风控模型开发验证报告

00 模型概要信息

项目	内容
模型名称	exp_20261009-134728-59fc
开发完成日期	2026-10-10
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	Local MVP full pipeline run.
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	flag_30
实验 ID	20261009-134728-59fc

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	1970-01-01 ~ 1970-01-01
预留验证样本窗口	1970-01-01 ~ 1970-01-01
跨时间验证样本窗口	1970-01-01 ~ 1970-01-01
PSI时点样本窗口	1970-01-01 ~ 1970-01-01

模型好坏定义

项目	内容
目标变量	flag_30
坏样本(BAD=1)占比	22.06%
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
1	credit_no	主键/时间/口径字段，不入模
2	apply_dt	主键/时间/口径字段，不入模
3	flag_30 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	30000
排除：目标变量为空	0
排除后样本	30000

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	30000	23383	6617	22.06%	100.00%
开发样本	10801	8453	2348	21.74%	36.00%
测试样本	7199	5635	1564	21.73%	24.00%
训练+测试样本	18000	14088	3912	21.73%	60.00%
跨时间验证样本	6000	4624	1376	22.93%	20.00%
PSI时点样本	6000	4671	1329	22.15%	20.00%

开发/预留验证样本交叉统计

类别	总样本数	开发样本数	开发占比	预留验证样本数	预留验证占比
好	23383	8453	36.15%	5635	24.10%
坏	6617	2348	35.48%	1564	23.64%
合计	30000	10801	36.00%	7199	24.00%

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
risk_score	0.1434	0.00%	0.91%	0.9986
risk_score_v2	0.1390	0.00%	0.94%	0.9986
bh_a001	0.1382	0.00%	0.15%	0.9298
ScoreCust2_v2	0.1321	0.00%	1.14%	0.9952
ScoreCust2	0.1277	0.00%	1.13%	0.9952
bh_a009	0.1271	0.00%	1.73%	0.8070
cust_seg2	0.0848	0.00%	30.86%	0.6662
avg_tnr	0.0841	0.00%	1.92%	0.6947
query_cnt_3m	0.0732	0.00%	16.53%	0.5822
income_grade	0.0632	0.00%	31.12%	0.5458
repay_amt_6m_grade	0.0607	0.00%	27.69%	0.5342
rare_field	0.0330	96.32%	0.76%	0.0812
tenure_m	0.0217	0.00%	5.50%	0.3328
card_usage_ratio	0.0214	0.00%	1.69%	0.3613
bank_997_cnt_3m	0.0205	0.00%	34.62%	0.2757
edu_level	0.0004	0.00%	42.13%	—
region_code	0.0004	0.00%	20.63%	—
flag_special	0.0000	0.00%	95.41%	0.0380

注：共 18 个候选变量，按 IV 降序展示前 18 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）

risk_score_v2 — IV 0.1290，10 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 495.5]	1406	1353	53	3.77%	0.2336
2	(495.5, 518.5]	1105	1019	86	7.78%	0.1453
3	(518.5, 527.5]	550	481	69	12.55%	0.0507
4	(527.5, 537.5]	639	574	65	10.17%	0.0735
5	(537.5, 563.5]	1942	1656	286	14.73%	0.1126
6	(563.5, 584.5]	1585	1243	342	21.58%	0.0022
7	(584.5, 602.5]	1181	853	328	27.77%	-0.0625
8	(602.5, 615.5]	667	442	225	33.73%	-0.0759
9	(615.5, 643.5]	986	551	435	44.12%	-0.1927
10	(643.5, inf]	740	281	459	62.03%	-0.2656

risk_score — IV 0.1272，10 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 492.5]	1279	1232	47	3.67%	0.2165
2	(492.5, 519.5]	1276	1182	94	7.37%	0.1696
3	(519.5, 535.5]	1032	914	118	11.43%	0.1000
4	(535.5, 546.5]	801	694	107	13.36%	0.0648
5	(546.5, 564.5]	1350	1144	206	15.26%	0.0779
6	(564.5, 587.5]	1704	1320	384	22.54%	-0.0112
7	(587.5, 600.5]	870	626	244	28.05%	-0.0507
8	(600.5, 616.5]	797	531	266	33.38%	-0.0859
9	(616.5, 643.5]	955	529	426	44.61%	-0.1917
10	(643.5, inf]	737	281	456	61.87%	-0.2638

ScoreCust2_v2 — IV 0.1262，10 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 507.5]	1649	1574	75	4.55%	0.2546
2	(507.5, 522.5]	1083	979	104	9.60%	0.1235
3	(522.5, 536.5]	1285	1138	147	11.44%	0.1205
4	(536.5, 544.5]	790	677	113	14.30%	0.0567
5	(544.5, 553.5]	959	776	183	19.08%	0.0237
6	(553.5, 565.5]	1232	965	267	21.67%	0.0007
7	(565.5, 583.5]	1611	1171	440	27.31%	-0.0737
8	(583.5, 593.5]	655	432	223	34.05%	-0.0766
9	(593.5, 606.5]	640	370	270	42.19%	-0.1231
10	(606.5, inf]	897	371	526	58.64%	-0.2861

