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Open Access Journal Article

Ensemble Learning Approaches for Credit Risk Assessment in Financial Institutions

by Emma Anderson 1,*
1
Emma Anderson
*
Author to whom correspondence should be addressed.
TASC  2021, 20; 3(1), 20; https://doi.org/10.69610/j.tasc.20210617
Received: 29 April 2021 / Accepted: 20 May 2021 / Published Online: 17 June 2021
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Abstract

Abstract This paper explores the application of ensemble learning approaches in credit risk assessment within financial institutions. Credit risk assessment is a critical function in the financial sector, involving the evaluation of borrowers' creditworthiness to prevent defaults and ensure the stability of the financial system. Ensemble learning, which combines multiple models to improve prediction accuracy, has gained significant attention due to its robustness and flexibility. The study reviews various ensemble methods such as bagging, boosting, and stacking, and discusses their effectiveness in credit risk assessment. By integrating diverse data sources and leveraging the strengths of different models, ensemble learning can enhance the predictive power and reliability of credit risk assessment models. The paper further evaluates the performance of ensemble approaches through empirical analysis, highlighting the advantages and challenges associated with their implementation in real-world financial institutions. The results suggest that ensemble learning can significantly improve the forecasting accuracy of credit risk assessment, contributing to more informed decision-making and risk management in the financial sector.

Keywords: Ensemble learning, Credit risk assessment, Financial institutions, Predictive modeling, Machine learning, Risk management.;
Copyright: © 2021 by Anderson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Anderson, E. Ensemble Learning Approaches for Credit Risk Assessment in Financial Institutions. Transactions on Applied Soft Computing, 2021, 3, 20. https://doi.org/10.69610/j.tasc.20210617
AMA Style
Anderson E. Ensemble Learning Approaches for Credit Risk Assessment in Financial Institutions. Transactions on Applied Soft Computing; 2021, 3(1):20. https://doi.org/10.69610/j.tasc.20210617
Chicago/Turabian Style
Anderson, Emma 2021. "Ensemble Learning Approaches for Credit Risk Assessment in Financial Institutions" Transactions on Applied Soft Computing 3, no.1:20. https://doi.org/10.69610/j.tasc.20210617
APA style
Anderson, E. (2021). Ensemble Learning Approaches for Credit Risk Assessment in Financial Institutions. Transactions on Applied Soft Computing, 3(1), 20. https://doi.org/10.69610/j.tasc.20210617

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