Abstract
This paper examines the impact of predictive analytics on financial risk management in businesses. Predictive analytics involves the use of statistical algorithms, machine learning, and data mining techniques to analyze historical data and predict future outcomes. In the context of financial risk management, predictive analytics plays a critical role in identifying, assessing, and mitigating potential financial risks. This paper explores various machine learning algorithms, including neural networks, decision trees, and support vector machines, and their applications in risk management. It also discusses data sources, preprocessing techniques, and the challenges associated with data privacy, model interpretability, and prediction accuracy. The review highlights successful implementations of predictive analytics in financial risk management and provides recommendations for future research and practical applications. As predictive analytics continues to advance, its integration with emerging technologies such as artificial intelligence and blockchain promises to enhance financial risk management practices.
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