Assessment of Support Vector Machine performance for default prediction and credit rating

Karim Amzile, Mohamed Habachi

Banks and Bank Systems 2022 article cited by 11

importance 5/10 ai assisted Financial Distress and Bankruptcy Prediction banking credit risk stage: research

Banks need reliable prediction of customer default probability, a key focus of Basel regulations.

Default prediction directly affects bank risk management and regulatory compliance.

A support vector machine with radial basis function kernel was used to predict default and compared against logistic regression using ROC curves.

SVM with RBF kernel outperformed logistic regression on ROC; exact value truncated in abstract.

Comparison run on a portfolio of bank customers; deployment not stated in source.

Venue Banks and Bank Systems
Published 2022
Work type article
Authors Karim Amzile, Mohamed Habachi
Citations 11 (OpenAlex, at import time)
References 55
Model or method Support Vector Machine (RBF kernel)
Organization not stated in source
Primary source https://doi.org/10.21511/bbs.17(1).2022.14
Open access gold
Keywords support vector machine, credit default, ROC, logistic regression, banking, Basel
5 / 10

real but narrower advance

Default prediction matters for bank risk management and Basel compliance; abstract shows ROC 98% vs 71.7% on one bank portfolio, no scale or deployment.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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