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DEVELOPMENT OF A CREDIT SCORING MODEL USING MACHINE LEARNING FOR COMMERCIAL BANKS IN VIETNAM
Keywords:
credit scoring, machine learning, commercial banks, logistic regression, K-nearest neighbor, decision tree, random forest, LightGBM, support vector method.DOI:
https://doi.org/10.17654/0972361725006Abstract
In an increasingly competitive financial market, developing an accurate and efficient credit scoring model is crucial for banks to make informed credit decisions, and to help customers obtain credit products that match their financial capabilities. This study aims to develop a credit scoring model for individual customers of Vietnamese banks using machine learning techniques. This article will use a set of financial and non-financial indicators as inputs for various machine learning models such as Logistic Regression, K-nearest Neighbor, Decision Tree, Random Forest, LightGBM, and Support Vector Method. The obtained results from these models will be carefully compared and evaluated to ultimately select the best credit scoring model for the bank. This study leverages Google Colab, a cloud-based platform, for comprehensive data analysis and model development, ensuring a robust and data-driven approach. This study hopes to provide a useful solution for Vietnamese banks in managing credit risk and improving their business performance.
Received: August 26, 2024
Revised: October 6, 2024
Accepted: October 26, 2024
References
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