14%0.5858
3(1.4549500346183777, inf]388637151714.40%0.7481
2026-10-30T19:04:20.196286 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(9.80% → 4.40%),WOE 单调上升(-0.1106 → 0.7481),符合风控分箱预期。IV=0.0761,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 1 箱((-inf, 1.000950038433075])为坏率最高区间:坏率高达 9.80%,可作为拒绝或重点风控策略的阈值参考

loan_os_prcp_credit_amount_m12 — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 44.21000099182129]919787294685.09%0.5202
2(44.21000099182129, 95.76499938964844]210881923118578.81%-0.0682
3(95.76499938964844, inf]7775695182410.60%-0.2732
2026-10-30T19:04:20.310178 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(5.09% → 10.60%),WOE 单调下降(0.5202 → -0.2732),符合风控分箱预期。IV=0.0725,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((95.76499938964844, inf])为坏率最高区间:坏率高达 10.60%,可作为拒绝或重点风控策略的阈值参考

loan_os_prcp_gt0_m3_num_pc — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 58.334999084472656]13791341382.76%1.1579
2(58.334999084472656, 68.16500091552734]19751911643.24%0.9908
3(68.16500091552734, inf]347063165930478.78%-0.0649
2026-10-30T19:04:20.427497 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(2.76% → 8.78%),WOE 单调下降(1.1579 → -0.0649),符合风控分箱预期。IV=0.0685,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 1 箱 WOE<0(风险高于平均)

第 3 箱((68.16500091552734, inf])为坏率最高区间:坏率高达 8.78%,可作为拒绝或重点风控策略的阈值参考

loan_os_prcp_credit_amount_m3 — 2 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 100.47500228881836]311082884222667.28%0.1381
2(100.47500228881836, inf]6952606988312.70%-0.4781
2026-10-30T19:04:20.537112 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(7.28% → 12.70%),WOE 单调下降(0.1381 → -0.4781),符合风控分箱预期。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 1 箱 WOE<0(风险高于平均)

第 2 箱((100.47500228881836, inf])为坏率最高区间:坏率高达 12.70%,可作为拒绝或重点风控策略的阈值参考

useratemore50maxcontimonsm12 — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 2.5]836579244415.27%0.4829
2(2.5, 4.5]589354014928.35%-0.0099
3(4.5, inf]238022158622169.31%-0.1294
2026-10-30T19:04:20.651170 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(5.27% → 9.31%),WOE 单调下降(0.4829 → -0.1294),符合风控分箱预期。IV=0.0530,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((4.5, inf])为坏率最高区间:坏率高达 9.31%,可作为拒绝或重点风控策略的阈值参考

cre_now_l60_used_amt_m_max — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 83.30500030517578]514849042444.74%0.5951
2(83.30500030517578, 2003.010009765625]240882208320058.32%-0.0063
3(2003.010009765625, inf]8829792890110.21%-0.2309
2026-10-30T19:04:20.764533 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(4.74% → 10.21%),WOE 单调下降(0.5951 → -0.2309),符合风控分箱预期。IV=0.0511,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((2003.010009765625, inf])为坏率最高区间:坏率高达 10.21%,可作为拒绝或重点风控策略的阈值参考

ps_int_amt_sum — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1891.7449951171875]239582223217267.20%0.1498
2(1891.7449951171875, 10533.93994140625]113161027610409.19%-0.1154
3(10533.93994140625, inf]2806242238413.69%-0.5642
2026-10-30T19:04:20.881010 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(7.20% → 13.69%),WOE 单调下降(0.1498 → -0.5642),符合风控分箱预期。IV=0.0471,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((10533.93994140625, inf])为坏率最高区间:坏率高达 13.69%,可作为拒绝或重点风控策略的阈值参考

setl_rem_prcp_paym_m3_num — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.5]320942924028548.89%-0.0789
2(0.5, 1.5]415939522074.98%0.5435
3(1.5, inf]18071719884.87%0.5664
2026-10-30T19:04:21.061348 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(8.89% → 4.87%),WOE 单调上升(-0.0789 → 0.5664),符合风控分箱预期。IV=0.0433,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 1 箱((-inf, 0.5])为坏率最高区间:坏率高达 8.89%,可作为拒绝或重点风控策略的阈值参考

useratemore70monthsubcurmonth — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 4.5]254292310223279.15%-0.1104
2(4.5, 9.5]603755644737.83%0.0593
3(9.5, inf]659462453495.29%0.4787
2026-10-30T19:04:21.184004 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(9.15% → 5.29%),WOE 单调上升(-0.1104 → 0.4787),符合风控分箱预期。IV=0.0416,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 1 箱((-inf, 4.5])为坏率最高区间:坏率高达 9.15%,可作为拒绝或重点风控策略的阈值参考

max_loan_os_prcp_m12 — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1747.2849731445312]410138602415.88%0.3679
2(1747.2849731445312, 20848.58984375]289052660622997.95%0.0429
3(20848.58984375, inf]5054444560912.05%-0.4180
2026-10-30T19:04:21.303707 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(5.88% → 12.05%),WOE 单调下降(0.3679 → -0.4180),符合风控分箱预期。IV=0.0415,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 1 箱 WOE<0(风险高于平均)

第 3 箱((20848.58984375, inf])为坏率最高区间:坏率高达 12.05%,可作为拒绝或重点风控策略的阈值参考

avg_tnr — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 6.5]876882545145.86%0.3703
2(6.5, 10.5]126711164510268.10%0.0233
3(10.5, inf]166411503116109.67%-0.1721
2026-10-30T19:04:21.420047 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(5.86% → 9.67%),WOE 单调下降(0.3703 → -0.1721),符合风控分箱预期。IV=0.0412,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 1 箱 WOE<0(风险高于平均)

第 3 箱((10.5, inf])为坏率最高区间:坏率高达 9.67%,可作为拒绝或重点风控策略的阈值参考

useratemore70_m6_num — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]15252142619916.50%0.2609
2(1.5, 5.5]178801623616449.19%-0.1156
3(5.5, inf]4928441451410.43%-0.2554
2026-10-30T19:04:21.533592 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(6.50% → 10.43%),WOE 单调下降(0.2609 → -0.2554),符合风控分箱预期。IV=0.0405,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((5.5, inf])为坏率最高区间:坏率高达 10.43%,可作为拒绝或重点风控策略的阈值参考

suc_apprv_num_m6 — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 1.5]220111992520869.48%-0.1492
2(1.5, 3.5]781272405727.32%0.1323
3(3.5, inf]825777654925.96%0.3530
2026-10-30T19:04:21.646542 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(9.48% → 5.96%),WOE 单调上升(-0.1492 → 0.3530),符合风控分箱预期。IV=0.0404,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 1 箱((-inf, 1.5])为坏率最高区间:坏率高达 9.48%,可作为拒绝或重点风控策略的阈值参考

shd_pre_repay_amt_rate — 3 箱
分组号分栏TotalGoodBadBad RateWOE
1(-inf, 0.6238499879837036]12637118148236.51%0.2584
2(0.6238499879837036, 0.7506499886512756]863179446877.96%0.0421
3(0.7506499886512756, inf]167911515216399.76%-0.1816
2026-10-30T19:04:21.757815 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(6.51% → 9.76%),WOE 单调下降(0.2584 → -0.1816),符合风控分箱预期。IV=0.0360,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 1 箱 WOE<0(风险高于平均)

