Assessment of Support Vector Machine performance for default prediction and credit rating
importance 5/10
ai assisted
Financial Distress and Bankruptcy Prediction banking credit risk stage: research
Problem
Banks need reliable prediction of customer default probability, a key focus of Basel regulations.
Why it matters
Default prediction directly affects bank risk management and regulatory compliance.
What the AI did
A support vector machine with radial basis function kernel was used to predict default and compared against logistic regression using ROC curves.
Results
SVM with RBF kernel outperformed logistic regression on ROC; exact value truncated in abstract.
Impact
Comparison run on a portfolio of bank customers; deployment not stated in source.
Record
| 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 |
Importance rating
5
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.
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