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_20261010-160711-f53e
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	ITE-mv249p2s-df95

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 — 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

avg_tnr — IV 0.1073，7 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, 5.950000047683716]	1583	1247	336	21.23%	0.0069
6	(5.950000047683716, 7.3500001430511475]	2097	1722	375	17.88%	0.0646
7	(7.3500001430511475, inf]	3426	3076	350	10.22%	0.2859

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

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

data_flag — IV 0.0000，1 箱

分组号	分栏	Total	Good	Bad	Bad Rate	WOE
1	TRAIN	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 17 个变量，共 17 个；完整数据见 feature_report.json。

04 单变量分析（粗分箱）

risk_score — 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 — 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

avg_tnr — 7 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, 5.950000047683716]	1583	1247	336	21.23%	0.0069
6	(5.950000047683716, 7.3500001430511475]	2097	1722	375	17.88%	0.0646
7	(7.3500001430511475, inf]	3426	3076	350	10.22%	0.2859

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

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	bh_a001、risk_score_v2
白名单	risk_score

候选变量 19，成功分箱 17，入模 8

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	白名单	Y	0.1272	PASS	PASS	—	—	0.0037
ScoreCust2	—	N	0.1191	PASS	PASS	PASS	FAIL	0.0026	与 ScoreCust2_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	—	—	—	—	—	—	黑名单
bh_a009	—	N	0.0991	PASS	PASS	PASS	FAIL	0.0012	与 ScoreCust2_v2 相关>0.7
bank_997_cnt_3m	—	Y	0.0246	PASS	PASS	PASS	PASS	0.0010
avg_tnr	—	Y	0.1073	PASS	PASS	PASS	PASS	0.0014
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	—	N	—	—	—	—	—	—	黑名单
ScoreCust2_v2	—	Y	0.1262	PASS	PASS	PASS	PASS	0.0020
data_flag	—	N	0.0000	PASS	FAIL	—	—	—	集中度>0.9

06 回归结果

硬门槛：P≥0.1 / VIF>4.0 迭代剔除，共 3 个变量被剔除（4 轮）：query_cnt_3m、cust_seg2、repay_amt_6m_grade。

Parameter	Label	Estimate(无惩罚)	Standard Error	Wald Chi-Square	Pr > ChiSq	Standardized Estimate
Intercept	Intercept	-1.3241	0.0278	2269.54	<0.0001	—
WOE_risk_score	risk_score	-4.3297	0.3692	137.53	<0.0001	-0.6004
WOE_ScoreCust2_v2	ScoreCust2_v2	-2.0345	0.3468	34.41	<0.0001	-0.2995
WOE_avg_tnr	avg_tnr	-0.5824	0.2002	8.46	0.0036	-0.0985
WOE_income_grade	income_grade	-0.4517	0.2269	3.96	0.0465	-0.0568
WOE_bank_997_cnt_3m	bank_997_cnt_3m	-0.6452	0.2972	4.71	0.0299	-0.0576

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	4.66709
ScoreCust2_v2	4.32246
avg_tnr	1.73573
income_grade	1.34257
bank_997_cnt_3m	1.07160