ScoreCust2 — IV 0.1191，10 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 494.5]	957	923	34	3.55%	0.1690
2	(494.5, 508.5]	749	703	46	6.14%	0.1154
3	(508.5, 528.5]	1534	1387	147	9.58%	0.1658
4	(528.5, 545.5]	1664	1438	226	13.58%	0.1168
5	(545.5, 562.5]	1785	1421	364	20.39%	0.0198
6	(562.5, 572.5]	972	735	237	24.38%	-0.0235
7	(572.5, 579.5]	636	457	179	28.14%	-0.0392
8	(579.5, 592.5]	912	609	303	33.22%	-0.0950
9	(592.5, 604.5]	615	364	251	40.81%	-0.1111
10	(604.5, inf]	977	416	561	57.42%	-0.2967

bh_a001 — IV 0.1128，10 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 223.6500015258789]	1032	994	38	3.68%	0.1794
2	(223.6500015258789, 254.3499984741211]	1349	1266	83	6.15%	0.1937
3	(254.3499984741211, 280.0500030517578]	1560	1381	179	11.47%	0.1408
4	(280.0500030517578, 290.84999084472656]	786	672	114	14.50%	0.0549
5	(290.84999084472656, 317.0500030517578]	1927	1560	367	19.05%	0.0421
6	(317.0500030517578, 332.75]	1088	818	270	24.82%	-0.0301
7	(332.75, 354.0500030517578]	1197	825	372	31.08%	-0.0969
8	(354.0500030517578, 367.75]	542	334	208	38.38%	-0.0871
9	(367.75, 394.25]	756	392	364	48.15%	-0.1814
10	(394.25, inf]	564	211	353	62.59%	-0.2142

cust_seg2 — IV 0.1060，4 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 3.5]	2056	1867	189	9.19%	0.2166
2	(3.5, 4.5]	3333	2822	511	15.33%	0.1501
3	(4.5, 5.5]	3330	2505	825	24.77%	-0.0668
4	(5.5, inf]	2082	1259	823	39.53%	-0.2705

bh_a009 — IV 0.0991，10 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 6.3500001430511475]	902	433	469	52.00%	-0.2386
2	(6.3500001430511475, 7.3500001430511475]	875	548	327	37.37%	-0.1238
3	(7.3500001430511475, 8.050000190734863]	773	509	264	34.15%	-0.0891
4	(8.050000190734863, 8.849999904632568]	1062	753	309	29.10%	-0.0697
5	(8.849999904632568, 9.25]	578	430	148	25.61%	-0.0218
6	(9.25, 10.050000190734863]	1235	982	253	20.49%	0.0138
7	(10.050000190734863, 11.449999809265137]	2187	1873	314	14.36%	0.1298
8	(11.449999809265137, 12.75]	1501	1362	139	9.26%	0.1674
9	(12.75, 13.550000190734863]	664	591	73	10.99%	0.0706
10	(13.550000190734863, inf]	1024	972	52	5.08%	0.1637

rare_field — IV 0.0827，10 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, -1.437000036239624]	23	21	2	8.70%	0.0847
2	(-1.437000036239624, -0.8540000021457672]	60	43	17	28.33%	-0.0735
3	(-0.8540000021457672, -0.443000003695488]	50	43	7	14.00%	0.1024
4	(-0.443000003695488, -0.25699999928474426]	25	15	10	40.00%	-0.1074
5	(-0.25699999928474426, -0.05299999937415123]	30	26	4	13.33%	0.0713
6	(-0.05299999937415123, 0.10999999940395355]	24	14	10	41.67%	-0.1134
7	(0.10999999940395355, 0.3699999898672104]	34	24	10	29.41%	-0.0554
8	(0.3699999898672104, 1.0099999904632568]	89	80	9	10.11%	0.2372
9	(1.0099999904632568, 1.324500024318695]	26	19	7	26.92%	-0.0279
10	(1.324500024318695, inf]	36	22	14	38.89%	-0.1369

avg_tnr — IV 0.0751，10 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 2.149999976158142]	926	474	452	48.81%	-0.2194
2	(2.149999976158142, 3.350000023841858]	961	609	352	36.63%	-0.1276
3	(3.350000023841858, 4.450000047683716]	1258	903	355	28.22%	-0.0706
4	(4.450000047683716, 4.8500001430511475]	550	422	128	23.27%	-0.0083
5	(4.8500001430511475, 5.450000047683716]	835	667	168	20.12%	0.0128
6	(5.450000047683716, 5.950000047683716]	748	580	168	22.46%	-0.0051
7	(5.950000047683716, 7.3500001430511475]	2097	1722	375	17.88%	0.0646
8	(7.3500001430511475, 8.550000190734863]	1478	1313	165	11.16%	0.1390
9	(8.550000190734863, 9.449999809265137]	754	657	97	12.86%	0.0651
10	(9.449999809265137, inf]	1194	1106	88	7.37%	0.1602

income_grade — IV 0.0632，5 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 3.5]	1211	743	468	38.65%	-0.1736
2	(3.5, 4.5]	2510	1766	744	29.64%	-0.1417
3	(4.5, 5.5]	3361	2689	672	19.99%	0.0398
4	(5.5, 6.5]	2503	2150	353	14.10%	0.1484
5	(6.5, inf]	1216	1105	111	9.13%	0.1419

repay_amt_6m_grade — IV 0.0607，5 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 3.5]	1647	1047	600	36.43%	-0.1915
2	(3.5, 4.5]	2268	1634	634	27.95%	-0.1049
3	(4.5, 5.5]	2991	2384	607	20.29%	0.0305
4	(5.5, 6.5]	2286	1944	342	14.96%	0.1227
5	(6.5, inf]	1609	1444	165	10.25%	0.1624

query_cnt_3m — IV 0.0483，9 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 0.5]	550	288	262	47.64%	-0.1355
2	(0.5, 1.5]	1123	724	399	35.53%	-0.1345
3	(1.5, 2.5]	1627	1151	476	29.26%	-0.0995
4	(2.5, 3.5]	1785	1384	401	22.46%	-0.0106
5	(3.5, 4.5]	1665	1365	300	18.02%	0.0523
6	(4.5, 5.5]	1347	1127	220	16.33%	0.0646
7	(5.5, 6.5]	1026	897	129	12.57%	0.0882
8	(6.5, 8.5]	1098	970	128	11.66%	0.1031
9	(8.5, inf]	580	547	33	5.69%	0.0940

bank_997_cnt_3m — IV 0.0246，4 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 0.5]	3447	2899	548	15.90%	0.1392
2	(0.5, 1.5]	3739	2928	811	21.69%	0.0012
3	(1.5, 2.5]	2224	1655	569	25.58%	-0.0648
4	(2.5, inf]	1391	971	420	30.19%	-0.0990

