2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 1 , Issue 2 , PP: 93-100, 2020 | Cite this article as | XML | Html | PDF | Full Length Article
Khyati Chaudhary 1 * , Gopal Chaudhary 2
Doi: https://doi.org/10.54216/AJBOR.010205
The dynamics of financial risk assessment in banking necessitate robust methodologies that harness the potential of intelligent data mining. In this study, we propose an applied approach that integrates sophisticated data mining techniques, notably XGBoost, within the context of banking data. Addressing the limitations of conventional risk assessment methodologies, our research emphasizes the need for a more precise and nuanced approach to identifying potential risks inherent in financial portfolios. Leveraging exploratory data analytics, meticulous preprocessing, and advanced modeling techniques, our methodology meticulously unraveled the intricate landscape of financial data. Through the application of XGBoost and comparative analysis against Support Vector Regression (SVR) and Random Forest (RF) models, this study elucidates the superiority of XGBoost in accurately predicting financial risk. Moreover, distributional analysis of socio-demographic attributes and loan amounts unveiled significant insights into risk determinants. The results underscore the pivotal role of intelligent data mining in refining risk assessment strategies within banking sectors. The comparative analysis, distributional insights, and superior predictive performance of XGBoost collectively emphasize the potential for advanced data mining techniques to revolutionize risk evaluation methodologies, empowering informed decision-making processes in navigating financial complexities.
Risk assessment , Machine learning algorithms , Financial risk management , Predictive analytics , Data-driven decision-making , Algorithmic risk analysis , Financial sector optimization , Data mining techniques , Intelligent risk modeling , Financial data analysis.
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