	risk_score	ScoreCust2_v2	avg_tnr	income_grade	bank_997_cnt_3m
risk_score	1.000	0.872	0.640	0.497	0.252
ScoreCust2_v2	0.872	1.000	0.615	0.476	0.245
avg_tnr	0.640	0.615	1.000	0.344	0.174
income_grade	0.497	0.476	0.344	1.000	0.152
bank_997_cnt_3m	0.252	0.245	0.174	0.152	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	(-inf, 492.5]	0.2165	-4.0120	130
	(492.5, 519.5]	0.1696	-4.0120	125
	(519.5, 535.5]	0.1000	-4.0120	117
	(535.5, 546.5]	0.0648	-4.0120	113
	(546.5, 564.5]	0.0779	-4.0120	114
	(564.5, 587.5]	-0.0112	-4.0120	104
	(587.5, 600.5]	-0.0507	-4.0120	99
	(600.5, 616.5]	-0.0859	-4.0120	95
	(616.5, 643.5]	-0.1917	-4.0120	83
	(643.5, inf]	-0.2638	-4.0120	74
ScoreCust2_v2	(-inf, 507.5]	0.2546	-2.1573	121
	(507.5, 522.5]	0.1235	-2.1573	113
	(522.5, 536.5]	0.1205	-2.1573	113
	(536.5, 544.5]	0.0567	-2.1573	109
	(544.5, 553.5]	0.0237	-2.1573	106
	(553.5, 565.5]	0.0007	-2.1573	105
	(565.5, 583.5]	-0.0737	-2.1573	100
	(583.5, 593.5]	-0.0766	-2.1573	100
	(593.5, 606.5]	-0.1231	-2.1573	97
	(606.5, inf]	-0.2861	-2.1573	87
avg_tnr	(-inf, 2.149999976158142]	-0.2194	-0.6596	101
	(2.149999976158142, 3.350000023841858]	-0.1276	-0.6596	103
	(3.350000023841858, 4.450000047683716]	-0.0706	-0.6596	104
	(4.450000047683716, 4.8500001430511475]	-0.0083	-0.6596	105
	(4.8500001430511475, 5.950000047683716]	0.0069	-0.6596	105
	(5.950000047683716, 7.3500001430511475]	0.0646	-0.6596	106
	(7.3500001430511475, inf]	0.2859	-0.6596	110
income_grade	(-inf, 3.5]	-0.1736	-0.4927	103
	(3.5, 4.5]	-0.1417	-0.4927	103
	(4.5, 5.5]	0.0398	-0.4927	106
	(5.5, 6.5]	0.1484	-0.4927	107
	(6.5, inf]	0.1419	-0.4927	107
bank_997_cnt_3m	(-inf, 0.5]	0.1392	-0.6349	108
	(0.5, 1.5]	0.0012	-0.6349	105
	(1.5, 2.5]	-0.0648	-0.6349	104
	(2.5, inf]	-0.0990	-0.6349	103

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	0.3782	0.7551	0.5102
LR + WOE 评分卡	测试样本	0.3709	0.7474	0.4948
LR + WOE 评分卡	训练+测试样本	0.3731	0.7520	0.5040
LR + WOE 评分卡	跨时间验证样本	0.3430	0.7325	0.4650
LR + WOE 评分卡	PSI时点样本	0.3809	0.7494	0.4988

LR + WOE 评分卡 — 4 样本评估表（score_cut 粒度）

开发样本（KS 0.3782，AUC 0.7551）

cuts	cnt	bad	good	bad_rate	population_pct	cum_bad_rate	cum_good_rate
(0.0438, 0.0551]	1095	44	1051	4.02%	10.14%	1.87%	12.43%
(0.0553, 0.0807]	1066	64	1002	6.00%	9.87%	4.60%	24.29%
(0.0808, 0.1098]	1082	103	979	9.52%	10.02%	8.99%	35.87%
(0.1099, 0.1354]	1080	130	950	12.04%	10.00%	14.52%	47.11%
(0.1355, 0.1654]	1078	177	901	16.42%	9.98%	22.06%	57.77%
(0.1654, 0.2127]	1080	204	876	18.89%	10.00%	30.75%	68.13%
(0.2128, 0.2598]	1085	275	810	25.35%	10.05%	42.46%	77.71%
(0.2600, 0.3228]	1075	300	775	27.91%	9.95%	55.24%	86.88%
(0.3230, 0.4710]	1081	425	656	39.32%	10.01%	73.34%	94.64%
(0.4713, 0.6577]	1079	626	453	58.02%	9.99%	100.00%	100.00%