card_usage_ratio — IV 0.0183，10 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 0.107999999076128]	922	854	68	7.38%	0.1277
2	(0.107999999076128, 0.1578500047326088]	616	511	105	17.05%	0.0285
3	(0.1578500047326088, 0.3025500029325485]	2324	1848	476	20.48%	0.0224
4	(0.3025500029325485, 0.3381499946117401]	649	545	104	16.02%	0.0364
5	(0.3381499946117401, 0.4825499951839447]	2550	1996	554	21.73%	0.0003
6	(0.4825499951839447, 0.5283499956130981]	687	497	190	27.66%	-0.0388
7	(0.5283499956130981, 0.6137499809265137]	1203	941	262	21.78%	-0.0004
8	(0.6137499809265137, 0.6709499955177307]	594	405	189	31.82%	-0.0578
9	(0.6709499955177307, 0.7716499865055084]	715	520	195	27.27%	-0.0376
10	(0.7716499865055084, inf]	541	336	205	37.89%	-0.0844

tenure_m — IV 0.0170，10 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 2.5]	696	414	282	40.52%	-0.1218
2	(2.5, 10.5]	1179	891	288	24.43%	-0.0281
3	(10.5, 23.5]	2292	1813	479	20.90%	0.0148
4	(23.5, 30.5]	1310	997	313	23.89%	-0.0245
5	(30.5, 36.5]	1080	844	236	21.85%	-0.0011
6	(36.5, 39.5]	554	448	106	19.13%	0.0143
7	(39.5, 46.5]	1268	974	294	23.19%	-0.0161
8	(46.5, 53.5]	1211	970	241	19.90%	0.0199
9	(53.5, 58.5]	647	568	79	12.21%	0.0610
10	(58.5, inf]	564	534	30	5.32%	0.0937

edu_level — IV 0.0004，4 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	大专	3260	2574	686	21.04%	0.0155
2	本科	4550	3555	995	21.87%	-0.0035
3	硕士及以上	1405	1081	324	23.06%	-0.0160
4	高中及以下	1586	1243	343	21.63%	0.0015

region_code — IV 0.0004，5 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	其他	2119	1657	462	21.80%	-0.0011
2	华东	2156	1685	471	21.85%	-0.0018
3	华北	2119	1682	437	20.62%	0.0186
4	华南	2179	1701	478	21.94%	-0.0033
5	西南	2228	1728	500	22.44%	-0.0120

flag_special — IV 0.0000，1 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, inf]	10801	8453	2348	21.74%	0.0000

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 18 个变量，共 18 个；完整数据见 feature_report.json。

04 单变量分析（粗分箱）

risk_score_v2 — 10 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 495.5]	1406	1353	53	3.77%	0.2336
2	(495.5, 518.5]	1105	1019	86	7.78%	0.1453
3	(518.5, 527.5]	550	481	69	12.55%	0.0507
4	(527.5, 537.5]	639	574	65	10.17%	0.0735
5	(537.5, 563.5]	1942	1656	286	14.73%	0.1126
6	(563.5, 584.5]	1585	1243	342	21.58%	0.0022
7	(584.5, 602.5]	1181	853	328	27.77%	-0.0625
8	(602.5, 615.5]	667	442	225	33.73%	-0.0759
9	(615.5, 643.5]	986	551	435	44.12%	-0.1927
10	(643.5, inf]	740	281	459	62.03%	-0.2656

cust_seg2 — 4 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 3.5]	2056	1867	189	9.19%	0.2166
2	(3.5, 4.5]	3333	2822	511	15.33%	0.1501
3	(4.5, 5.5]	3330	2505	825	24.77%	-0.0668
4	(5.5, inf]	2082	1259	823	39.53%	-0.2705

avg_tnr — 10 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 2.149999976158142]	926	474	452	48.81%	-0.2194
2	(2.149999976158142, 3.350000023841858]	961	609	352	36.63%	-0.1276
3	(3.350000023841858, 4.450000047683716]	1258	903	355	28.22%	-0.0706
4	(4.450000047683716, 4.8500001430511475]	550	422	128	23.27%	-0.0083
5	(4.8500001430511475, 5.450000047683716]	835	667	168	20.12%	0.0128
6	(5.450000047683716, 5.950000047683716]	748	580	168	22.46%	-0.0051
7	(5.950000047683716, 7.3500001430511475]	2097	1722	375	17.88%	0.0646
8	(7.3500001430511475, 8.550000190734863]	1478	1313	165	11.16%	0.1390
9	(8.550000190734863, 9.449999809265137]	754	657	97	12.86%	0.0651
10	(9.449999809265137, inf]	1194	1106	88	7.37%	0.1602

income_grade — 5 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 3.5]	1211	743	468	38.65%	-0.1736
2	(3.5, 4.5]	2510	1766	744	29.64%	-0.1417
3	(4.5, 5.5]	3361	2689	672	19.99%	0.0398
4	(5.5, 6.5]	2503	2150	353	14.10%	0.1484
5	(6.5, inf]	1216	1105	111	9.13%	0.1419

repay_amt_6m_grade — 5 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 3.5]	1647	1047	600	36.43%	-0.1915
2	(3.5, 4.5]	2268	1634	634	27.95%	-0.1049
3	(4.5, 5.5]	2991	2384	607	20.29%	0.0305
4	(5.5, 6.5]	2286	1944	342	14.96%	0.1227
5	(6.5, inf]	1609	1444	165	10.25%	0.1624

query_cnt_3m — 9 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 0.5]	550	288	262	47.64%	-0.1355
2	(0.5, 1.5]	1123	724	399	35.53%	-0.1345
3	(1.5, 2.5]	1627	1151	476	29.26%	-0.0995
4	(2.5, 3.5]	1785	1384	401	22.46%	-0.0106
5	(3.5, 4.5]	1665	1365	300	18.02%	0.0523
6	(4.5, 5.5]	1347	1127	220	16.33%	0.0646
7	(5.5, 6.5]	1026	897	129	12.57%	0.0882
8	(6.5, 8.5]	1098	970	128	11.66%	0.1031
9	(8.5, inf]	580	547	33	5.69%	0.0940

bank_997_cnt_3m — 4 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	(-inf, 0.5]	3447	2899	548	15.90%	0.1392
2	(0.5, 1.5]	3739	2928	811	21.69%	0.0012
3	(1.5, 2.5]	2224	1655	569	25.58%	-0.0648
4	(2.5, inf]	1391	971	420	30.19%	-0.0990

