# Synthesizing Individual Consumers' Credit Historical Data Using Generative Adversarial Networks
## 5 Conclusions  
We generated synthetic consumer credit data with the order of accounts as a condition. The results show that the univariate and multivariate distributions of the synthetic dataset are more peaked than the original dataset due to GMM. NMI comparison shows that the mutual dependence among the variables is similar. The prediction of delinquency \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Data** & **Actual** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline \multirow{4}{*}{Logistic Regression} & Original & Actual 0 & 0.74 & 0.79 & 0.76 & 900 \\  & & Actual 1 & 0.77 & 0.71 & 0.74 & 887 \\  & Synthetic & Actual 0 & 0.71 & 0.86 & 0.77 & 900 \\  & & Actual 1 & 0.81 & 0.64 & 0.72 & 887 \\ \hline \multirow{4}{*}{Decision Tree} & Original & Actual 0 & 0.75 & 0.75 & 0.75 & 900 \\  & & Actual 1 & 0.75 & 0.74 & 0.74 & 887 \\  & Synthetic & Actual 0 & 0.69 & 0.70 & 0.70 & 900 \\  & & Actual 1 & 0.69 & 0.68 & 0.69 & 887 \\ \hline \multirow{4}{*}{LDA (Latent Dirichlet Allocation)} & Original & Actual 0 & 0.73 & 0.86 & 0.79 & 900 \\  & & Actual 1 & 0.82 & 0.68 & 0.74 & 887 \\  & Synthetic & Actual 0 & 0.70 & 0.85 & 0.77 & 900 \\  & & Actual 1 & 0.81 & 0.63 & 0.71 & 887 \\ \hline \multirow{4}{*}{Support Vector Machine} & Original & Actual 0 & 0.76 & 0.81 & 0.79 & 900 \\  & & Actual 1 & 0.80 & 0.74 & 0.77 & 887 \\ \cline{1-1}  & Synthetic & Actual 0 & 0.72 & 0.86 & 0.78 & 900 \\ \cline{1-1} \cline{2-6}  & & Actual 1 & 0.82 & 0.66 & 0.73 & 887 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Forecast accuracy comparison. using standalone machine learning algorithms worked at a comparable level. Finally, the data fidelity shows that the overdue difference is within 5% points with consistent trends. Besides, the exposure risk as low as 0.05%, which would not deteriorate the sample, the synthetic consumer credit data generated using GAN could be a useful resource for big data training programs in financial sectors. It remains to note that this paper does not cover the individuals' accounts as the time-series data other than setting the order of accounts as a condition. In other words, we assumed that consumers' economic activities would show similar patterns such as the age range they first open an account or their first loan and its purpose. Future work should focus on clustering the samples into consumer groups to strengthen the assumption. Moreover, inspired by [PERSON] et al. (2019), we continue working on rearranging individuals' records as layers and generating compositional layer-based synthetic data ([PERSON] et al., 2019; [PERSON] et al., 2019). Another limitation of our study is that we consider the uniqueness of individuals' records as the exposure risk measure. As introduced in Jordan (2019), we plan to apply the differential privacy algorithm to the extent of a pre-determined threshold for disclosure risk and data post-process ([PERSON] et al., 2019; [PERSON] et al., 2019). Conceptualization, S.J.Y. and Y.H.G. ; methodology, N.P. ; validation, S.J.Y., Y.H.G. and N.P. ; writing--original draft preparation, N.P. ; writing--review and editing, S.J.Y. and Y.H.G. ; visualization, N.P. ; supervision, Y.H.G. ; project administration, S.J.Y. ; funding acquisition, Y.H.G. All authors have read and agreed to the published version of the manuscript. This research was supported by Institute for Information and communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) [No. 2017-0-00302, Development of Self Evolutionary AI Investing Technology] and [No. 2019-0-00136, Development of AI-Convergence Technologies for Smart City Industry Productivity Innovation]. Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Restrictions may apply to the availability of these data.