International Journal of Science, Engineering and TechnologyAuthors: Krishnaben Rajeshkumar Kachhiya
Abstract: The rapid advancement of predictive analytics and machine learning has transformed financial risk management within international banking frameworks by enabling financial institutions to identify, assess, and mitigate risks with greater speed and accuracy. This paper examines the integration of artificial intelligence-driven predictive models across key banking functions, including credit risk assessment, fraud detection, anti-money laundering (AML), market risk forecasting, liquidity management, and regulatory compliance. It explores how machine learning algorithms process large volumes of structured and unstructured financial data to improve decision-making, enhance operational efficiency, and strengthen financial resilience. The paper also discusses the role of predictive analytics in supporting international regulatory frameworks such as Basel III, IFRS 9, and FATF standards. Furthermore, it evaluates the ethical, technical, and governance challenges associated with AI adoption, including algorithmic bias, cybersecurity, data privacy, and model explainability. Finally, the study highlights emerging trends, including generative AI, explainable AI, cloud computing, quantum computing, and ESG-focused analytics, that are expected to shape the future of global banking risk management.