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	0.02
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-K	80
黑名单	无
白名单	无

候选变量 18，成功分箱 18，入模 7

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
risk_score	—	N	0.1272	PASS	PASS	PASS	FAIL	0.0037	与 risk_score_v2 相关>0.7
ScoreCust2	—	N	0.1191	PASS	PASS	PASS	FAIL	0.0026	与 risk_score_v2 相关>0.7
cust_seg2	—	Y	0.1060	PASS	PASS	PASS	PASS	0.0005
income_grade	—	Y	0.0632	PASS	PASS	PASS	PASS	0.0009
bh_a001	—	N	0.1128	PASS	PASS	PASS	FAIL	0.0027	与 risk_score_v2 相关>0.7
bh_a009	—	N	0.0991	PASS	PASS	PASS	FAIL	0.0012	与 risk_score_v2 相关>0.7
bank_997_cnt_3m	—	Y	0.0246	PASS	PASS	PASS	PASS	0.0010
avg_tnr	—	Y	0.0751	PASS	PASS	PASS	PASS	0.0030
repay_amt_6m_grade	—	Y	0.0607	PASS	PASS	PASS	PASS	0.0007
tenure_m	—	N	0.0170	PASS	PASS	FAIL	—	—	IV<0.02
query_cnt_3m	—	Y	0.0483	PASS	PASS	PASS	PASS	0.0014
card_usage_ratio	—	N	0.0183	PASS	PASS	FAIL	—	—	IV<0.02
rare_field	—	N	0.0827	FAIL	PASS	—	—	—	缺失率>0.8
flag_special	—	N	0.0000	PASS	FAIL	—	—	—	集中度>0.9
edu_level	—	N	0.0004	PASS	PASS	FAIL	—	—	IV<0.02
region_code	—	N	0.0004	PASS	PASS	FAIL	—	—	IV<0.02
risk_score_v2	—	Y	0.1290	PASS	PASS	PASS	PASS	0.0032
ScoreCust2_v2	—	N	0.1262	PASS	PASS	PASS	FAIL	0.0020	与 risk_score_v2 相关>0.7

06 回归结果

硬门槛：P≥0.1 / VIF>4.0 迭代剔除，共 0 个变量被剔除（1 轮）。

Parameter	Label	Estimate(无惩罚)	Standard Error	Wald Chi-Square	Pr > ChiSq	Standardized Estimate
Intercept	Intercept	-1.3418	0.0267	2521.30	<0.0001	—
WOE_risk_score_v2	risk_score_v2	-4.8453	0.2906	278.00	<0.0001	-0.6912
WOE_cust_seg2	cust_seg2	-0.3736	0.1742	4.60	0.0319	-0.0659
WOE_avg_tnr	avg_tnr	-1.2616	0.2752	21.02	<0.0001	-0.1411
WOE_income_grade	income_grade	-0.6472	0.2251	8.26	0.0040	-0.0814
WOE_repay_amt_6m_grade	repay_amt_6m_grade	-0.4905	0.2297	4.56	0.0327	-0.0605
WOE_query_cnt_3m	query_cnt_3m	-0.7275	0.3382	4.63	0.0315	-0.0632
WOE_bank_997_cnt_3m	bank_997_cnt_3m	-0.7723	0.2966	6.78	0.0092	-0.0689

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

Parameter	VIF
risk_score_v2	2.87240
cust_seg2	1.61868
avg_tnr	1.69886
income_grade	1.31863
repay_amt_6m_grade	1.33480
query_cnt_3m	1.44759
bank_997_cnt_3m	1.06846

	risk_score_v2	cust_seg2	avg_tnr	income_grade	repay_amt_6m_grade	query_cnt_3m	bank_997_cnt_3m
risk_score_v2	1.000	0.608	0.634	0.482	0.492	0.546	0.246
cust_seg2	0.608	1.000	0.435	0.333	0.352	0.385	0.176
avg_tnr	0.634	0.435	1.000	0.343	0.350	0.392	0.178
income_grade	0.482	0.333	0.343	1.000	0.277	0.310	0.152
repay_amt_6m_grade	0.492	0.352	0.350	0.277	1.000	0.305	0.137
query_cnt_3m	0.546	0.385	0.392	0.310	0.305	1.000	0.155
bank_997_cnt_3m	0.246	0.176	0.178	0.152	0.137	0.155	1.000

