Accuracies of some Learning or Scoring Models for Credit Risk Measurement

Given the role played by banks in the financial system as well, risks are subject to regulatory attention, and Credit risk is one of the major financial risks faced by banks. According to Basel I to III, banks have the responsibility to implement the credit risk strategy. Nowadays, machine learning techniques have attracted an important interest for different applications to financial institutions and its applications have received much attention from investors and researchers. Hence in this paper, we discuss existing literature by shedding more light on a number of techniques and examine machine learning models for Credit risk by focusing on Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN) for credit risk. Different test performances of these models such as back-testing and stress-testing have been done using Home Credit historical data and simulated data respectively. We realized that the MLP and CNN models were able to predict well with an accuracy of 91% and 67% respectively for back-testing. To test our models in stress scenarios and extreme scenarios, we consider a generated imbalanced data with 80% of defaults and 20% of non-default. Using the same model trained on Home Credit data, we perform a stress-test on the simulated data and we realized that the MLP model did not perform well compared to the CNN model, with an accuracy of 43% as against 89% obtained during the training. Thus, the CNN model was able to perform better during stressed situations for accuracy and for other metrics such as ROC AUC curve, recall, and precision.