Design and development of an intelligent loan eligibility prediction system using machine learning & explainable AI

Abstract

It can be seen that the value of assets is rising daily. That often necessary a lot of capital to buy the whole asset. Many times it becomes impossible to buy even from our savings. So we can apply for a loan to get the money we need because, through the loan application, we can easily get the money for buying assets. However, obtaining a loan is a lengthy procedure. The application must go through several steps before being accepted, and approval is not guaranteed. Many loan prediction models have been created to reduce the time and also reduce the risk attached to the loan. The main goal of my project was to compare different types of prediction models and then finalize which one is the best for loan-eligible prediction with the lowest amount of mistakes. Also used SHAP method for Feature importance of model prediction. After that choose the best ML model for deployment. So, I used eight machine learning models for my research paper. I get Logistic Regression is the best among the eight machine learning models with high accuracy and F1 score. For my application, I used this logistic regression model for prediction and also used SHAP for feature importance. After that, used text generation model L Lama-3.3-70b-versatile with Groq API to explain the reason for the prediction and give suggestions based on the prediction.