08 最终评分卡

标准分600

标准ODD50

PDO20

变量	分栏	WOE	Coefficient	Scorecard Points
risk_score_v2	(-inf, 495.5]	0.2336	-4.5742	106
	(495.5, 518.5]	0.1453	-4.5742	94
	(518.5, 527.5]	0.0507	-4.5742	82
	(527.5, 537.5]	0.0735	-4.5742	85
	(537.5, 563.5]	0.1126	-4.5742	90
	(563.5, 584.5]	0.0022	-4.5742	75
	(584.5, 602.5]	-0.0625	-4.5742	67
	(602.5, 615.5]	-0.0759	-4.5742	65
	(615.5, 643.5]	-0.1927	-4.5742	50
	(643.5, inf]	-0.2656	-4.5742	40
cust_seg2	(-inf, 3.5]	0.2166	-0.4371	78
	(3.5, 4.5]	0.1501	-0.4371	77
	(4.5, 5.5]	-0.0668	-0.4371	74
	(5.5, inf]	-0.2705	-0.4371	72
avg_tnr	(-inf, 2.149999976158142]	-0.2194	-1.3407	67
	(2.149999976158142, 3.350000023841858]	-0.1276	-1.3407	70
	(3.350000023841858, 4.450000047683716]	-0.0706	-1.3407	72
	(4.450000047683716, 4.8500001430511475]	-0.0083	-1.3407	75
	(4.8500001430511475, 5.450000047683716]	0.0128	-1.3407	76
	(5.450000047683716, 5.950000047683716]	-0.0051	-1.3407	75
	(5.950000047683716, 7.3500001430511475]	0.0646	-1.3407	78
	(7.3500001430511475, 8.550000190734863]	0.1390	-1.3407	80
	(8.550000190734863, 9.449999809265137]	0.0651	-1.3407	78
	(9.449999809265137, inf]	0.1602	-1.3407	81
income_grade	(-inf, 3.5]	-0.1736	-0.6858	72
	(3.5, 4.5]	-0.1417	-0.6858	72
	(4.5, 5.5]	0.0398	-0.6858	76
	(5.5, 6.5]	0.1484	-0.6858	78
	(6.5, inf]	0.1419	-0.6858	78
repay_amt_6m_grade	(-inf, 3.5]	-0.1915	-0.5370	72
	(3.5, 4.5]	-0.1049	-0.5370	73
	(4.5, 5.5]	0.0305	-0.5370	76
	(5.5, 6.5]	0.1227	-0.5370	77
	(6.5, inf]	0.1624	-0.5370	78
query_cnt_3m	(-inf, 0.5]	-0.1355	-0.7543	72
	(0.5, 1.5]	-0.1345	-0.7543	72
	(1.5, 2.5]	-0.0995	-0.7543	73
	(2.5, 3.5]	-0.0106	-0.7543	75
	(3.5, 4.5]	0.0523	-0.7543	76
	(4.5, 5.5]	0.0646	-0.7543	77
	(5.5, 6.5]	0.0882	-0.7543	77
	(6.5, 8.5]	0.1031	-0.7543	77
	(8.5, inf]	0.0940	-0.7543	77
bank_997_cnt_3m	(-inf, 0.5]	0.1392	-0.7458	78
	(0.5, 1.5]	0.0012	-0.7458	75
	(1.5, 2.5]	-0.0648	-0.7458	74
	(2.5, inf]	-0.0990	-0.7458	73

09 模型表现

模型区分度评估（按样本）

模型	样本	KS	AUC	GINI
LR + WOE 评分卡	开发样本	0.3779	0.7520	0.5040
LR + WOE 评分卡	测试样本	0.3652	0.7444	0.4888
LR + WOE 评分卡	训练+测试样本	0.3709	0.7489	0.4978
LR + WOE 评分卡	跨时间验证样本	0.3426	0.7288	0.4576
LR + WOE 评分卡	PSI时点样本	0.3750	0.7455	0.4910

LR + WOE 评分卡 — 4 样本评估表（score_cut 粒度）

开发样本（KS 0.3779，AUC 0.7520）

cuts	cnt	bad	good	bad_rate	population_pct	cum_bad_rate	cum_good_rate
(0.0437, 0.0598]	1082	41	1041	3.79%	10.02%	1.75%	12.32%
(0.0598, 0.0910]	1079	71	1008	6.58%	9.99%	4.77%	24.24%
(0.0910, 0.1114]	1081	112	969	10.36%	10.01%	9.54%	35.70%
(0.1114, 0.1293]	1079	144	935	13.35%	9.99%	15.67%	46.76%
(0.1293, 0.1567]	1080	151	929	13.98%	10.00%	22.10%	57.75%
(0.1568, 0.2061]	1080	203	877	18.80%	10.00%	30.75%	68.13%
(0.2061, 0.2612]	1080	269	811	24.91%	10.00%	42.21%	77.72%
(0.2612, 0.3293]	1080	299	781	27.69%	10.00%	54.94%	86.96%
(0.3297, 0.4877]	1080	435	645	40.28%	10.00%	73.47%	94.59%
(0.4878, 0.6655]	1080	623	457	57.69%	10.00%	100.00%	100.00%

测试样本（KS 0.3652，AUC 0.7444）

cuts	cnt	bad	good	bad_rate	population_pct	cum_bad_rate	cum_good_rate
(0.0437, 0.0587]	720	30	690	4.17%	10.00%	1.92%	12.24%
(0.0587, 0.0910]	720	49	671	6.81%	10.00%	5.05%	24.15%
(0.0910, 0.1112]	720	85	635	11.81%	10.00%	10.49%	35.42%
(0.1112, 0.1294]	720	89	631	12.36%	10.00%	16.18%	46.62%
(0.1295, 0.1532]	720	101	619	14.03%	10.00%	22.63%	57.60%
(0.1532, 0.2041]	719	139	580	19.33%	9.99%	31.52%	67.90%
(0.2043, 0.2621]	720	184	536	25.56%	10.00%	43.29%	77.41%
(0.2622, 0.3280]	720	213	507	29.58%	10.00%	56.91%	86.41%
(0.3282, 0.4782]	720	269	451	37.36%	10.00%	74.10%	94.41%
(0.4785, 0.6655]	720	405	315	56.25%	10.00%	100.00%	100.00%

训练+测试样本（KS 0.3709，AUC 0.7489）

cuts	cnt	bad	good	bad_rate	population_pct	cum_bad_rate	cum_good_rate
(0.0437, 0.0591]	1800	71	1729	3.94%	10.00%	1.81%	12.27%
(0.0591, 0.0910]	1800	120	1680	6.67%	10.00%	4.88%	24.20%
(0.0910, 0.1113]	1800	198	1602	11.00%	10.00%	9.94%	35.57%
(0.1113, 0.1294]	1800	230	1570	12.78%	10.00%	15.82%	46.71%
(0.1294, 0.1554]	1800	255	1545	14.17%	10.00%	22.34%	57.68%
(0.1554, 0.2053]	1800	342	1458	19.00%	10.00%	31.08%	68.03%
(0.2053, 0.2614]	1800	452	1348	25.11%	10.00%	42.64%	77.60%
(0.2614, 0.3286]	1800	510	1290	28.33%	10.00%	55.67%	86.75%
(0.3287, 0.4844]	1800	710	1090	39.44%	10.00%	73.82%	94.49%
(0.4845, 0.6655]	1800	1024	776	56.89%	10.00%	100.00%	100.00%

