BANK REPAYMENT PREDICTION-SYSTEM ON DEEP LEARNING TECHNIQUES

Abstract

A significant service provided by financial institutions is loans, however, with little resources to spread out banks should be selective in selecting low-risk borrowers in order to reduce the number of defaults. The traditional machine learning algorithms such as SVM and KNN are limited in one way or another; SVM is too computationally intensive, KNN tends to perform significantly worse because of inefficiency and parameter sensitivities. Similarly, Random Forest, despite its popularity, suffers in two aspects, namely, feature selection and model tuning.

This research paper proposes a deep learning-based system to predict bank loan repayment to address such shortfalls. Through an instance of Deep Neural Network (DNN), the model can learn and optimize features automatically, can support more kinds of data, and achieves a higher degree of accuracy and scalability than the classical methodologies.

A dataset of 100,000 loan applications provided by Kaggle was used to train the proposed model on. Its accuracy of 82% was better than the 68% exhibited by Random Forest by 13%. The results indicated the possibility of deep learning to minimize the bad loans, enhance the risk assessment, and also to optimize the future loan approvals.

KeywordsDeepLearning,LoanPredictionModel,raining,Testing,Prediction,AccuracyAnalysis.