1 Business Analytics, Trine University, USA.
2 International Institute of Business Analysis.
3 Information Technology, Westcliff University, USA.
4 Business Analytics, University of Texas at Arlington, USA.
5 Information Technology, Washington University of Science and Technology, Virginia, USA.
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 1378-1386
Received on 27 July 2024; revised on 10 September 2024; accepted on 12 September 2024
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.
Predictive Analytics; Financial Risk Management; Machine Learning Algorithms; Neural Networks; Decision Trees; Support Vector Machines; Data Preprocessing; Data Privacy; Model Interpretability; Risk Identification; Risk Mitigation; Artificial Intelligence; Blockchain
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Rakibul Hasan Chowdhury, Abdullah Al Masum, Md Zahidur Rahman Farazi and Israt Jahan. The impact of predictive analytics on financial risk management in businesses. World Journal of Advanced Research and Reviews, 2024, 23(3), 1378-1386. Article DOI: https://doi.org/10.30574/wjarr.2024.23.3.2807