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Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring. (arXiv:2209.10070v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
The forecasting of credit default risk has been an active research field for
several decades. Historically, logistic regression has been used as a major
tool due to its compliance with regulatory requirements: transparency,
explainability, and fairness. In recent years, researchers have increasingly
used complex and advanced machine learning methods to improve prediction
accuracy. Even though a machine learning method could potentially improve the
model accuracy, it complicates simple logistic regression, deteriorates
explainability, and often violates fairness. In the absence of …
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