跨时间验证样本（KS 0.3426，AUC 0.7288）

cuts	cnt	bad	good	bad_rate	population_pct	cum_bad_rate	cum_good_rate
(0.0437, 0.0598]	600	22	578	3.67%	10.00%	1.60%	12.50%
(0.0600, 0.0910]	600	56	544	9.33%	10.00%	5.67%	24.26%
(0.0910, 0.1119]	600	73	527	12.17%	10.00%	10.97%	35.66%
(0.1120, 0.1300]	600	99	501	16.50%	10.00%	18.17%	46.50%
(0.1301, 0.1575]	600	84	516	14.00%	10.00%	24.27%	57.66%
(0.1575, 0.2072]	600	133	467	22.17%	10.00%	33.94%	67.76%
(0.2076, 0.2579]	600	165	435	27.50%	10.00%	45.93%	77.16%
(0.2580, 0.3281]	600	182	418	30.33%	10.00%	59.16%	86.20%
(0.3282, 0.4824]	600	239	361	39.83%	10.00%	76.53%	94.01%
(0.4824, 0.6655]	600	323	277	53.83%	10.00%	100.00%	100.00%

注：score_cut 表基于模型输出 PD 等频分 10 档（分数升序），每表附 KS/AUC；训练+测试 = TRAIN 全体。

10 KS和GINI

LR + WOE 评分卡

样本	KS	GINI	AUC
开发样本	0.3779	0.5040	0.7520
测试样本	0.3652	0.4888	0.7444
训练+测试样本	0.3709	0.4978	0.7489
跨时间验证样本	0.3426	0.4576	0.7288
PSI时点样本	0.3750	0.4910	0.7455

开发样本

测试样本

训练+测试样本

跨时间验证样本

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	0.0027	0.0016	—	0.0023

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,0.0598]	10.00%	9.95%	0.0000	0.0000
(0.0598,0.091]	10.00%	10.03%	0.0000	0.0000
(0.091,0.1114]	10.00%	9.68%	0.0001	0.0001
(0.1114,0.1293]	10.00%	10.02%	0.0000	0.0001
(0.1293,0.1567]	10.00%	10.10%	0.0000	0.0001
(0.1567,0.2061]	10.01%	9.75%	0.0001	0.0002
(0.2061,0.2612]	10.00%	11.02%	0.0010	0.0012
(0.2612,0.3293]	9.98%	9.57%	0.0002	0.0013
(0.3293,0.4877]	10.00%	10.28%	0.0001	0.0014
(0.4877,inf]	10.02%	9.60%	0.0002	0.0016

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,0.0598]	10.00%	9.65%	0.0001	0.0001
(0.0598,0.091]	10.00%	9.75%	0.0001	0.0002
(0.091,0.1114]	10.00%	10.25%	0.0001	0.0002
(0.1114,0.1293]	10.00%	10.67%	0.0004	0.0007
(0.1293,0.1567]	10.00%	10.43%	0.0002	0.0009
(0.1567,0.2061]	10.01%	10.27%	0.0001	0.0009
(0.2061,0.2612]	10.00%	9.50%	0.0003	0.0012
(0.2612,0.3293]	9.98%	9.00%	0.0010	0.0022
(0.3293,0.4877]	10.00%	10.23%	0.0001	0.0023
(0.4877,inf]	10.02%	10.25%	0.0001	0.0023

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]	1080	10.00%	751	10.43%	1831	10.17%	597	9.95%	579	9.65%
(0.0598, 0.091]	1080	10.00%	685	9.52%	1765	9.81%	602	10.03%	585	9.75%
(0.091, 0.1114]	1080	10.00%	735	10.21%	1815	10.08%	581	9.68%	615	10.25%
(0.1114, 0.1293]	1080	10.00%	703	9.77%	1783	9.91%	601	10.02%	640	10.67%
(0.1293, 0.1567]	1080	10.00%	792	11.00%	1872	10.40%	606	10.10%	626	10.43%
(0.1567, 0.2061]	1081	10.01%	680	9.45%	1761	9.78%	585	9.75%	616	10.27%
(0.2061, 0.2612]	1080	10.00%	689	9.57%	1769	9.83%	661	11.02%	570	9.50%
(0.2612, 0.3293]	1078	9.98%	737	10.24%	1815	10.08%	574	9.57%	540	9.00%
(0.3293, 0.4877]	1080	10.00%	753	10.46%	1833	10.18%	617	10.28%	614	10.23%
(0.4877, inf]	1082	10.02%	674	9.36%	1756	9.76%	576	9.60%	615	10.25%
sum	10801	100.00%	7199	100.00%	18000	100.00%	6000	100.00%	6000	100.00%