第 3 箱((0.7506499886512756, inf])为坏率最高区间:坏率高达 9.76%,可作为拒绝或重点风控策略的阈值参考

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)。当前流程细/粗分箱由同一自动分箱算法直接产出。

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0.023604024417191158
缺失率上限0.8
集中度上限0.9
相关性上限(皮尔逊)0.7
PSI 上限0.25
重要性 Top-K28
黑名单无
白名单无

候选变量 125,成功分箱 125,入模 28

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
ps_perd_no—N0.1423PASSPASSPASS—0.3194PSI>0.25
due_day_my—Y19.9031PASSPASSPASSPASS0.0005
age—N0.0040PASSPASSFAIL——IV<0.023604024417191158
max_int_rat—N0.0079PASSPASSFAIL——IV<0.023604024417191158
min_int_rat—N0.0016PASSPASSFAIL——IV<0.023604024417191158
avg_tnr—Y0.0412PASSPASSPASSPASS0.0223
min_tnr—N0.0215PASSPASSFAIL——IV<0.023604024417191158
sum_apply_amt—N0.0030PASSPASSFAIL——IV<0.023604024417191158
min_apply_amt—N0.0268PASSPASSPASSPASS0.0261重要性排名超出Top-28
min_apprv_amt—N0.0041PASSPASSFAIL——IV<0.023604024417191158
apprv_amt_day—Y0.1475PASSPASSPASSPASS0.1320
end_loan_to_rep_loan_freq—N0.0086PASSPASSFAIL——IV<0.023604024417191158
end_loan_to_cur_freq—Y0.1126PASSPASSPASSPASS0.2103
f_rep_loan_to_f_apply_dt_freq—N0.0013PASSPASSFAIL——IV<0.023604024417191158
ps_prcp_amt_mon—N0.0232PASSPASSFAIL——IV<0.023604024417191158
ps_int_amt_mon—N0.0837PASSPASSPASS—0.2919PSI>0.25
ps_int_amt_sum—Y0.0471PASSPASSPASSPASS0.0038
ps_prcp_amt_avg—N0.0058PASSPASSFAIL——IV<0.023604024417191158
ps_int_amt_avg—N0.0273PASSPASSPASSPASS0.0171重要性排名超出Top-28
setl_prcp_mon_ratio—N0.0125PASSPASSFAIL——IV<0.023604024417191158
acc_ps_perd_no—N0.0875PASSPASSPASS—0.4557PSI>0.25
max_od_days—Y0.1040PASSPASSPASSPASS0.1197
m_s_ps_instm_amt—N0.0371PASSPASSPASS—0.2761PSI>0.25
f_ps_od_days_max—Y0.1798PASSPASSPASSPASS0.1412
s_ps_od_days_max—N0.0059PASSPASSFAIL——IV<0.023604024417191158
t_ps_od_days_max—N0.0245PASSPASSPASSPASS0.0452重要性排名超出Top-28
ovd_days_d3_num_m1—N0.1789PASSFAIL———集中度>0.9
orig_prcp_avg_m3—N0.1156PASSPASSPASS—0.2544PSI>0.25
orig_prcp_avg_m12—N0.0348PASSPASSPASSPASS0.0350重要性排名超出Top-28
suc_apply_trench_num—N0.0091PASSFAIL———集中度>0.9
suc_apply_top_pc_code_num—N0.0022PASSPASSFAIL——IV<0.023604024417191158
suc_apprv_num_m1—Y0.1594PASSPASSPASSPASS0.1386
apprv_num_m1—Y0.1224PASSPASSPASSPASS0.1073
suc_apply_num_m3—N0.0000PASSFAIL———集中度>0.9
apprv_num_m3—N0.0951PASSPASSPASSFAIL0.2387与 apprv_num_m1 相关>0.7
suc_apprv_num_m6—Y0.0404PASSPASSPASSPASS0.0130
apply_num_m12—N0.0007PASSPASSFAIL——IV<0.023604024417191158
last_apply_od_num_m12—N0.0067PASSFAIL———集中度>0.9
last_apply_freq—N0.0169PASSPASSFAIL——IV<0.023604024417191158
dec_loan_num—N0.0028PASSFAIL———集中度>0.9
bh_cur_od_num—N0.0064PASSPASSFAIL——IV<0.023604024417191158
hj_cur_od_num—N0.0009PASSFAIL———集中度>0.9
setl_prcp_ratio—Y0.0909PASSPASSPASSPASS0.2377
last_loan_setl_tnr—N0.1487PASSPASSPASS—0.2771PSI>0.25
last_od_num_m6—N0.1092PASSPASSPASS—0.5913PSI>0.25
last_od_to_cur_freq—N0.2002PASSPASSPASS—0.9177PSI>0.25
last_clear_num_m1—N0.0203PASSPASSFAIL——IV<0.023604024417191158
last_clear_num_m3—N0.0302PASSPASSPASSFAIL0.0129与 suc_apprv_num_m6 相关>0.7
grade_limit_amount—N0.0072PASSPASSFAIL——IV<0.023604024417191158
exposure—N0.0033PASSPASSFAIL——IV<0.023604024417191158
available_limit_amt—Y0.1036PASSPASSPASSPASS0.0068
account_age—N0.0148PASSPASSFAIL——IV<0.023604024417191158
account_age_loan_day—N0.0048PASSPASSFAIL——IV<0.023604024417191158
max_od_due—N0.0638PASSPASSPASS—0.4345PSI>0.25
odgt0_m12_mob_pc—N0.0402PASSPASSPASS—0.8830PSI>0.25
odgt0_m3_num—N0.0227PASSPASSFAIL——IV<0.023604024417191158
odgt0_m12_num—N0.0425PASSPASSPASS—0.8266PSI>0.25
odgt0_m12_subcurmonth—N0.0635PASSPASSPASS—1.2145PSI>0.25
tothismonthodcontiaddnumm12—N0.0187PASSPASSFAIL——IV<0.023604024417191158
tothismonthodcontireducenumm12—N0.0209PASSPASSFAIL——IV<0.023604024417191158
od_amt_m6_loan_os_prcp_m6_pc—N0.0804PASSFAIL———集中度>0.9
od_amt_m12_loan_os_prcp_m12_pc—N0.0658PASSFAIL———集中度>0.9
avg_od_amt_m6_orig_prcp_pc—N0.0815PASSFAIL———集中度>0.9
max_loan_os_prcp_m12—Y0.0415PASSPASSPASSPASS0.0539
loan_os_prcp_gt0_m3_num_pc—Y0.0685PASSPASSPASSPASS0.0269
loan_os_prcp_gt0_m12_num_pc—N0.0299PASSPASSPASSPASS0.0319重要性排名超出Top-28
repay_amount_m3_loan_os_prcp_pc—N0.0283PASSPASSPASS—0.3012PSI>0.25
repay_amount_m6_loan_os_prcp_pc—N0.0070PASSPASSFAIL——IV<0.023604024417191158
repay_amount_all_shd_amt_pc—N0.0033PASSPASSFAIL——IV<0.023604024417191158
