CONSUMER LOAN DEFAULT RISK PREDICTION USING BORROWER FINANCIAL AND DEMOGRAPHIC PROFILE

Abstract

The increasing rate of loan defaults has become a major concern for financial
institutions, leading to significant financial losses, reduced lending capacity, and, in
some cases, the collapse of banks. High-profile financial fraud cases in the banking
sector have further raised concerns about the safety and reliability of lending systems.
These challenges highlight the need for intelligent and reliable mechanisms to assess
borrower risk and prevent potential defaults.
This project, titled “Consumer Loan Default Risk Prediction Using Borrower
Financial and Demographic Profile,” presents a data-driven approach to predict the
likelihood of loan default using Machine Learning techniques. The system analyzes
borrower information such as income, employment status, credit history, loan amount,
demographic details, and other financial attributes to identify patterns associated with
default risk.
Traditional credit evaluation methods, such as manual assessment and credit scoring
systems, often lack accuracy and fail to detect hidden patterns in large and complex
datasets. In contrast, this project leverages advanced Machine Learning algorithms
including Decision Tree, Logistic Regression, Random Forest, and XGBoost to
improve prediction accuracy. Techniques such as confusion matrix and performance
metrics are used to evaluate model effectiveness