A Credit Risk Model with Small Sample Data Based on G-XGBoost

Figures & data

Figure 1. The basic structure of GAN.

Table 1. The confusion matrix

	Test outcome-Positive	Test outcome- Negative	Total
Condition-Positive	True Positive (TP)	False Negative (FN)	TP+FN
Condition-Negative	False Positive (FP)	True Negative (TN)	FP+TN
Total	TP+FP	FN+TN	TP+FP+FN+TN

Figure 2. Model evaluation – KS diagram and ROC diagram.

Figure 3. Flow chart of model construction.

Table 2. The influencing variable

Category	Variable
Enterprise basic information	industry, number of employees, length of existence, industrial and commercial registration status, registered capital, etc
Enterprise equity information	legal person, changes in shareholders, borrowing enterprise equity ratio, etc
Enterprise judicial information	litigation and bad records, such as enterprise breach of faith, execution, court notice, judgment documents, etc
Enterprise related party information	related party quantity, related party operation and related lawsuit, etc
Enterprise business information	financial information, such as enterprise quick ratio, operating margin, total assets turnover, accounts receivable turnover, return on total assets, etc
Enterprise credit information	credit history, current liabilities, contingent liabilities, credit demand, default and overdue situation, etc
Legal person basic information	age, education, sex, marital status, etc
Legal person assets information	deposits and other financial products, etc
Legal person debt information	loan type, loan date, loan amount, etc
Legal person judicial information	litigation and bad record, etc
Legal person credit information	credit history, current liabilities, contingent liabilities, credit demand, default and overdue, etc
Macroeconomic information	GDP, CPI, PPI, etc

Figure 4. The distribution of the amplified data is consistent with the original data.

Figure 5. KS under different amplified sample thresholds.

Figure 6. AUC under different amplified sample thresholds.

Table 3. The KS and AUC value of different model

	KS of XGBoost	AUC of XGBoost	KS of G-XGBoost	AUC of G-XGBoost
Group1	0.3490	0.7429	0.3968	0.7499
Group2	0.3841	0.7508	0.4042	0.7491
Group3	0.3623	0.7534	0.3903	0.7564
Group4	0.3618	0.7342	0.3664	0.7354
Average	0.3643	0.7453	0.3894	0.7477

Table 4. The statistics of Model 1 validation sets

Level	Predicted values	Good	Bad	Good%	Bad%	Bad ratio	KS
1	>0.581	17	33	4.8%	22.2%	66%	17.30%
2	0.477–0.581	24	26	6.8%	17.5%	52%	27.92%
3	0.405–0.477	32	18	9.1%	12.1%	36%	30.88%
4	0.333–0.405	32	18	9.1%	12.1%	36%	33.84%
5	0.276–0.333	34	16	9.7%	10.7%	32%	34.90%
6	0.211–0.276	36	14	10.3%	9.4%	28%	34.04%
7	0.167–0.211	41	9	11.7%	6.0%	18%	28.39%
8	0.129–0.167	39	11	11.1%	7.4%	22%	24.67%
9	0.096–0.129	49	1	14.0%	0.7%	2%	11.38%
10	≤0.096	47	3	13.4%	2.0%	6%	0.00%
Total		351	149	100%	100%	29.8%

Figure 7. The prediction effect of Model 1 validation sets.

Table 5. The statistics of Model 2 validation sets

Level	Predicted values	Good	Bad	Good%	Bad%	Bad ratio	KS
1	>0.576	17	33	4.8%	22.2%	66%	17.30%
2	0.484–0.576	24	26	6.8%	17.5%	52%	27.92%
3	0.409–0.484	29	21	8.3%	14.1%	42%	33.75%
4	0.331–0.409	32	18	9.1%	12.1%	36%	36.71%
5	0.264–0.331	32	18	9.1%	12.1%	36%	39.68%
6	0.212–0.264	42	8	12.0%	5.4%	16%	33.08%
7	0.173–0.212	37	13	10.5%	8.7%	26%	31.26%
8	0.142–0.173	44	6	12.5%	4.0%	12%	22.75%
9	0.108–0.142	47	3	13.4%	2.0%	6%	11.38%
10	≤0.108	47	3	13.4%	2.0%	6%	0.00%
Total		351	149	100%	100%	29.8%

Figure 8. The prediction effect of Model 2 validation sets.

Figure 9. KS under only using bad samples to amplify the sample set.