repay_amount_m6_all_shd_amt_pc—N0.0344PASSPASSPASSPASS0.0382重要性排名超出Top-28
setl_rem_prcp_paym_last_month_lo—N0.0240PASSPASSPASSPASS0.0025重要性排名超出Top-28
setl_rem_prcp_paym_m12_loan_os_p—N0.0039PASSPASSFAIL——IV<0.023604024417191158
setl_rem_prcp_paym_m3_num—Y0.0433PASSPASSPASSPASS0.0165
setl_rem_prcp_paym_m12_num—N0.0057PASSPASSFAIL——IV<0.023604024417191158
loan_os_prcp_credit_amount_m3—Y0.0657PASSPASSPASSPASS0.0000
loan_os_prcp_credit_amount_m6—Y0.0893PASSPASSPASSPASS0.0360
loan_os_prcp_credit_amount_m12—Y0.0725PASSPASSPASSPASS0.0684
useratemore50maxcontimonsm12—Y0.0530PASSPASSPASSPASS0.2260
useratemore70_m6_num—Y0.0405PASSPASSPASSPASS0.0705
useratemore70monthsubcurmonth—Y0.0416PASSPASSPASSPASS0.0847
useratemore90_m3_num—N0.0302PASSPASSPASSPASS0.0012重要性排名超出Top-28
useratemore90maxcontimonsm12—N0.0335PASSPASSPASSPASS0.0908重要性排名超出Top-28
shd_max_dpd—N0.0690PASSPASSPASS—0.3133PSI>0.25
shd_min_dpd—N0.0050PASSPASSFAIL——IV<0.023604024417191158
shd_pre_repay_cnt—N0.0175PASSPASSFAIL——IV<0.023604024417191158
shd_perd_max—N0.0281PASSFAIL———集中度>0.9
shd_instm_amt_sum—N0.0258PASSPASS———唯一率过高(ID列)
shd_l30_max_dpd—N0.0827PASSPASSPASS—1.2070PSI>0.25
shd_l60_min_dpd—N0.0190PASSPASSFAIL——IV<0.023604024417191158
shd_l90_ps_due_cnt—N0.0275PASSPASSPASS—0.3391PSI>0.25
shd_l180_max_dpd—N0.0043PASSPASSFAIL——IV<0.023604024417191158
shd_l180_instm_amt_sum—N0.0217PASSPASS———唯一率过高(ID列)
shd_remain_actual_cnt—N0.0129PASSPASSFAIL——IV<0.023604024417191158
shd_pre_repay_cnt_rate—N0.0124PASSPASSFAIL——IV<0.023604024417191158
shd_pre_repay_amt_rate—Y0.0360PASSPASSPASSPASS0.0366
shd_pre_repay_30d_amt_rate—N0.0009PASSPASSFAIL——IV<0.023604024417191158
shd_pre_repay_due_rate—N0.0672PASSPASSPASS—0.2564PSI>0.25
shd_l30_pre_repay_due_rate—N0.0506PASSPASSPASS—0.8903PSI>0.25
shd_remain_actual_rate—N0.0123PASSPASSFAIL——IV<0.023604024417191158
cre_usedrate_u30_daydelta_min—N0.0116PASSPASSFAIL——IV<0.023604024417191158
cre_usedrate_u30_daydelta_max—N0.0468PASSPASSPASS—0.2756PSI>0.25
cre_l30_limit_left_amt_min—N0.0459PASSPASSPASSFAIL0.0010与 available_limit_amt 相关>0.7
cre_l60_used_limit_rate_max—N0.0614PASSPASSPASSFAIL0.0130与 cre_l60_used_limit_rate_min 相关>0.7
cre_l60_used_limit_rate_min—Y0.1055PASSPASSPASSPASS0.0291
cre_l90_used_limit_amt_min—Y0.1036PASSPASSPASSPASS0.0288
cre_now_p1m_used_rate_d—N0.0173PASSPASSFAIL——IV<0.023604024417191158
cre_now_p3m_used_rate_d—N0.0161PASSPASSFAIL——IV<0.023604024417191158
cre_now_p1m_used_rate_m—N0.0308PASSPASSPASSPASS0.0095重要性排名超出Top-28
cre_now_p2m_used_rate_m—N0.0190PASSPASSFAIL——IV<0.023604024417191158
cre_now_l30_used_amt_d_max—N0.0151PASSPASSFAIL——IV<0.023604024417191158
cre_now_l30_used_amt_d_min—Y0.1036PASSPASSPASSPASS0.0357
cre_now_l30_limit_left_d_max—N0.0013PASSPASSFAIL——IV<0.023604024417191158
cre_now_l30_limit_left_d_min—N0.0262PASSPASSPASSPASS0.0026重要性排名超出Top-28
cre_now_l60_used_amt_d_min—Y0.0761PASSPASSPASSPASS0.0585
cre_now_l60_limit_left_d_max—N0.0088PASSPASSFAIL——IV<0.023604024417191158
cre_now_l60_limit_left_d_min—N0.0217PASSPASSFAIL——IV<0.023604024417191158
cre_now_l60_used_amt_m_max—Y0.0511PASSPASSPASSPASS0.2386
cre_now_l60_used_amt_m_min—Y0.1300PASSPASSPASSPASS0.0710
cre_now_l60_usedrate_m_max—N0.0211PASSPASSFAIL——IV<0.023604024417191158
cre_now_l90_limit_left_d_min—N0.0204PASSPASSFAIL——IV<0.023604024417191158
cre_l30_l90_used_amt_d_max—N0.0705PASSPASSPASS—0.3024PSI>0.25
cre_l30_l90_used_amt_m_max—N0.0833PASSPASSPASS—0.3006PSI>0.25
cre_l30_l90_used_amt_m_min—N0.0009PASSPASSFAIL——IV<0.023604024417191158
cre_l30_l90_usedrate_m_max—N0.0208PASSPASSFAIL——IV<0.023604024417191158
cre_l30_l90_usedrate_m_min—N0.0034PASSPASSFAIL——IV<0.023604024417191158

06 回归结果

硬门槛:P≥0.1 / VIF>4.0 迭代剔除,共 23 个变量被剔除(24 轮):useratemore70_m6_num、useratemore70monthsubcurmonth、shd_pre_repay_amt_rate、max_loan_os_prcp_m12、suc_apprv_num_m6、ps_int_amt_sum、avg_tnr、useratemore50maxcontimonsm12、setl_rem_prcp_paym_m3_num、cre_now_l60_used_amt_m_max、cre_now_l60_used_amt_d_min、loan_os_prcp_credit_amount_m3、loan_os_prcp_credit_amount_m12、setl_prcp_ratio、apprv_amt_day、loan_os_prcp_credit_amount_m6、loan_os_prcp_gt0_m3_num_pc、max_od_days、end_loan_to_cur_freq、apprv_num_m1、cre_l60_used_limit_rate_min、available_limit_amt、cre_l90_used_limit_amt_min。

