PREDICTING LOAN DEFAULTERS WITH MACHINE LEARNING MODELS FOR CREDIT CARD MANAGEMENT

Abstract

The financial sector faces growing challenges in assessing loan eligibility due to the increasing complexity and volume of applications. Fraudulent loan submissions continue to result in significant financial losses, with global impacts amounting to billions of dollars each year. Accurate and efficient prediction of loan eligibility is therefore essential to protect financial institutions and promote fair lending practices. Traditional manual assessment methods are becoming obsolete, as they are timeconsuming, error-prone, and often fail to identify subtle signs of fraud. These manual processes rely heavily on subjective judgment, which can introduce inconsistencies and biases, ultimately affecting the reliability of loan decisions. Moreover, verifying a large number of data points manually delays approvals and reduces customer satisfaction.To address these issues, we propose a machine learningbased solution that automates loan eligibility prediction and fraud detection. Using the SYL Bank dataset—which includes features such as age, occupation, marital status, credit score, income level, and historical financial behavior—we aim to train predictive models capable of accurately identifying eligible applicants and detecting fraudulent activity. This data-driven approach enhances the accuracy, consistency, and speed of loan assessments, providing a more secure and streamlined process for financial institutions and their clients