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
risk_score_v2	0.0032	稳定
avg_tnr	0.0030	稳定
query_cnt_3m	0.0014	稳定
bank_997_cnt_3m	0.0010	稳定
income_grade	0.0009	稳定
repay_amt_6m_grade	0.0007	稳定
cust_seg2	0.0005	稳定

risk_score_v2 — PSI 0.0032

分栏	开发基准比例	PSI时点样本比例	变更	贡献
(-inf, 495.5]	13.02%	12.23%	-0.78%	0.0005
(495.5, 518.5]	10.23%	11.20%	0.97%	0.0009
(518.5, 527.5]	5.09%	5.32%	0.22%	0.0001
(527.5, 537.5]	5.92%	6.25%	0.33%	0.0002
(537.5, 563.5]	17.98%	18.28%	0.30%	0.0001
(563.5, 584.5]	14.67%	14.03%	-0.64%	0.0003
(584.5, 602.5]	10.93%	10.13%	-0.80%	0.0006
(602.5, 615.5]	6.18%	5.78%	-0.39%	0.0003
(615.5, 643.5]	9.13%	9.52%	0.39%	0.0002
(643.5, inf]	6.85%	7.25%	0.40%	0.0002

cust_seg2 — PSI 0.0005

分栏	开发基准比例	PSI时点样本比例	变更	贡献
(-inf, 3.5]	19.04%	19.07%	0.03%	0.0000
(3.5, 4.5]	30.86%	30.50%	-0.36%	0.0000
(4.5, 5.5]	30.83%	31.77%	0.94%	0.0003
(5.5, inf]	19.28%	18.67%	-0.61%	0.0002

avg_tnr — PSI 0.0030

分栏	开发基准比例	PSI时点样本比例	变更	贡献
(-inf, 2.149999976158142]	8.57%	8.88%	0.31%	0.0001
(2.149999976158142, 3.350000023841858]	8.90%	8.87%	-0.03%	0.0000
(3.350000023841858, 4.450000047683716]	11.65%	11.57%	-0.08%	0.0000
(4.450000047683716, 4.8500001430511475]	5.09%	5.18%	0.09%	0.0000
(4.8500001430511475, 5.450000047683716]	7.73%	7.87%	0.14%	0.0000
(5.450000047683716, 5.950000047683716]	6.93%	6.62%	-0.31%	0.0001
(5.950000047683716, 7.3500001430511475]	19.41%	18.15%	-1.26%	0.0009
(7.3500001430511475, 8.550000190734863]	13.68%	13.52%	-0.17%	0.0000
(8.550000190734863, 9.449999809265137]	6.98%	8.15%	1.17%	0.0018
(9.449999809265137, inf]	11.05%	11.20%	0.15%	0.0000

income_grade — PSI 0.0009

分栏	开发基准比例	PSI时点样本比例	变更	贡献
(-inf, 3.5]	11.21%	10.70%	-0.51%	0.0002
(3.5, 4.5]	23.24%	23.15%	-0.09%	0.0000
(4.5, 5.5]	31.12%	31.07%	-0.05%	0.0000
(5.5, 6.5]	23.17%	24.25%	1.08%	0.0005
(6.5, inf]	11.26%	10.83%	-0.42%	0.0002

repay_amt_6m_grade — PSI 0.0007

分栏	开发基准比例	PSI时点样本比例	变更	贡献
(-inf, 3.5]	15.25%	14.83%	-0.42%	0.0001
(3.5, 4.5]	21.00%	21.75%	0.75%	0.0003
(4.5, 5.5]	27.69%	26.92%	-0.78%	0.0002
(5.5, 6.5]	21.16%	21.43%	0.27%	0.0000
(6.5, inf]	14.90%	15.07%	0.17%	0.0000

query_cnt_3m — PSI 0.0014

分栏	开发基准比例	PSI时点样本比例	变更	贡献
(-inf, 0.5]	5.09%	5.28%	0.19%	0.0001
(0.5, 1.5]	10.40%	10.45%	0.05%	0.0000
(1.5, 2.5]	15.06%	14.95%	-0.11%	0.0000
(2.5, 3.5]	16.53%	16.27%	-0.26%	0.0000
(3.5, 4.5]	15.42%	15.28%	-0.13%	0.0000
(4.5, 5.5]	12.47%	13.08%	0.61%	0.0003
(5.5, 6.5]	9.50%	9.20%	-0.30%	0.0001
(6.5, 8.5]	10.17%	10.67%	0.50%	0.0002
(8.5, inf]	5.37%	4.82%	-0.55%	0.0006

bank_997_cnt_3m — PSI 0.0010

分栏	开发基准比例	PSI时点样本比例	变更	贡献
(-inf, 0.5]	31.91%	32.48%	0.57%	0.0001
(0.5, 1.5]	34.62%	35.27%	0.65%	0.0001
(1.5, 2.5]	20.59%	20.35%	-0.24%	0.0000
(2.5, inf]	12.88%	11.90%	-0.98%	0.0008

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。

risk_score_v2

组号	Value	Tot. Records	累计占比	#Goods	#Bads	Bad Rate	G/B Odds
1	(-inf, 495.5]	1406	13.02%	1353	53	3.77%	25.53
2	(495.5, 518.5]	1105	23.25%	1019	86	7.78%	11.85
3	(518.5, 527.5]	550	28.34%	481	69	12.55%	6.97
4	(527.5, 537.5]	639	34.26%	574	65	10.17%	8.83
5	(537.5, 563.5]	1942	52.24%	1656	286	14.73%	5.79
6	(563.5, 584.5]	1585	66.91%	1243	342	21.58%	3.63
7	(584.5, 602.5]	1181	77.84%	853	328	27.77%	2.60
8	(602.5, 615.5]	667	84.02%	442	225	33.73%	1.96
9	(615.5, 643.5]	986	93.15%	551	435	44.12%	1.27
10	(643.5, inf]	740	100.00%	281	459	62.03%	0.61
合计		10801		8453	2348	21.74%	3.60

cust_seg2

组号	Value	Tot. Records	累计占比	#Goods	#Bads	Bad Rate	G/B Odds
1	(-inf, 3.5]	2056	19.04%	1867	189	9.19%	9.88
2	(3.5, 4.5]	3333	49.89%	2822	511	15.33%	5.52
3	(4.5, 5.5]	3330	80.72%	2505	825	24.77%	3.04
4	(5.5, inf]	2082	100.00%	1259	823	39.53%	1.53
合计		10801		8453	2348	21.74%	3.60

