Prediction of Loan Behaviour with Machine Learning Models for Secure Banking
Authors: Anand, M., Velu, A., Whig, P.
Journal: Journal of Computer Science Review and Engineering (JCSRE), ISSN 1694-4488
Citation: JCSRE 2(2), 2022-08-11.
DOI: 10.36596/jcse.v3i1.237
Type: Original Research
Abstract
This paper presents an overview of various machine learning models that can be used to predict loan defaults, which is a critical issue for financial institutions. The study evaluates a wide range of state-of-the-art machine learning models, including Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbors, Naïve Bayes, and Artificial Neural Networks. These models are applied to a comprehensive dataset of past loan applications, encompassing diverse features such as borrower demographics, credit history, loan characteristics, and economic indicators. The performance of each model is rigorously assessed using various metrics like accuracy, precision, recall, F1-score, and AUC-ROC. Furthermore, the paper delves into interpretability techniques to understand the key factors influencing loan default predictions for each model. The findings highlight the efficacy of machine learning in identifying potential defaulters, thereby enabling banks to make informed decisions, mitigate risks, and enhance the security of their lending operations. This research contributes to the development of robust and transparent credit scoring systems, fostering financial stability and responsible lending practices.
Keywords
Machine Learning, Loan Prediction, Credit Scoring, Financial Risk, Banking Security
Full Text
This paper presents an overview of various machine learning models that can be used to predict loan defaults, which is a critical issue for financial institutions. The study evaluates a wide range of state-of-the-art machine learning models, including Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbors, Naïve Bayes, and Artificial Neural Networks. These models are applied to a comprehensive dataset of past loan applications, encompassing diverse features such as borrower demographics, credit history, loan characteristics, and economic indicators. The performance of each model is rigorously assessed using various metrics like accuracy, precision, recall, F1-score, and AUC-ROC. Furthermore, the paper delves into interpretability techniques to understand the key factors influencing loan default predictions for each model. The findings highlight the efficacy of machine learning in identifying potential defaulters, thereby enabling banks to make informed decisions, mitigate risks, and enhance the security of their lending operations. This research contributes to the development of robust and transparent credit scoring systems, fostering financial stability and responsible lending practices.