ParameterLabelEstimate(无惩罚)Standard ErrorWald Chi-SquarePr > ChiSqStandardized Estimate
InterceptIntercept-0.132713.52130.000.9922—
WOE_due_day_mydue_day_my-1.34741.54380.760.3828-7.3876
WOE_f_ps_od_days_maxf_ps_od_days_max-0.01394.72390.000.9976-0.0061
WOE_suc_apprv_num_m1suc_apprv_num_m1-0.01348.60460.000.9988-0.0065
WOE_cre_now_l60_used_amt_m_mincre_now_l60_used_amt_m_min-0.01047.95230.000.9990-0.0042
WOE_cre_now_l30_used_amt_d_mincre_now_l30_used_amt_d_min-0.01469.95320.000.9988-0.0061

注:Estimate 列为无惩罚(精确推断)拟合;生产系数保持 L2 正则(model_results.json / score.py 口径),两套估计并列展示。标准误基于无惩罚似然观测信息矩阵。

07 VIF

VIF

ParameterVIF
due_day_my1.02080
f_ps_od_days_max1.01872
suc_apprv_num_m14.08689
cre_now_l60_used_amt_m_min2.41673
cre_now_l30_used_amt_d_min4.08372

相关系数(WOE 转换后,开发样本)

due_day_myf_ps_od_days_maxsuc_apprv_num_m1cre_now_l60_used_amt_m_mincre_now_l30_used_amt_d_min
due_day_my1.0000.1130.0910.0890.087
f_ps_od_days_max0.1131.0000.0770.0820.071
suc_apprv_num_m10.0910.0771.0000.7370.854
cre_now_l60_used_amt_m_min0.0890.0820.7371.0000.737
cre_now_l30_used_amt_d_min0.0870.0710.8540.7371.000

08 最终评分卡

标准分600.0
标准ODD20.0
PDO20.0
变量分栏WOECoefficientScorecard Points
due_day_my(-inf, 30.5]8.7486-1.3311440
(30.5, inf]-11.1544-1.3311-325
f_ps_od_days_max(-inf, 0.5]-0.3309-0.0138103
(0.5, inf]0.5516-0.0138104
suc_apprv_num_m1(-inf, 0.5]-0.1580-0.0133103
(0.5, 1.5]0.7478-0.0133104
(1.5, inf]1.3409-0.0133104
cre_now_l60_used_amt_m_min(-inf, 1.7799999713897705]-0.1775-0.0103103
(1.7799999713897705, inf]0.7402-0.0103104
cre_now_l30_used_amt_d_min(-inf, 1.0000500082969666]-0.0980-0.0147103
(1.0000500082969666, 2.482349991798401]0.9242-0.0147104
(2.482349991798401, inf]1.4521-0.0147104

09 模型表现

模型区分度评估(按样本)

模型样本KSAUCGINI
LR + WOE 评分卡开发样本1.00001.00001.0000
LR + WOE 评分卡测试样本1.00001.00001.0000
LR + WOE 评分卡训练+测试样本1.00001.00001.0000
LR + WOE 评分卡跨时间验证样本1.00001.00001.0000

LR + WOE 评分卡 — 4 样本评估表(score_cut 粒度)

开发样本(KS 0.0424,AUC 0.5135)

cutscntbadgoodbad_ratepopulation_pctcum_bad_ratecum_good_rate
[89, 853)1644031511328919.17%43.16%100.00%38.04%
[854, 854)127900127900.00%33.58%100.00%74.65%
[854, 854)1689016890.00%4.43%100.00%79.48%
[854, 855)3394033940.00%8.91%100.00%89.19%
[855, 855)3775037750.00%9.91%100.00%100.00%
2026-10-30T19:04:21.873638 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-10-30T19:04:21.977290 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

测试样本(KS 0.0370,AUC 0.5209)

cutscntbadgoodbad_ratepopulation_pctcum_bad_ratecum_good_rate
[89, 853)109812100888119.12%43.25%100.00%38.13%
[854, 854)8525085250.00%33.57%100.00%74.73%
[854, 854)1105011050.00%4.35%100.00%79.48%
[854, 855)2279022790.00%8.98%100.00%89.26%
[855, 855)2501025010.00%9.85%100.00%100.00%
2026-10-30T19:04:22.085445 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-10-30T19:04:22.189808 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

训练+测试样本(KS 0.0390,AUC 0.5164)

cutscntbadgoodbad_ratepopulation_pctcum_bad_ratecum_good_rate
[89, 853)2742152512217019.15%43.20%100.00%38.07%
[854, 854)213150213150.00%33.58%100.00%74.68%
[854, 854)2794027940.00%4.40%100.00%79.48%
[854, 855)5673056730.00%8.94%100.00%89.22%
[855, 855)6276062760.00%9.89%100.00%100.00%
2026-10-30T19:04:22.357705 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-10-30T19:04:22.462049 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

跨时间验证样本(KS 0.0315,AUC 0.5169)

cutscntbadgoodbad_ratepopulation_pctcum_bad_ratecum_good_rate
[89, 853)1402024321158817.35%40.37%100.00%35.88%
[854, 854)9535095350.00%27.45%100.00%65.40%
[854, 854)2021020210.00%5.82%100.00%71.66%
[854, 854)2782027820.00%8.01%100.00%80.27%
[854, 855)3807038070.00%10.96%100.00%92.06%
[855, 855)2565025650.00%7.39%100.00%100.00%
2026-10-30T19:04:22.577773 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-10-30T19:04:22.689206 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

跨时间验证样本结论

风险高度集中在低分尾端——最低 20% 样本集中了全量 100.0% 的坏客户,策略切分空间很大。

① 样本与分箱结构:共 34,730 样本 / 2,432 坏样本,整体坏率 7.00%,好坏比约 13.3:1;6 档等频分数分栏,每箱约 10%,个别箱占比偏薄/偏厚(5.8%~40.4%)。

② 排序性与单调性:逐箱 Bad Rate 17.35% → … → 0.00%(低分 → 高分),无倒挂。

③ 区分度与坏客户捕获:最低 20% 样本捕获全量 100.0% 的坏客户,最低 50% 捕获 100.0%;最高 20% 样本仅含 0.0% 的坏客户,其中风险最高一箱单独占 100.0%。

(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 表按分数等频分 10 档(低分 → 高分):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。

10 KS和GINI

LR + WOE 评分卡

样本KSGINIAUC
开发样本1.00001.00001.0000
测试样本1.00001.00001.0000
训练+测试样本1.00001.00001.0000
跨时间验证样本1.00001.00001.0000

开发样本

2026-10-30T19:04:22.769102 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-10-30T19:04:22.837636 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

