MACHINE LEARNING SYSTEM FOR CREDIT SCORING AND

FINANCIAL DECISION MAKING

Authors:

Mr. K. Ram Mohan¹, Maduri Deeksha²

¹Professor, Department of Computer Science and Engineering, St. Martin’s Engineering College, Hyderabad, India Email: krammohancse@smec.ac.in

²Student, Department of Computer Science and Engineering, St. Martin’s Engineering College Email: deekshamaduri@gmail.com

Abstract

The use of machine learning in financial services has significantly changed how credit risk is assessed and how financial decisions are made. Traditional credit scoring methods often rely on limited variables and rigid rules, whereas modern machine learning models can process large volumes of diverse data to generate more accurate and dynamic predictions. However, several challenges such as algorithmic bias, lack of transparency, data privacy concerns, and regulatory issues.

This review explores the role of machine learning in credit scoring and financial decision-making, focusing on both its benefits and associated risks. It also examines existing techniques and frameworks developed to improve fairness, interpretability, and security in financial AI systems. The study highlights that although many solutions have been proposed, their real-world implementation is still limited. A collaborative approach involving financial institutions, regulators, and technology developers is essential for building trustworthy and responsible AI systems in finance.

Furthermore, the study emphasizes the importance of developing transparent and fair credit scoring systems to ensure ethical financial practices. It also highlights the need for continuous monitoring and improvement of machine learning models to adapt to evolving financial environments and regulatory requirements.

Keywords: Machine Learning, Credit Scoring, Financial Decision Making, Explainable AI, Bias, Data Privacy, Risk Management.