BUILDING A SCORING MODEL FOR FINANCIAL INSTITUTIONS USING THE XGBOOST MACHINE LEARNING ALGORITHM

Authors

DOI:

https://doi.org/10.30888/2709-2267.2024-25-00-004

Keywords:

model validation, feature engineering, machine learning, predictive analytics, scoring model.

Abstract

The construction of a credit scoring model using machine learning methods for determining the reliability of clients when making loan agreements by financial institutions has been considered. The application of the XGBoost algorithm is thoroughly investig

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References

E Deng,H, Runger, G., Tuv, E. (2011). Bias of importance measures for multi-valued attributes and solutions. Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN). pp. 293–300.

Hastie, T., Tibshirani, R., Friedman, J. H. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer Verlag.

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Published

2024-07-30

How to Cite

Volkov, O., & Voinalovych, N. (2024). BUILDING A SCORING MODEL FOR FINANCIAL INSTITUTIONS USING THE XGBOOST MACHINE LEARNING ALGORITHM. Sworld-Us Conference Proceedings, 1(usc25-00), 7–15. https://doi.org/10.30888/2709-2267.2024-25-00-004

Issue

No. usc25-00 (2024): International scientific integration '2024

Section

Abstracts

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This work is licensed under a Creative Commons Attribution 4.0 International License.