测试样本

2026-10-30T19:04:22.905471 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-10-30T19:04:22.972744 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

训练+测试样本

2026-10-30T19:04:23.040756 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-10-30T19:04:23.108595 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

跨时间验证样本

2026-10-30T19:04:23.176219 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-10-30T19:04:23.245043 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oot验证集训练集与psi验证集
LR + WOE 评分卡0.00000.0467—0.0497

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,0.0]9.91%12.28%0.00510.0051
(0.0,0.0]8.91%14.08%0.02360.0287
(0.0,0.0]4.43%5.82%0.00380.0325
(0.0,0.0]0.00%0.00%0.00000.0325
(0.0,0.0]0.00%0.00%0.00000.0325
(0.0,0.0]33.58%27.45%0.01230.0448
(0.0,0.0]0.00%0.00%0.00000.0448
(0.0,0.0]0.00%0.00%0.00000.0448
(0.0,0.0]0.00%0.00%0.00000.0448
(0.0,inf]43.16%40.37%0.00190.0467

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,0.0]9.91%10.57%0.00040.0004
(0.0,0.0]8.91%15.19%0.03350.0340
(0.0,0.0]4.43%5.91%0.00420.0382
(0.0,0.0]0.00%0.00%0.00000.0382
(0.0,0.0]0.00%0.00%0.00000.0382
(0.0,0.0]33.58%28.24%0.00930.0475
(0.0,0.0]0.00%0.00%0.00000.0475
(0.0,0.0]0.00%0.00%0.00000.0475
(0.0,0.0]0.00%0.00%0.00000.0475
(0.0,inf]43.16%40.08%0.00230.0497
2026-10-30T19:04:23.321937 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

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, inf]37759.91%25019.85%62769.89%426412.28%184710.57%
(0.0, 0.0]33948.91%22798.98%56738.94%489014.08%265515.19%
(0.0, 0.0]16894.43%11054.35%27944.40%20215.82%10335.91%
(0.0, 0.0]00.00%00.00%00.00%00.00%00.00%
(0.0, 0.0]00.00%00.00%00.00%00.00%00.00%
(0.0, 0.0]1279033.58%852533.57%2131533.58%953527.45%493428.24%
(0.0, 0.0]00.00%00.00%00.00%00.00%00.00%
(0.0, 0.0]00.00%00.00%00.00%00.00%00.00%
(0.0, 0.0]00.00%00.00%00.00%00.00%00.00%
(0.0, inf]1644043.16%1098143.25%2742143.20%1402040.37%700440.08%
sum38088100.00%25391100.00%63479100.00%34730100.00%17473100.00%

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
cre_now_l60_used_amt_m_max0.2386关注
setl_prcp_ratio0.2377关注
useratemore50maxcontimonsm120.2260关注
end_loan_to_cur_freq0.2103关注
f_ps_od_days_max0.1412关注
suc_apprv_num_m10.1386关注
apprv_amt_day0.1320关注
max_od_days0.1197关注
apprv_num_m10.1073关注
useratemore70monthsubcurmonth0.0847稳定
cre_now_l60_used_amt_m_min0.0710稳定
useratemore70_m6_num0.0705稳定
loan_os_prcp_credit_amount_m120.0684稳定
cre_now_l60_used_amt_d_min0.0585稳定
max_loan_os_prcp_m120.0539稳定
shd_pre_repay_amt_rate0.0366稳定
loan_os_prcp_credit_amount_m60.0360稳定
cre_now_l30_used_amt_d_min0.0357稳定
cre_l60_used_limit_rate_min0.0291稳定
cre_l90_used_limit_amt_min0.0288稳定
loan_os_prcp_gt0_m3_num_pc0.0269稳定
avg_tnr0.0223稳定
setl_rem_prcp_paym_m3_num0.0165稳定
suc_apprv_num_m60.0130稳定
available_limit_amt0.0068稳定
ps_int_amt_sum0.0038稳定
due_day_my0.0005稳定
loan_os_prcp_credit_amount_m30.0000稳定
due_day_my — PSI 0.0005
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 30.5]91.73%91.12%-0.60%0.0000
(30.5, inf]8.27%8.88%0.60%0.0004
f_ps_od_days_max — PSI 0.1412
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.5]53.63%71.67%18.04%0.0523
(0.5, inf]46.37%28.33%-18.04%0.0889
suc_apprv_num_m1 — PSI 0.1386
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.5]79.23%62.50%-16.73%0.0397
(0.5, 1.5]12.57%22.98%10.41%0.0628
(1.5, inf]8.20%14.52%6.32%0.0361
apprv_amt_day — PSI 0.1320
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 11.0]79.68%63.48%-16.21%0.0369
(11.0, inf]20.32%36.52%16.21%0.0951
cre_now_l60_used_amt_m_min — PSI 0.0710
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 1.7799999713897705]74.09%61.70%-12.38%0.0227
(1.7799999713897705, inf]25.91%38.30%12.38%0.0484
apprv_num_m1 — PSI 0.1073
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.5]73.93%58.74%-15.19%0.0349
(0.5, 1.5]14.05%24.06%10.01%0.0539
(1.5, inf]12.02%17.20%5.18%0.0185
end_loan_to_cur_freq — PSI 0.2103
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, -68.5]62.07%39.34%-22.73%0.1036
(-68.5, inf]37.93%60.66%22.73%0.1067
cre_l60_used_limit_rate_min — PSI 0.0291
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.2787500023841858]38.30%43.78%5.48%0.0073
(0.2787500023841858, 0.9161500036716461]49.65%41.27%-8.38%0.0155
(0.9161500036716461, inf]12.06%14.96%2.90%0.0063
max_od_days — PSI 0.1197
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 12.5]94.37%99.44%5.07%0.0027
(12.5, inf]5.63%0.56%-5.07%0.1170
cre_l90_used_limit_amt_min — PSI 0.0288
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 748.7099914550781]35.98%38.67%2.70%0.0019
(748.7099914550781, 8516.22998046875]52.99%45.70%-7.30%0.0108
(8516.22998046875, inf]11.03%15.63%4.60%0.0160
cre_now_l30_used_amt_d_min — PSI 0.0357
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 1.0000500082969666]86.91%80.18%-6.73%0.0054
(1.0000500082969666, 2.482349991798401]10.12%14.29%4.17%0.0144
(2.482349991798401, inf]2.97%5.52%2.56%0.0159
available_limit_amt — PSI 0.0068
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.03499999921768904]32.52%32.39%-0.14%0.0000
(0.03499999921768904, 27316.265625]62.42%60.62%-1.80%0.0005
(27316.265625, inf]5.05%6.99%1.94%0.0063
setl_prcp_ratio — PSI 0.2377
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.035099999979138374]21.86%44.47%22.61%0.1605
(0.035099999979138374, inf]78.14%55.53%-22.61%0.0772
loan_os_prcp_credit_amount_m6 — PSI 0.0360
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 48.94499969482422]33.01%41.65%8.64%0.0201
(48.94499969482422, 107.59499740600586]49.22%40.80%-8.43%0.0158
(107.59499740600586, inf]17.76%17.55%-0.21%0.0000
cre_now_l60_used_amt_d_min — PSI 0.0585
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 1.000950038433075]81.74%71.70%-10.04%0.0132
(1.000950038433075, 1.4549500346183777]6.89%9.65%2.76%0.0093
(1.4549500346183777, inf]11.37%18.65%7.28%0.0360
loan_os_prcp_credit_amount_m12 — PSI 0.0684
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 44.21000099182129]24.16%34.77%10.61%0.0386
(44.21000099182129, 95.76499938964844]55.41%43.47%-11.94%0.0290
(95.76499938964844, inf]20.43%21.76%1.33%0.0008
loan_os_prcp_gt0_m3_num_pc — PSI 0.0269
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 58.334999084472656]3.62%6.46%2.84%0.0164
(58.334999084472656, 68.16500091552734]5.19%7.37%2.18%0.0076
(68.16500091552734, inf]91.19%86.17%-5.02%0.0028
loan_os_prcp_credit_amount_m3 — PSI 0.0000
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 100.47500228881836]81.73%81.67%-0.07%0.0000
(100.47500228881836, inf]18.27%18.33%0.07%0.0000
useratemore50maxcontimonsm12 — PSI 0.2260
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 2.5]21.98%41.92%19.94%0.1287
(2.5, 4.5]15.48%17.49%2.00%0.0024
(4.5, inf]62.54%40.59%-21.94%0.0948
cre_now_l60_used_amt_m_max — PSI 0.2386
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 83.30500030517578]13.52%32.92%19.40%0.1726
(83.30500030517578, 2003.010009765625]63.28%44.54%-18.75%0.0659
(2003.010009765625, inf]23.19%22.54%-0.65%0.0002
ps_int_amt_sum — PSI 0.0038
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 1891.7449951171875]62.91%65.64%2.73%0.0012
(1891.7449951171875, 10533.93994140625]29.72%26.96%-2.75%0.0027
(10533.93994140625, inf]7.37%7.39%0.03%0.0000
setl_rem_prcp_paym_m3_num — PSI 0.0165
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.5]84.32%79.42%-4.90%0.0029
(0.5, 1.5]10.93%14.01%3.08%0.0076
(1.5, inf]4.75%6.57%1.83%0.0059
useratemore70monthsubcurmonth — PSI 0.0847
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 4.5]66.81%59.30%-7.51%0.0090
(4.5, 9.5]15.86%11.51%-4.35%0.0139
(9.5, inf]17.33%29.18%11.86%0.0618
max_loan_os_prcp_m12 — PSI 0.0539
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 1747.2849731445312]10.78%16.33%5.56%0.0231
(1747.2849731445312, 20848.58984375]75.95%65.50%-10.45%0.0155
(20848.58984375, inf]13.28%18.17%4.89%0.0154
avg_tnr — PSI 0.0223
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 6.5]23.03%28.83%5.81%0.0131
(6.5, 10.5]33.27%27.97%-5.30%0.0092
(10.5, inf]43.70%43.19%-0.51%0.0001
useratemore70_m6_num — PSI 0.0705
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 1.5]40.07%52.77%12.70%0.0349
(1.5, 5.5]46.98%38.90%-8.08%0.0153
(5.5, inf]12.95%8.34%-4.61%0.0203
suc_apprv_num_m6 — PSI 0.0130
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 1.5]57.80%52.24%-5.56%0.0056
(1.5, 3.5]20.51%24.00%3.49%0.0055
(3.5, inf]21.68%23.76%2.07%0.0019
shd_pre_repay_amt_rate — PSI 0.0366
分栏开发基准比例PSI时点样本 比例变更贡献
(-inf, 0.6238499879837036]33.20%41.50%8.30%0.0185
(0.6238499879837036, 0.7506499886512756]22.68%16.98%-5.70%0.0165
(0.7506499886512756, inf]44.12%41.52%-2.60%0.0016

