# Synthesizing Individual Consumers' Credit Historical Data Using Generative Adversarial Networks
## 4 Results
### Data Consistency  
For data fidelity, we compared the percentage of overdue by sector, loan type, number of credit cards, and its holding period at the end of the years from 2016 to 2018. The left column of Figure 7 shows the delinquency rate by sector at the end of each year. The balances are relatively well maintained. The delinquency rate is quite higher than the original data; however, the synthetic dataset reflects the overall trends throughout the outstanding period. Sectors 17 and 21 are indicating third-tier financial institutions and savings banks, respectively. It seems to be that the number of unsecured loans made by third-tier institutions is relatively small and, therefore, difficult to predict its probability distribution more precisely. The right column of Figure 7 shows the delinquency rate by loan types at the end of each year from 2016 to 2018, except for the loan type 170 and 510, which are loans for a lump-sum deposit for housing and used car mortgage, respectively, their trends are consistent with each other. Although the rate is slightly higher than the original dataset, the synthetic dataset replicates the overall trends throughout the outstanding period. As seen in Figures 8 and 9, the delinquency rates by the credit card holding period and the number of credit cards, respectively, from the synthetic dataset are higher than the original dataset. However, the overall trends are consistent with the original dataset. Figure 7: Delinquency rate by SECTOR ID and Loan Type at the end of each year from 2016–2018. (* indicates over the 5%-point difference between original and synthetic dataset). We found that the delinquency rates are exaggerated throughout the period from 2016 to 2018, no matter under which condition. It seems to account for that the risk of delinquency is low in the early period and increasing throughout the whole period. We assume that the algorithm might have highlighted such features, ignoring rare cases, and emphasizing the regular ones. In addition to this, we tried to evaluate the synthetic dataset using Logistic Regression, Decision Tree, and Support Vector Machine algorithms. Although those models are not generally used standalone to forecast or classify, for the scope of this study is to generate a dataset preserving the statistical properties of the original data, comparing the datasets using the above standalone models would suffice to say that the two datasets agree at a reasonable level. We created new columns--the number and amount of loans, non-bank loans, and collateral-free loans--for each car owner for predictor variables, as listed in Table 3, since creditors, or models, should not discriminate against borrowers on a loan application or interest rates based on one's characteristics such as age, sex, or race. The results show that the algorithm's performances on original and synthetic datasets are considerably close enough that the synthetic dataset can be used as training data as in Figure 9.  
Figure 8: The delinquency rate by the credit card holding period in months at the end of the years from 2016 to 2018. Figure 9: The delinquency rate by the number of holding credit cards at the end of the years from 2016 to 2018. Table 4 shows the confusion matrices from the forementioned standalone machine learning algorithms. For each model, a confusion matrix with two rows and two columns reporting the number of true negative, false positive, true positive, and false negative, respectively from top left, clockwise. The synthetic dataset tends to yield more correct prediction on the loans that are not delinquent while yielding more wrong prediction on the loans that are delinquent than the original data. It indicates that the synthetic data has selected and generated the features of non-delinquent loans better than it has on the features of delinquent loans. Finding a significant difference between two confusion matrices could be not only difficult, but risky as uninformative as it can be.