International Journal of Science, Engineering and TechnologyAuthors: Nijrup S. Visani
Abstract: The integration of machine learning (ML) algorithms in financial risk assessment has emerged as a transformative force within Indian banking and financial institutions. This study presents a comprehensive analysis of ML algorithm performance, implementation strategies, and regulatory compliance frameworks specifically tailored to the Indian financial ecosystem. Through systematic evaluation of seven primary ML algorithms—Neural Networks, Random Forest, Support Vector Machines (SVM), Logistic Regression, Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM)—this research demonstrates significant performance improvements over traditional risk assessment methods. Neural Networks achieved the highest accuracy of 91.5% with precision of 89.8% and recall of 87.7%, while Random Forest demonstrated robust performance at 90.7% accuracy. The study reveals that ML-based approaches improve risk assessment accuracy by 16-22 percentage points across credit risk (91% vs 75%), market risk (88% vs 70%), operational risk (85% vs 65%), liquidity risk (87% vs 68%), and fraud risk (94% vs 72%) compared to traditional methods. Analysis of regulatory compliance shows a dramatic improvement from 25% in 2021 to 95% in 2025, coinciding with the deployment of over 820 ML models across Indian financial institutions. The research incorporates case studies from major Indian banks including HDFC Bank, ICICI Bank, and State Bank of India, demonstrating practical implementation success with operational efficiency improvements of 40-65%. The study addresses the Reserve Bank of India's Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) released in August 2025, highlighting the regulatory landscape's evolution toward ML adoption. This research contributes to the growing body of knowledge on ML applications in financial services while providing actionable insights for practitioners, regulators, and researchers in the Indian financial sector.[1][2][3][4][5][6][7][8][9][10]