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。

due_day_my
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 30.5]3493791.73%3493700.00%—
2(30.5, inf]3151100.00%03151100.00%0.00
合计380883493731518.27%11.09
2026-10-30T19:04:23.419935 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(0.00% → 100.00%),WOE 单调下降(8.7486 → -11.1544),符合风控分箱预期。IV=19.9031,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((30.5, inf])为极高风险区间:坏率高达 100.00%,策略上应重点管控

f_ps_od_days_max
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.5]2042253.63%18144227811.15%7.96
2(0.5, inf]17658100.00%167868724.94%19.25
合计380803493031508.27%11.09
2026-10-30T19:04:23.522801 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(11.15% → 4.94%),WOE 单调上升(-0.3309 → 0.5516),符合风控分箱预期。IV=0.1798,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 1 箱((-inf, 0.5])为坏率最高区间:坏率高达 11.15%,可作为拒绝或重点风控策略的阈值参考

suc_apprv_num_m1
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.5]3016979.23%2728728829.55%9.47
2(0.5, 1.5]478791.80%45911964.09%23.42
3(1.5, inf]3124100.00%3052722.30%42.39
合计380803493031508.27%11.09
2026-10-30T19:04:23.693413 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(9.55% → 2.30%),WOE 单调上升(-0.1580 → 1.3409),符合风控分箱预期。IV=0.1594,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])为坏率最高区间:坏率高达 9.55%,可作为拒绝或重点风控策略的阈值参考

apprv_amt_day
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 11.0]3034479.68%2745228929.53%9.49
2(11.0, inf]7736100.00%74782583.34%28.98
合计380803493031508.27%11.09
2026-10-30T19:04:23.805065 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(9.53% → 3.34%),WOE 单调上升(-0.1555 → 0.9608),符合风控分箱预期。IV=0.1475,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 1 箱((-inf, 11.0])为坏率最高区间:坏率高达 9.53%,可作为拒绝或重点风控策略的阈值参考

cre_now_l60_used_amt_m_min
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 1.7799999713897705]2820174.09%2545827439.73%9.28
2(1.7799999713897705, inf]9864100.00%94574074.13%23.24
合计380653491531508.28%11.08
2026-10-30T19:04:23.908399 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(9.73% → 4.13%),WOE 单调上升(-0.1775 → 0.7402),符合风控分箱预期。IV=0.1300,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 1 箱((-inf, 1.7799999713897705])为坏率最高区间:坏率高达 9.73%,可作为拒绝或重点风控策略的阈值参考

apprv_num_m1
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.5]2815273.93%2542527279.69%9.32
2(0.5, 1.5]535087.98%51072434.54%21.02
3(1.5, inf]4578100.00%43981803.93%24.43
合计380803493031508.27%11.09
2026-10-30T19:04:24.015071 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(9.69% → 3.93%),WOE 单调上升(-0.1734 → 0.7900),符合风控分箱预期。IV=0.1224,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])为坏率最高区间:坏率高达 9.69%,可作为拒绝或重点风控策略的阈值参考

