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Informative Article | Finance | India | Volume 8 Issue 12, December 2019 | Rating: 4.7 / 10
Leveraging Predictive Analytics in Banking: Managing Risks with Big Data
Abstract: Predictive analytics has become a cornerstone in modern banking, offering significant enhancements in risk management strategies by utilizing advanced statistical models and machine learning techniques on big data. This article explores the pivotal role of predictive analytics in addressing three major types of risks that banks face: credit risk, market risk, and operational risk. Each type of risk requires tailored analytical approaches and models to effectively predict and mitigate potential losses. For credit risk, models such as logistic regression and decision trees help in assessing borrower reliability and preventing defaults. Market risk management benefits from techniques like Value at Risk (VaR) and Monte Carlo simulations, which aid banks in understanding potential market volatilities and preparing accordingly. Operational risk, encompassing fraud, system failures, and compliance breaches, is addressed through anomaly detection and scenario analysis, ensuring robustness against internal and external threats. The integration of predictive analytics into banking not only enhances the precision of risk assessment but also optimizes risk - related decision - making processes. This article also highlights the technological infrastructure required, challenges encountered, and the future trends in predictive analytics in the banking sector, aiming to provide a comprehensive insight into how banks can leverage these tools to fortify their risk management frameworks.
Keywords: Predictive Analytics, Risk Management, Credit Risk, Market Risk, Operational Risk, Data Mining, Machine Learning, Statistical Models, Risk Assessment, Financial Stability, Scenario Analysis, Anomaly Detection, Stress Testing, Compliance, Fraud Detection
Edition: Volume 8 Issue 12, December 2019,
Pages: 2051 - 2054