2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 11 , Issue 1 , PP: 54-61, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Serkan Yilmaz Kandir 1 * , Murat Ismet Haseki 2
Doi: https://doi.org/10.54216/AJBOR.110106
effective risk management is an indispensable requirement for improving the flow of transactions in dynamic financial markets. To this end, this study presents an applied predictive analytics methodology, that integrate gradient boosting algorithm to model the risk behavior in dynamic markets. This study, based on predictive analytics in monetary and financial systems, faces an urgent need for robust models that can overcome the uncertainties inherent in dynamic markets. Holistic experimentations on public case study of U.S retail data demonstrate the predictive power of the proposed approach of the state-of-the-art techniques across different performance metrics. This in turn highlights the nuanced interaction between variables and delivering intuitions into crucial risk determining factor.
Business Intelligence , Machine Learning , Risk Managements , Market Analysis , Predictive Modeling
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