Multi-Class Credit Risk Analysis Using Deep Learning

Abstract

Credit risk prediction, reliability, monitoring and effective loan processing are the keys to proper bank decision-making. So, understanding the credit customer during the initial loan processing phase would help the bank prevent future losses. In this regard, this study aims to develop a credit risk evaluation model using deep learning algorithms. The model utilizes a credit risk analysis dataset published in Kaggle. The objective is to build deep learning models for predicting credit risk using real banking datasets published on Kaggle. Firstly, data preprocessing and feature engineering are done. Suitable features such as irrelevant and null valued features are identified and removed with techniques like the Karl Pearson correlation, information values, and weight of evidence. Next, data normalization is performed and target features are separated into three classes: high risk, medium risk and low risk. SMOTE-ENN (Synthetic Minority Oversampling Technique with Edited Nearest Neighbor) was applied to balance the dataset. State-of-the-art deep learning algorithms such as GRU (Gated Recurrent Units) Model and Bidirectional Long Short-Term Memory (Bi-LSTM) are implemented to train and learn from the pre-processed data. GRU and Bi-LSTM models performed well, with F1 scores of 0.92 and 0.93, respectively. The result of this investigation illustrates that deep learning models seem promising for evaluating and predicting multi-class problems.