Machine learning for credit scoring: improving logistic regression with non-linear decision-tree effects

DOI10.1016/j.ejor.2021.06.053zbMath1490.91227OpenAlexW3173725123MaRDI QIDQ2060438

Sessi Tokpavi, Sullivan Hué, Elena Dumitrescu, Christophe Hurlin

Publication date: 13 December 2021

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2021.06.053

zbMATH Keywords

interpretabilitymachine learningrisk managementcredit scoringeconometrics

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Generalized linear models (logistic models) (62J12) Learning and adaptive systems in artificial intelligence (68T05) Credit risk (91G40)

Related Items (5)

EFFECT OF THE COMPANY RELATIONSHIP NETWORK ON DEFAULT PREDICTION: EVIDENCE FROM CHINESE LISTED COMPANIES ⋮ Credit default prediction from user-generated text in peer-to-peer lending using deep learning ⋮ Interpretable machine learning for imbalanced credit scoring datasets ⋮ Machine learning in bank merger prediction: a text-based approach ⋮ The profitability of online loans: a competing risks analysis on default and prepayment

Cites Work

• Unnamed Item
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• Bagging predictors
• The Model Confidence Set
• A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees
• Development and application of consumer credit scoring models using profit-based classification measures
• The Adaptive Lasso and Its Oracle Properties
• Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research
• A comparison of neural networks and linear scoring models in the credit union environment
• Neural network credit scoring models
• Loss functions for loss given default model comparison
• Cost-sensitive learning and decision making revisited
• Credit Scoring and Its Applications
• Managerial Applications of Neural Networks: The Case of Bank Failure Predictions
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Credit Scoring and Its Applications, Second Edition
• Benchmarking state-of-the-art classification algorithms for credit scoring
• L 1-Regularization Path Algorithm for Generalized Linear Models
• Regularization and Variable Selection Via the Elastic Net
• Adaptive Lasso for Cox's proportional hazards model
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
• Good practice in retail credit scorecard assessment
• Logistic model trees
• Random forests

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