Intelligent Finance: A Comprehensive Analysis Of Machine Learning Transforming Banking, Credit Risk & Fraud Detection
Keywords:
Machine Learning, Banking, Credit Risk, Fraud Detection, Fintech, Neural Networks, Random Forest, Deep LearningAbstract
The integration of machine learning (ML) into banking and financial services represents one of the most significant technological transformations of the 21st century. This research paper presents an in-depth analysis of how ML algorithms and models are reshaping core banking operations—with a focused examination of two critical objectives: enhancing credit risk assessment and improving fraud detection and prevention. Drawing on empirical data, industry case studies, comparative model evaluations, and forward-looking projections, this paper demonstrates that ML-driven systems consistently outperform traditional statistical methods in accuracy, speed, and adaptability. The findings underscore the urgent need for financial institutions to adopt robust ML frameworks, while also addressing challenges related to model interpretability, regulatory compliance, and ethical deployment.
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