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Explainable machine learning models for the probability of credit default
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Resumo(s)
This dissertation addresses the challenge of balancing predictive accuracy and
interpretability in credit risk assessment models within regulated financial environments.
Using annual panel data from a company’s credit application in 2024, the study conducts
a comparative analysis of traditional logistic regression and two advanced machine
learning models: Catboost and Explainable Boosting Machine, EBM. Through empirical
evaluation using standardized metrics for both accuracy and interpretability, the research
demonstrates that modern machine learning models can effectively overcome the
conventional trade-off between accuracy and interpretability.
This study’s results revealed that both Catboost and Explainable Boosting Machine
outperform logistic regression in predictive accuracy while meeting the interpretability
standards required by regulatory frameworks. Catboost achieved superior predictive
performance and utilized SHAP values for post-hoc interpretation. Meanwhile, EBM
demonstrated competitive accuracy with built-in interpretability features. These findings
indicate that financial institutions can modernize their credit risk assessment frameworks
without compromising regulatory compliance or stakeholder trust.
The present work contributes to the broader discourse on responsible machine
learning in financial services, providing empirical evidence that advanced models can
simultaneously achieve high accuracy and interpretability in credit risk assessment
applications.
Descrição
Palavras-chave
Credit Risk Assessment Machine Learning Interpretability Logistic Regression Catboost Explainable Boosting Machine Explainable Boosting Machine
Contexto Educativo
Citação
Elias , Ana Rita dos Santos (2025). “Explainable machine learning models for the probability of credit default ”. Dissertação de Mestrado. Universidade de Lisboa. Instituto Superior de Economia e Gestão
Editora
Instituto Superior de Economia e Gestão