Abstract
We evaluate the creditworthiness of banks using statistical, as well as combinatorics-, optimization-, and logic-based methodologies. We reverse-engineer the Fitch risk ratings of banks using ordered logistic regression, support vector machine, and Logical Analysis of Data (LAD). The LAD ratings are shown to be the most accurate and most successfully cross-validated. The study shows that the LAD rating approach is (i) objective, (ii) transparent, and (iii) generalizable. It can be used to build internal rating systems that (iv) have varying levels of granularity, and (v) are Basel compliant, allowing for their use in the decisions pertaining to the determination of the amount of regulatory capital.
Original language | English (US) |
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Pages (from-to) | 7808-7821 |
Number of pages | 14 |
Journal | Expert Systems With Applications |
Volume | 39 |
Issue number | 9 |
DOIs | |
State | Published - Jul 2012 |
All Science Journal Classification (ASJC) codes
- Engineering(all)
- Computer Science Applications
- Artificial Intelligence
Keywords
- Credit risk rating
- Data mining
- Decision support systems
- Logical Analysis of Data