测试样本（KS 0.3709，AUC 0.7474）

cuts	cnt	bad	good	bad_rate	population_pct	cum_bad_rate	cum_good_rate
(0.0438, 0.0551]	723	28	695	3.87%	10.04%	1.79%	12.33%
(0.0567, 0.0789]	717	54	663	7.53%	9.96%	5.24%	24.10%
(0.0790, 0.1095]	720	65	655	9.03%	10.00%	9.40%	35.72%
(0.1096, 0.1339]	720	98	622	13.61%	10.00%	15.66%	46.76%
(0.1339, 0.1610]	720	113	607	15.69%	10.00%	22.89%	57.53%
(0.1610, 0.2107]	731	139	592	19.02%	10.15%	31.78%	68.04%
(0.2110, 0.2596]	709	165	544	23.27%	9.85%	42.33%	77.69%
(0.2597, 0.3216]	722	240	482	33.24%	10.03%	57.67%	86.25%
(0.3222, 0.4609]	719	254	465	35.33%	9.99%	73.91%	94.50%
(0.4613, 0.6577]	718	408	310	56.82%	9.97%	100.00%	100.00%

训练+测试样本（KS 0.3731，AUC 0.7520）

cuts	cnt	bad	good	bad_rate	population_pct	cum_bad_rate	cum_good_rate
(0.0438, 0.0551]	1818	72	1746	3.96%	10.10%	1.84%	12.39%
(0.0553, 0.0793]	1784	120	1664	6.73%	9.91%	4.91%	24.20%
(0.0796, 0.1098]	1799	167	1632	9.28%	9.99%	9.18%	35.79%
(0.1098, 0.1346]	1799	225	1574	12.51%	9.99%	14.93%	46.96%
(0.1346, 0.1638]	1800	291	1509	16.17%	10.00%	22.37%	57.67%
(0.1638, 0.2118]	1802	340	1462	18.87%	10.01%	31.06%	68.05%
(0.2119, 0.2598]	1801	443	1358	24.60%	10.01%	42.38%	77.69%
(0.2598, 0.3225]	1798	541	1257	30.09%	9.99%	56.21%	86.61%
(0.3226, 0.4671]	1801	681	1120	37.81%	10.01%	73.62%	94.56%
(0.4672, 0.6577]	1798	1032	766	57.40%	9.99%	100.00%	100.00%

跨时间验证样本（KS 0.3430，AUC 0.7325）

cuts	cnt	bad	good	bad_rate	population_pct	cum_bad_rate	cum_good_rate
(0.0438, 0.0569]	616	23	593	3.73%	10.27%	1.67%	12.82%
(0.0571, 0.0810]	593	49	544	8.26%	9.88%	5.23%	24.59%
(0.0810, 0.1104]	591	72	519	12.18%	9.85%	10.47%	35.81%
(0.1105, 0.1358]	600	81	519	13.50%	10.00%	16.35%	47.04%
(0.1359, 0.1665]	601	111	490	18.47%	10.02%	24.42%	57.63%
(0.1666, 0.2127]	600	134	466	22.33%	10.00%	34.16%	67.71%
(0.2130, 0.2605]	602	159	443	26.41%	10.03%	45.71%	77.29%
(0.2606, 0.3226]	597	183	414	30.65%	9.95%	59.01%	86.25%
(0.3227, 0.4681]	602	242	360	40.20%	10.03%	76.60%	94.03%
(0.4682, 0.6577]	598	322	276	53.85%	9.97%	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	0.3782	0.5102	0.7551
测试样本	0.3709	0.4948	0.7474
训练+测试样本	0.3731	0.5040	0.7520
跨时间验证样本	0.3430	0.4650	0.7325
PSI时点样本	0.3809	0.4988	0.7494

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	0.0012	0.0011	—	0.0015

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,0.0551]	9.76%	9.27%	0.0003	0.0003
(0.0551,0.0807]	10.21%	10.63%	0.0002	0.0004
(0.0807,0.1098]	9.95%	9.65%	0.0001	0.0005
(0.1098,0.1354]	10.06%	10.37%	0.0001	0.0006
(0.1354,0.1654]	10.01%	9.72%	0.0001	0.0007
(0.1654,0.2127]	10.01%	10.38%	0.0001	0.0008
(0.2127,0.2598]	9.98%	9.63%	0.0001	0.0010
(0.2598,0.3228]	10.00%	10.38%	0.0001	0.0011
(0.3228,0.471]	10.01%	10.12%	0.0000	0.0011
(0.471,inf]	10.01%	9.85%	0.0000	0.0011

