# A Deep Learning Approach with Feature Derivation and Selection for Overdue Repayment Forecasting
## 1 Introduction  
With the development of the economy and the rising level of consumption in national standards of living, the majority of people and companies have encountered capital turnover problems and have therefore attempted to obtain a loan for consumption, capital turnover, investment, etc. Therefore, the demand for financial credit services is continuously increasing. Online lending is a convenient and fast micro-loan innovation finance mode. Platforms streamline the intermediate, tedious loan procedure, thus attracting increasing numbers of clients who intend to solve their financial difficulties. While online lending provides customers and platforms with an effective path towards a loan transaction, various fraudulent and insecurity factors emerge as well. Almost all credit businesses face problems such as long-term liabilities, loan delinquencies and overdue repayment, which pose great challenges for risk control in online lending. When overdue behavior occurs, financial institutions can make up for loss according to some collateral in traditional credit business. In contrast, manuallyhandling small monetary loans with high frequency is difficult in online lending. Once frauds such as overdue repayment occur, tracking accountability and recuperating loss become difficult problems. Repayment behaviors in online lending always accumulate in time to form a repayment behavior sequence. Analyzing the historical record of repayment behaviors can allow potential repayment behavior patterns to be discerned, thus predicting the occurrence of overdue repayment. In [1; 2], the authors developed machine learning models to analyze event records, but they ignored the sequential format in these records. Deep learning models, especially sequential neural networks, have achieved remarkable performance in event sequence-related tasks [3; 4; 5]. These kinds of models are well suited to handle the massive amount of online repayment behavior data. However, as only limited features can be collected in online lending, deep neural models may easily sink into an over-fitting problem. Feature derivation by manually designing new features is commonly utilized to enlarge the feature size, but relies on human expertise. In this paper, we publish a new dataset collected from a practical application scenario in online lending and propose a novel feature derivation and selection approach. Based on the dataset, we propose an overdue repayment forecasting method based on a fusion of deep learning models. Experiments demonstrate that our method outperforms various general machine learning models and neural network models. Furthermore, visualization and interpretability improvement work in our approach show the critical time points and causes of overdue repayment behavior. The contributions of our research can be summarized as follows:  
1. We present a new dataset that originates from a practical application scenario in online lending, which can be downloaded at [[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)). Over one million repayment records of 85,000 anonymous borrowers are contained, and all the sensitive information is encrypted for confidentiality. 2. An improved feature derivation and selection method is proposed that can generate extensive, fully-combined new features and select an arbitrary number of the most significant features based on a scorecard model. 3. We introduce deep learning models into the domain of risk control in online lending; specifically, overdue repayment forecasting based on historical repayment behaviors. Our proposed architecture, namely AD-BLSTM, combines a deep neural network (DNN), bidirectional long short-term memory (LSTM) [6] (BiLSTM) and the attention mechanism [7]. DNN and BiLSTM are used to learn from the static background information and dynamic event sequence, respectively, to maximize the superiority of the two networks.