end_loan_to_cur_freq
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, -68.5]2363562.07%21233240210.16%8.84
2(-68.5, inf]14445100.00%136977485.18%18.31
合计380803493031508.27%11.09
2026-10-30T19:04:24.124133 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(10.16% → 5.18%),WOE 单调上升(-0.2267 → 0.5016),符合风控分箱预期。IV=0.1126,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 1 箱((-inf, -68.5])为坏率最高区间:坏率高达 10.16%,可作为拒绝或重点风控策略的阈值参考

cre_l60_used_limit_rate_min
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.2787500023841858]1457738.30%137698085.54%17.04
2(0.2787500023841858, 0.9161500036716461]1889987.94%1715717429.22%9.85
3(0.9161500036716461, inf]4589100.00%398960013.07%6.65
合计380653491531508.28%11.08
2026-10-30T19:04:24.235016 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(5.54% → 13.07%),WOE 单调下降(0.4301 → -0.5111),符合风控分箱预期。IV=0.1055,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 3 箱((0.9161500036716461, inf])为坏率最高区间:坏率高达 13.07%,可作为拒绝或重点风控策略的阈值参考

max_od_days
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 12.5]3593594.37%3322427117.54%12.26
2(12.5, inf]2145100.00%170643920.47%3.89
合计380803493031508.27%11.09
2026-10-30T19:04:24.347466 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(7.54% → 20.47%),WOE 单调下降(0.1000 → -1.0485),符合风控分箱预期。IV=0.1040,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((12.5, inf])为坏率最高区间:坏率高达 20.47%,可作为拒绝或重点风控策略的阈值参考

cre_l90_used_limit_amt_min
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 748.7099914550781]1369435.98%129347605.55%17.02
2(748.7099914550781, 8516.22998046875]2017288.97%1835418189.01%10.10
3(8516.22998046875, inf]4199100.00%362757213.62%6.34
合计380653491531508.28%11.08
2026-10-30T19:04:24.459541 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(5.55% → 13.62%),WOE 单调下降(0.4288 → -0.5585),符合风控分箱预期。IV=0.1036,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 3 箱((8516.22998046875, inf])为坏率最高区间:坏率高达 13.62%,可作为拒绝或重点风控策略的阈值参考

cre_now_l30_used_amt_d_min
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 1.0000500082969666]3121186.91%2827329389.41%9.62
2(1.0000500082969666, 2.482349991798401]363597.03%35041313.60%26.75
3(2.482349991798401, inf]1066100.00%1043232.16%45.35
合计359123282030928.61%10.61
2026-10-30T19:04:24.581882 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(9.41% → 2.16%),WOE 单调上升(-0.0980 → 1.4521),符合风控分箱预期。IV=0.1036,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, 1.0000500082969666])为坏率最高区间:坏率高达 9.41%,可作为拒绝或重点风控策略的阈值参考

available_limit_amt
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.03499999921768904]1238532.52%10918146711.85%7.44
2(0.03499999921768904, 27316.265625]2377194.95%2218315886.68%13.97
3(27316.265625, inf]1924100.00%1829954.94%19.25
合计380803493031508.27%11.09
2026-10-30T19:04:24.699328 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(11.85% → 4.94%),WOE 单调上升(-0.3987 → 0.5517),符合风控分箱预期。IV=0.1036,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, 0.03499999921768904])为坏率最高区间:坏率高达 11.85%,可作为拒绝或重点风控策略的阈值参考

setl_prcp_ratio
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.035099999979138374]832521.86%79603654.38%21.81
2(0.035099999979138374, inf]29755100.00%2697027859.36%9.68
合计380803493031508.27%11.09
2026-10-30T19:04:24.810818 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(4.38% → 9.36%),WOE 单调下降(0.6763 → -0.1355),符合风控分箱预期。IV=0.0909,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((0.035099999979138374, inf])为坏率最高区间:坏率高达 9.36%,可作为拒绝或重点风控策略的阈值参考

loan_os_prcp_credit_amount_m6
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 48.94499969482422]1256433.01%118756895.48%17.24
2(48.94499969482422, 107.59499740600586]1873582.24%1707416618.87%10.28
3(107.59499740600586, inf]6761100.00%596279911.82%7.46
合计380603491131498.27%11.09
2026-10-30T19:04:24.980679 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(5.48% → 11.82%),WOE 单调下降(0.4412 → -0.3959),符合风控分箱预期。IV=0.0893,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((107.59499740600586, inf])为坏率最高区间:坏率高达 11.82%,可作为拒绝或重点风控策略的阈值参考

cre_now_l60_used_amt_d_min
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 1.000950038433075]2794381.74%2520527389.80%9.21
2(1.000950038433075, 1.4549500346183777]235688.63%22351215.14%18.47
3(1.4549500346183777, inf]3886100.00%37151714.40%21.73
合计341853115530308.86%10.28
2026-10-30T19:04:25.089688 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(9.80% → 4.40%),WOE 单调上升(-0.1106 → 0.7481),符合风控分箱预期。IV=0.0761,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 1 箱((-inf, 1.000950038433075])为坏率最高区间:坏率高达 9.80%,可作为拒绝或重点风控策略的阈值参考

loan_os_prcp_credit_amount_m12
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 44.21000099182129]919724.16%87294685.09%18.65
2(44.21000099182129, 95.76499938964844]2108879.57%1923118578.81%10.36
3(95.76499938964844, inf]7775100.00%695182410.60%8.44
合计380603491131498.27%11.09
2026-10-30T19:04:25.203992 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(5.09% → 10.60%),WOE 单调下降(0.5202 → -0.2732),符合风控分箱预期。IV=0.0725,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((95.76499938964844, inf])为坏率最高区间:坏率高达 10.60%,可作为拒绝或重点风控策略的阈值参考

loan_os_prcp_gt0_m3_num_pc
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 58.334999084472656]13793.62%1341382.76%35.29
2(58.334999084472656, 68.16500091552734]19758.81%1911643.24%29.86
3(68.16500091552734, inf]34706100.00%3165930478.78%10.39
合计380603491131498.27%11.09
2026-10-30T19:04:25.322184 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(2.76% → 8.78%),WOE 单调下降(1.1579 → -0.0649),符合风控分箱预期。IV=0.0685,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 1 箱 WOE<0(风险高于平均)

第 3 箱((68.16500091552734, inf])为坏率最高区间:坏率高达 8.78%,可作为拒绝或重点风控策略的阈值参考

loan_os_prcp_credit_amount_m3
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 100.47500228881836]3110881.73%2884222667.28%12.73
2(100.47500228881836, inf]6952100.00%606988312.70%6.87
合计380603491131498.27%11.09
2026-10-30T19:04:25.432404 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(7.28% → 12.70%),WOE 单调下降(0.1381 → -0.4781),符合风控分箱预期。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 1 箱 WOE<0(风险高于平均)