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,0.0551]	9.76%	9.47%	0.0001	0.0001
(0.0551,0.0807]	10.21%	10.40%	0.0000	0.0001
(0.0807,0.1098]	9.95%	10.17%	0.0000	0.0002
(0.1098,0.1354]	10.06%	10.45%	0.0001	0.0003
(0.1354,0.1654]	10.01%	10.03%	0.0000	0.0003
(0.1654,0.2127]	10.01%	10.38%	0.0001	0.0005
(0.2127,0.2598]	9.98%	9.43%	0.0003	0.0008
(0.2598,0.3228]	10.00%	9.25%	0.0006	0.0013
(0.3228,0.471]	10.01%	10.07%	0.0000	0.0013
(0.471,inf]	10.01%	10.35%	0.0001	0.0015

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]	1054	9.76%	709	9.85%	1763	9.79%	556	9.27%	568	9.47%
(0.0551, 0.0807]	1103	10.21%	745	10.35%	1848	10.27%	638	10.63%	624	10.40%
(0.0807, 0.1098]	1075	9.95%	715	9.93%	1790	9.94%	579	9.65%	610	10.17%
(0.1098, 0.1354]	1087	10.06%	761	10.57%	1848	10.27%	622	10.37%	627	10.45%
(0.1354, 0.1654]	1081	10.01%	722	10.03%	1803	10.02%	583	9.72%	602	10.03%
(0.1654, 0.2127]	1081	10.01%	714	9.92%	1795	9.97%	623	10.38%	623	10.38%
(0.2127, 0.2598]	1078	9.98%	678	9.42%	1756	9.76%	578	9.63%	566	9.43%
(0.2598, 0.3228]	1080	10.00%	730	10.14%	1810	10.06%	623	10.38%	555	9.25%
(0.3228, 0.471]	1081	10.01%	750	10.42%	1831	10.17%	607	10.12%	604	10.07%
(0.471, inf]	1081	10.01%	675	9.38%	1756	9.76%	591	9.85%	621	10.35%
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	0.0037	稳定
ScoreCust2_v2	0.0020	稳定
avg_tnr	0.0014	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 — PSI 0.0037

分栏	开发基准比例	PSI时点样本比例	变更	贡献
(-inf, 492.5]	11.84%	10.97%	-0.87%	0.0007
(492.5, 519.5]	11.81%	12.98%	1.17%	0.0011
(519.5, 535.5]	9.55%	9.55%	-0.00%	0.0000
(535.5, 546.5]	7.42%	8.02%	0.60%	0.0005
(546.5, 564.5]	12.50%	12.57%	0.07%	0.0000
(564.5, 587.5]	15.78%	14.98%	-0.79%	0.0004
(587.5, 600.5]	8.05%	7.53%	-0.52%	0.0003
(600.5, 616.5]	7.38%	6.93%	-0.45%	0.0003
(616.5, 643.5]	8.84%	9.43%	0.59%	0.0004
(643.5, inf]	6.82%	7.03%	0.21%	0.0001

ScoreCust2_v2 — PSI 0.0020

分栏	开发基准比例	PSI时点样本比例	变更	贡献
(-inf, 507.5]	15.27%	15.12%	-0.15%	0.0000
(507.5, 522.5]	10.03%	9.77%	-0.26%	0.0001
(522.5, 536.5]	11.90%	12.98%	1.09%	0.0009
(536.5, 544.5]	7.31%	7.58%	0.27%	0.0001
(544.5, 553.5]	8.88%	8.73%	-0.15%	0.0000
(553.5, 565.5]	11.41%	11.03%	-0.37%	0.0001
(565.5, 583.5]	14.92%	14.18%	-0.73%	0.0004
(583.5, 593.5]	6.06%	6.55%	0.49%	0.0004
(593.5, 606.5]	5.93%	5.82%	-0.11%	0.0000
(606.5, inf]	8.30%	8.23%	-0.07%	0.0000

avg_tnr — PSI 0.0014

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, 5.950000047683716]	14.66%	14.48%	-0.17%	0.0000
(5.950000047683716, 7.3500001430511475]	19.41%	18.15%	-1.26%	0.0009
(7.3500001430511475, inf]	31.72%	32.87%	1.15%	0.0004

