<![CDATA[The banking and finance industry has entered a period of accelerated transformation as machine learning (ML) methods migrate from experimental pilots into core decision-making infrastructure. This paper presents an in-depth, literature-grounded analysis of how machine learning is reshaping credit risk assessment, fraud detection and prevention, algorithmic trading and portfolio management, and customer engagement in retail and commercial banking. Drawing on a structured review of foundational and contemporary studies, the paper traces the evolution of analytics maturity in finance from descriptive and predictive analytics toward prescriptive and real-time decision-making, and synthesizes comparative evidence on the performance of classical and modern ML architectures for credit scoring. The paper further examines the ethical, regulatory, and interpretability challenges that accompany ML adoption, including algorithmic bias, fairness, data privacy under regimes such as the GDPR, and capital-adequacy compliance under Basel III. A mixed-method research design is proposed and illustrated with representative tables and figures summarizing application-level adoption patterns, thematic distribution of the literature, and comparative model performance metrics. The analysis identifies persistent research gaps in interdisciplinary methodology, bias mitigation, real-time trading risk, model explainability, and financial inclusion, and concludes with a research agenda intended to guide the next phase of responsible machine learning adoption in banking and finance.]]>
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