Beyond Algorithms: The Strategic Impact of Machine Learning on Modern Banking

Abstract

This research paper provides a comprehensive analysis of the implementation of machine learning (ML) in the banking and financial sectors. The primary objectives are to examine how ML enhances credit risk assessment, improves fraud detection and prevention, optimizes portfolio management and investment strategies, and personalizes customer services. Through a systematic review of existing literature and a methodological framework involving quantitative and qualitative analysis, this study identifies current trends, challenges, and gaps in ML adoption. The findings reveal that while ML offers transformative potential, issues such as data privacy, model interpretability, and integration with legacy systems remain significant hurdles. The paper concludes with recommendations for future research and practical implementations to bridge these gaps.