avg_tnr

组号	Value	Tot. Records	累计占比	#Goods	#Bads	Bad Rate	G/B Odds
1	(-inf, 2.149999976158142]	926	8.57%	474	452	48.81%	1.05
2	(2.149999976158142, 3.350000023841858]	961	17.47%	609	352	36.63%	1.73
3	(3.350000023841858, 4.450000047683716]	1258	29.12%	903	355	28.22%	2.54
4	(4.450000047683716, 4.8500001430511475]	550	34.21%	422	128	23.27%	3.30
5	(4.8500001430511475, 5.450000047683716]	835	41.94%	667	168	20.12%	3.97
6	(5.450000047683716, 5.950000047683716]	748	48.87%	580	168	22.46%	3.45
7	(5.950000047683716, 7.3500001430511475]	2097	68.28%	1722	375	17.88%	4.59
8	(7.3500001430511475, 8.550000190734863]	1478	81.96%	1313	165	11.16%	7.96
9	(8.550000190734863, 9.449999809265137]	754	88.95%	657	97	12.86%	6.77
10	(9.449999809265137, inf]	1194	100.00%	1106	88	7.37%	12.57
合计		10801		8453	2348	21.74%	3.60

income_grade

组号	Value	Tot. Records	累计占比	#Goods	#Bads	Bad Rate	G/B Odds
1	(-inf, 3.5]	1211	11.21%	743	468	38.65%	1.59
2	(3.5, 4.5]	2510	34.45%	1766	744	29.64%	2.37
3	(4.5, 5.5]	3361	65.57%	2689	672	19.99%	4.00
4	(5.5, 6.5]	2503	88.74%	2150	353	14.10%	6.09
5	(6.5, inf]	1216	100.00%	1105	111	9.13%	9.95
合计		10801		8453	2348	21.74%	3.60

repay_amt_6m_grade

组号	Value	Tot. Records	累计占比	#Goods	#Bads	Bad Rate	G/B Odds
1	(-inf, 3.5]	1647	15.25%	1047	600	36.43%	1.75
2	(3.5, 4.5]	2268	36.25%	1634	634	27.95%	2.58
3	(4.5, 5.5]	2991	63.94%	2384	607	20.29%	3.93
4	(5.5, 6.5]	2286	85.10%	1944	342	14.96%	5.68
5	(6.5, inf]	1609	100.00%	1444	165	10.25%	8.75
合计		10801		8453	2348	21.74%	3.60

query_cnt_3m

组号	Value	Tot. Records	累计占比	#Goods	#Bads	Bad Rate	G/B Odds
1	(-inf, 0.5]	550	5.09%	288	262	47.64%	1.10
2	(0.5, 1.5]	1123	15.49%	724	399	35.53%	1.81
3	(1.5, 2.5]	1627	30.55%	1151	476	29.26%	2.42
4	(2.5, 3.5]	1785	47.08%	1384	401	22.46%	3.45
5	(3.5, 4.5]	1665	62.49%	1365	300	18.02%	4.55
6	(4.5, 5.5]	1347	74.97%	1127	220	16.33%	5.12
7	(5.5, 6.5]	1026	84.46%	897	129	12.57%	6.95
8	(6.5, 8.5]	1098	94.63%	970	128	11.66%	7.58
9	(8.5, inf]	580	100.00%	547	33	5.69%	16.58
合计		10801		8453	2348	21.74%	3.60

bank_997_cnt_3m

组号	Value	Tot. Records	累计占比	#Goods	#Bads	Bad Rate	G/B Odds
1	(-inf, 0.5]	3447	31.91%	2899	548	15.90%	5.29
2	(0.5, 1.5]	3739	66.53%	2928	811	21.69%	3.61
3	(1.5, 2.5]	2224	87.12%	1655	569	25.58%	2.91
4	(2.5, inf]	1391	100.00%	971	420	30.19%	2.31
合计		10801		8453	2348	21.74%	3.60

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
risk_score_v2	0.6912
avg_tnr	0.1411
income_grade	0.0814
bank_997_cnt_3m	0.0689
cust_seg2	0.0659
query_cnt_3m	0.0632
repay_amt_6m_grade	0.0605

区分度验证 — LR + WOE 评分卡 @ 跨时间样本

KS = 0.3426，GINI = 0.4576

组号	分数区间(PD)	样本数	累计占比	Good	Bad	累计Good占比	累计Bad占比	分组坏率	累计坏率	分组Odds	累计Odds
1	0.0437 ~ 0.0598	600	10.00%	578	22	12.50%	1.60%	3.67%	3.67%	26.27	26.27
2	0.0600 ~ 0.0910	600	20.00%	544	56	11.76%	5.67%	9.33%	6.50%	9.71	14.38
3	0.0910 ~ 0.1119	600	30.00%	527	73	11.40%	10.97%	12.17%	8.39%	7.22	10.92
4	0.1120 ~ 0.1300	600	40.00%	501	99	10.83%	18.17%	16.50%	10.42%	5.06	8.60
5	0.1301 ~ 0.1575	600	50.00%	516	84	11.16%	24.27%	14.00%	11.13%	6.14	7.98
6	0.1575 ~ 0.2072	600	60.00%	467	133	10.10%	33.94%	22.17%	12.97%	3.51	6.71
7	0.2076 ~ 0.2579	600	70.00%	435	165	9.41%	45.93%	27.50%	15.05%	2.64	5.65
8	0.2580 ~ 0.3281	600	80.00%	418	182	9.04%	59.16%	30.33%	16.96%	2.30	4.90
9	0.3282 ~ 0.4824	600	90.00%	361	239	7.81%	76.53%	39.83%	19.50%	1.51	4.13
10	0.4824 ~ 0.6655	600	100.00%	277	323	5.99%	100.00%	53.83%	22.93%	0.86	3.36
TOTALS		6000		4624	1376			22.93%		—	3.36

测试情景2：跨时间样本剔除一半好样本

指标	数值
样本量	6000 → 3688（剔除好样本 2312）
KS	0.3372
AUC	0.7250
GINI	0.4500

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。