# Synthesizing Individual Consumers' Credit Historical Data Using Generative Adversarial Networks
## 4 Results
### Data Consistency
A statistical difference might advise one cell of the matrix is different but does not provide enough information which metric or metrics are different. Therefore, we used standard approach that uses a single value metric to reduce each matrix into one value, and then to compare the values. Table 4 compares \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Data** & **Actual** & **Predicted 0** & **Predicted 1** & **Total** \\ \hline \multirow{4}{*}{Logistic Regression} & \multirow{4}{*}{Original} & Actual 0 & 709 & 191 & 900 \\  & & Actual 1 & 255 & 632 & 887 \\  & & Total & 964 & 823 & 1787 \\  & & Actual 0 & 771 & 129 & 900 \\  & & Actual 1 & 321 & 566 & 887 \\  & & Total & 1092 & 695 & 1787 \\ \hline \multirow{4}{*}{Decision Tree} & \multirow{4}{*}{Synthetic} & Actual 0 & 675 & 225 & 900 \\  & & Actual 1 & 227 & 660 & 887 \\  & & Total & 902 & 885 & 1787 \\  & & Actual 0 & 629 & 271 & 900 \\  & & Actual 1 & 281 & 606 & 887 \\  & & Total & 910 & 877 & 1787 \\ \hline \multirow{4}{*}{LDA (Latent Dirichlet Allocation)} & \multirow{4}{*}{Original} & Actual 0 & 770 & 130 & 900 \\  & & Actual 1 & 287 & 600 & 887 \\  & & Total & 1057 & 730 & 1787 \\  & & Actual 0 & 769 & 131 & 900 \\  & & Actual 1 & 328 & 559 & 887 \\  & & Total & 1097 & 690 & 1787 \\ \hline \multirow{4}{*}{Support Vector Machine} & \multirow{4}{*}{Original} & Actual 0 & 732 & 168 & 900 \\  & & Actual 1 & 227 & 660 & 887 \\ \cline{1-1}  & & Total & 959 & 828 & 1787 \\ \cline{1-1}  & & Actual 0 & 771 & 129 & 900 \\ \cline{1-1}  & & Actual 1 & 305 & 582 & 887 \\ \cline{1-1}  & & Total & 1076 & 711 & 1787 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrices for original and synthetic data. \begin{table}
\begin{tabular}{c c} \hline \hline
**Column** & **Description** \\ \hline num\_NBLN\_COM & Number of non-bank institutions \\ num\_NCLN\_COM & Number of institutions made unsecured loans \\ MAX\_LN\_DUR & The maximum loan outstanding period \\ MIN\_LN\_DUR & The minimum loan outstanding period \\ num\_LN & Number of total loans outstanding \\ amt\_LN & Total amount of loans outstanding \\ num\_NBLN & Number of non-bank loans \\ amt\_NBLN & Amount of non-bank loans \\ num\_NCLN & Number of unsecured loans \\ amt\_NCLN & Amount of unsecured loans \\ dimq & 0 = Not delinquent loan, 1 = Delinquent loan \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Predictor variables to forecast delinquency. the same model on different datasets, the original and synthetic data, using precision, recall, and F1 metrics. Not to mention that we are not trying to compare the performance of the algorithms, but how algorithms work on two different datasets and check if they yield similar performance. Precision is the ratio of the actual delinquent loans to the loans that are predicted to be delinquent. Once a loan is predicted to be delinquent, the agency should start managing it by reminding the borrowers or offering other payment methods with lower interest rates, which are costly. Recall measures how many of the loans that are delinquent over the loans that are predicted not to be delinquent. Although this seems to be the situation most agencies want to avoid, higher recall does not always come before higher precision in this case for banks would not want to miss customers by rejecting a loan. To get a tradeoff between precision and recall, we use their harmonic mean, or F1-Score. In Table 5, the F1-Score is no more than 5% point in all models for each category, 0 and 1. We conclude that the synthetic data could be used for training in developing machine learning models without distortion of reality.