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

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

组号	Value	Tot. Records	累计占比	#Goods	#Bads	Bad Rate	G/B Odds
1	(-inf, 492.5]	1279	11.84%	1232	47	3.67%	26.21
2	(492.5, 519.5]	1276	23.66%	1182	94	7.37%	12.57
3	(519.5, 535.5]	1032	33.21%	914	118	11.43%	7.75
4	(535.5, 546.5]	801	40.63%	694	107	13.36%	6.49
5	(546.5, 564.5]	1350	53.12%	1144	206	15.26%	5.55
6	(564.5, 587.5]	1704	68.90%	1320	384	22.54%	3.44
7	(587.5, 600.5]	870	76.96%	626	244	28.05%	2.57
8	(600.5, 616.5]	797	84.33%	531	266	33.38%	2.00
9	(616.5, 643.5]	955	93.18%	529	426	44.61%	1.24
10	(643.5, inf]	737	100.00%	281	456	61.87%	0.62
合计		10801		8453	2348	21.74%	3.60

ScoreCust2_v2

组号	Value	Tot. Records	累计占比	#Goods	#Bads	Bad Rate	G/B Odds
1	(-inf, 507.5]	1649	15.27%	1574	75	4.55%	20.99
2	(507.5, 522.5]	1083	25.29%	979	104	9.60%	9.41
3	(522.5, 536.5]	1285	37.19%	1138	147	11.44%	7.74
4	(536.5, 544.5]	790	44.51%	677	113	14.30%	5.99
5	(544.5, 553.5]	959	53.38%	776	183	19.08%	4.24
6	(553.5, 565.5]	1232	64.79%	965	267	21.67%	3.61
7	(565.5, 583.5]	1611	79.71%	1171	440	27.31%	2.66
8	(583.5, 593.5]	655	85.77%	432	223	34.05%	1.94
9	(593.5, 606.5]	640	91.70%	370	270	42.19%	1.37
10	(606.5, inf]	897	100.00%	371	526	58.64%	0.71
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, 5.950000047683716]	1583	48.87%	1247	336	21.23%	3.71
6	(5.950000047683716, 7.3500001430511475]	2097	68.28%	1722	375	17.88%	4.59
7	(7.3500001430511475, inf]	3426	100.00%	3076	350	10.22%	8.79
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

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	0.6004
ScoreCust2_v2	0.2995
avg_tnr	0.0985
bank_997_cnt_3m	0.0576
income_grade	0.0568

区分度验证 — LR + WOE 评分卡 @ 跨时间样本

KS = 0.3430，GINI = 0.4650

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	0.0438 ~ 0.0569	591	9.85%	570	21	12.33%	1.53%	3.55%	3.55%	27.14	27.14
2	0.0569 ~ 0.0809	608	19.98%	558	50	12.07%	5.16%	8.22%	5.92%	11.16	15.89
3	0.0810 ~ 0.1104	601	30.00%	528	73	11.42%	10.47%	12.15%	8.00%	7.23	11.50
4	0.1105 ~ 0.1358	600	40.00%	519	81	11.22%	16.35%	13.50%	9.38%	6.41	9.67
5	0.1359 ~ 0.1664	599	49.98%	488	111	10.55%	24.42%	18.53%	11.20%	4.40	7.93
6	0.1665 ~ 0.2126	599	59.97%	465	134	10.06%	34.16%	22.37%	13.06%	3.47	6.66
7	0.2127 ~ 0.2605	601	69.98%	442	159	9.56%	45.71%	26.46%	14.98%	2.78	5.68
8	0.2605 ~ 0.3226	601	80.00%	418	183	9.04%	59.01%	30.45%	16.92%	2.28	4.91
9	0.3227 ~ 0.4671	596	89.93%	355	241	7.68%	76.53%	40.44%	19.51%	1.47	4.12
10	0.4681 ~ 0.6577	604	100.00%	281	323	6.08%	100.00%	53.48%	22.93%	0.87	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	0.3344
AUC	0.7295
GINI	0.4590

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。