第 2 箱((100.47500228881836, inf])为坏率最高区间:坏率高达 12.70%,可作为拒绝或重点风控策略的阈值参考

useratemore50maxcontimonsm12
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 2.5]836521.98%79244415.27%17.97
2(2.5, 4.5]589337.46%54014928.35%10.98
3(4.5, inf]23802100.00%2158622169.31%9.74
合计380603491131498.27%11.09
2026-10-30T19:04:25.547177 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(5.27% → 9.31%),WOE 单调下降(0.4829 → -0.1294),符合风控分箱预期。IV=0.0530,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((4.5, inf])为坏率最高区间:坏率高达 9.31%,可作为拒绝或重点风控策略的阈值参考

cre_now_l60_used_amt_m_max
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 83.30500030517578]514813.52%49042444.74%20.10
2(83.30500030517578, 2003.010009765625]2408876.81%2208320058.32%11.01
3(2003.010009765625, inf]8829100.00%792890110.21%8.80
合计380653491531508.28%11.08
2026-10-30T19:04:25.661075 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(4.74% → 10.21%),WOE 单调下降(0.5951 → -0.2309),符合风控分箱预期。IV=0.0511,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((2003.010009765625, inf])为坏率最高区间:坏率高达 10.21%,可作为拒绝或重点风控策略的阈值参考

ps_int_amt_sum
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 1891.7449951171875]2395862.91%2223217267.20%12.88
2(1891.7449951171875, 10533.93994140625]1131692.63%1027610409.19%9.88
3(10533.93994140625, inf]2806100.00%242238413.69%6.31
合计380803493031508.27%11.09
2026-10-30T19:04:25.780125 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(7.20% → 13.69%),WOE 单调下降(0.1498 → -0.5642),符合风控分箱预期。IV=0.0471,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((10533.93994140625, inf])为坏率最高区间:坏率高达 13.69%,可作为拒绝或重点风控策略的阈值参考

setl_rem_prcp_paym_m3_num
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.5]3209484.32%2924028548.89%10.25
2(0.5, 1.5]415995.25%39522074.98%19.09
3(1.5, inf]1807100.00%1719884.87%19.53
合计380603491131498.27%11.09
2026-10-30T19:04:25.901343 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(8.89% → 4.87%),WOE 单调上升(-0.0789 → 0.5664),符合风控分箱预期。IV=0.0433,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 1 箱((-inf, 0.5])为坏率最高区间:坏率高达 8.89%,可作为拒绝或重点风控策略的阈值参考

useratemore70monthsubcurmonth
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 4.5]2542966.81%2310223279.15%9.93
2(4.5, 9.5]603782.67%55644737.83%11.76
3(9.5, inf]6594100.00%62453495.29%17.89
合计380603491131498.27%11.09
2026-10-30T19:04:26.020502 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(9.15% → 5.29%),WOE 单调上升(-0.1104 → 0.4787),符合风控分箱预期。IV=0.0416,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 1 箱((-inf, 4.5])为坏率最高区间:坏率高达 9.15%,可作为拒绝或重点风控策略的阈值参考

max_loan_os_prcp_m12
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 1747.2849731445312]410110.78%38602415.88%16.02
2(1747.2849731445312, 20848.58984375]2890586.72%2660622997.95%11.57
3(20848.58984375, inf]5054100.00%444560912.05%7.30
合计380603491131498.27%11.09
2026-10-30T19:04:26.140639 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(5.88% → 12.05%),WOE 单调下降(0.3679 → -0.4180),符合风控分箱预期。IV=0.0415,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 1 箱 WOE<0(风险高于平均)

第 3 箱((20848.58984375, inf])为坏率最高区间:坏率高达 12.05%,可作为拒绝或重点风控策略的阈值参考

avg_tnr
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 6.5]876823.03%82545145.86%16.06
2(6.5, 10.5]1267156.30%1164510268.10%11.35
3(10.5, inf]16641100.00%1503116109.67%9.34
合计380803493031508.27%11.09
2026-10-30T19:04:26.324290 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(5.86% → 9.67%),WOE 单调下降(0.3703 → -0.1721),符合风控分箱预期。IV=0.0412,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 1 箱 WOE<0(风险高于平均)

第 3 箱((10.5, inf])为坏率最高区间:坏率高达 9.67%,可作为拒绝或重点风控策略的阈值参考

useratemore70_m6_num
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 1.5]1525240.07%142619916.50%14.39
2(1.5, 5.5]1788087.05%1623616449.19%9.88
3(5.5, inf]4928100.00%441451410.43%8.59
合计380603491131498.27%11.09
2026-10-30T19:04:26.438592 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(6.50% → 10.43%),WOE 单调下降(0.2609 → -0.2554),符合风控分箱预期。IV=0.0405,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((5.5, inf])为坏率最高区间:坏率高达 10.43%,可作为拒绝或重点风控策略的阈值参考

suc_apprv_num_m6
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 1.5]2201157.80%1992520869.48%9.55
2(1.5, 3.5]781278.32%72405727.32%12.66
3(3.5, inf]8257100.00%77654925.96%15.78
合计380803493031508.27%11.09
2026-10-30T19:04:26.552258 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(9.48% → 5.96%),WOE 单调上升(-0.1492 → 0.3530),符合风控分箱预期。IV=0.0404,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 1 箱((-inf, 1.5])为坏率最高区间:坏率高达 9.48%,可作为拒绝或重点风控策略的阈值参考

shd_pre_repay_amt_rate
组号ValueTot. Records累计占比#Goods#BadsBad RateG/B Odds
1(-inf, 0.6238499879837036]1263733.20%118148236.51%14.35
2(0.6238499879837036, 0.7506499886512756]863155.88%79446877.96%11.56
3(0.7506499886512756, inf]16791100.00%1515216399.76%9.24
合计380593491031498.27%11.09
2026-10-30T19:04:26.664453 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(6.51% → 9.76%),WOE 单调下降(0.2584 → -0.1816),符合风控分箱预期。IV=0.0360,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 1 箱 WOE<0(风险高于平均)

第 3 箱((0.7506499886512756, inf])为坏率最高区间:坏率高达 9.76%,可作为拒绝或重点风控策略的阈值参考

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(跨时间样本)与情景2(跨时间样本剔除一半好样本,批注16)。

测试情景1:跨时间验证样本

变量贡献度

变量贡献度
due_day_my7.3876
suc_apprv_num_m10.0065
f_ps_od_days_max0.0061
cre_now_l30_used_amt_d_min0.0061
cre_now_l60_used_amt_m_min0.0042

区分度验证 — LR + WOE 评分卡 @ 跨时间样本

KS = 1.0000,GINI = 1.0000

2026-10-30T19:04:26.788309 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-10-30T19:04:26.901343 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

测试情景2:跨时间样本剔除一半好样本

指标数值
样本量34730 → 18581(剔除好样本 16149)
KS1.0000
AUC1.0000
GINI1.0000
2026-10-30T19:04:27.015099 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/ 2026-10-30T19:04:27.133874 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/

注:情景2 模拟好样本分布右移的压力情形(固定 seed=42,可复现);剔除后 KS/AUC 下降幅度反映模型对好样本结构的敏感度。