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Informative Article | Decision Science | India | Volume 11 Issue 1, January 2022 | Rating: 5.4 / 10
Advancing Risk Management in Financial Institutions through Deep Learning Methods: Opportunities and Challenges
Sai Kalyana Pranitha Buddiga [2]
Abstract: Risk management is a vital aspect of financial institutions' operations, essential for ensuring stability, resilience, and regulatory compliance in an ever - evolving landscape. This paper explores the potential of deep learning methods to revolutionize risk management practices within financial institutions. Deep learning techniques, powered by neural networks, offer unparalleled capabilities in analyzing vast amounts of data, identifying complex patterns, and improving decision - making processes. Through a comprehensive review of the literature, this paper examines the applications of deep learning in various domains of financial risk management, including credit risk assessment, market risk prediction, fraud detection, and anti - money laundering compliance. Furthermore, it discusses the challenges and considerations associated with the adoption of deep learning methods, including data quality, interpretability, regulatory compliance, and model risk management. Finally, the paper provides insights into best practices, implementation strategies, and future directions for financial institutions looking to leverage the potential of deep learning in risk management.
Keywords: Risk Management, Deep Learning, Fraud Detection, AML, Regulatory Compliance
Edition: Volume 11 Issue 1, January 2022,
Pages: 1628 - 1630