Machine Learning for Enhanced Credit Risk Assessment: An Empirical Approach

Nicolas Suhadolnik, Jo Ueyama and Sergio Da Silva
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Nicolas Suhadolnik: Institute of Mathematics and Computer Science, University of Sao Paulo, Sao Carlos 13566-590, Brazil
Jo Ueyama: Institute of Mathematics and Computer Science, University of Sao Paulo, Sao Carlos 13566-590, Brazil

JRFM, 2023, vol. 16, issue 12, 1-21

Abstract: Financial institutions and regulators increasingly rely on large-scale data analysis, particularly machine learning, for credit decisions. This paper assesses ten machine learning algorithms using a dataset of over 2.5 million observations from a financial institution. We also summarize key statistical and machine learning models in credit scoring and review current research findings. Our results indicate that ensemble models, particularly XGBoost, outperform traditional algorithms such as logistic regression in credit classification. Researchers and experts in the subject of credit risk can use this work as a practical reference as it covers crucial phases of data processing, exploratory data analysis, modeling, and evaluation metrics.

Keywords: credit risk; computer methods; machine learning (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
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