# A Deep Learning Approach with Feature Derivation and Selection for Overdue Repayment Forecasting
## 4 Experiments  
### Dataset  
Customers normally submit their fundamental information, such as identity information, wealth information and credit records, when they borrow money from online lending platforms. Besides, online lending platforms may record transaction details when loan behaviors are continued.  
We present a new dataset in this paper, which is available to the public ([[https://github.com/zjersey/payment_overdue_dataset](https://github.com/zjersey/payment_overdue_dataset)]([https://github.com/zjersey/payment_overdue_dataset](https://github.com/zjersey/payment_overdue_dataset))). The real-world dataset was provided by a company in Shanghai and collected from the two situations above. The practical situation is that of an online lending platform that is used by a large number of borrowers and lenders. Borrowers and lenders can engage in loan transactions with each other under the control of the platform. Our dataset is sampled from the transaction records of the platform.  
Each piece of data represents a record of a repayment transaction from a borrower to a lender. Our dataset contains 1,048,575 transaction records and 85,236 borrowers are involved, with an average of 12.3 repayment behaviors for each borrower. The maximal number of repayment records for a borrower is 20. The number of borrowers with a different number of records (ranging from 1 to 20) was counted, and the proportion is illustrated in Figure 4. Since the length of the behavior sequence in our dataset is not particularly long and there are borrowers with few records, some long-sequence modeling based methods [6; 19] would not perform well for our dataset.  
As a borrower might have transactions with multiple lenders at the same time, the repayment behavior does not have a regular frequency in our dataset. Besides, repayment behaviors between precise borrower-to-lender matches may be small in number. Therefore, our proposed approaches focus on modeling the historical records from the borrower level instead of the borrower-to-lender level.  
Each transaction record contains 64 engineering features, which can be divided into three categories based on content:  
* Customer information (after data masking): borrowers' unique identification, industry, etc. Note that data masking has been applied to protect the privacy of customers.
* Dynamic repayment records: obliged repayment amount, obliged repayment time, overdue days, etc.
* Background information: expense ratio, loan type, order scenario, etc.  
The most meaningful information lies in the dynamic features. Each repayment transaction record has a due date, due amount of money, actual repayment date and actual repayment amount. The transaction record is identified as an overdue repayment if the actual repayment date is later than the due date or the actual repayment amount is less than the due amount. The background and identity features provide some auxiliary information. Although there are 64 features in total, some of them are  
Figure 4: Statistical illustration of the proportion of borrowers with different numbers of repayment records.  
meaningless or have a strong correlation. Therefore, the workable features after feature screening are limited in number, which makes feature engineering essential and challenging.