AI-Based Hybrid Models for Predicting Loan Risk in the Banking Sector

Abstract

Every real-world scenario is now digitally replicated in order to reduce paperwork and human labor costs. Machine Learning (ML) models are also being used to make predictions in these applications. Accurate forecasting requires knowledge of these machine learning models and their distinguishing features. The datasets we use as input for each of these different types of ML models, yielding different results. The choice of an ML model for a dataset is critical. A loan risk model is used to show how ML models for a dataset can be linked together. The purpose of this study is to look into how we could use machine learning to quantify or forecast mortgage credit risk. This phrase refers to the process of evaluating massive amounts of data in order to derive useful information for making decisions in a variety of fields. If credit risk is considered, a method based on an examination of what caused and how mortgage credit risk affected credit defaults during the still-current economic crisis of 2021 will be tried. Various approaches to credit risk calculation will be examined, ranging from the most basic to the most complex. In addition, we will conduct a case study on a sample of mortgage loans and compare the results of three different analytical approaches, logistic regression, decision tree, and gradient boost to see which one produced the most commercially useful insights.