A logical analysis of banks' financial strength ratings

Peter L. Hammer, Alexander Kogan, Miguel A. Lejeune

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

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 languageEnglish (US)
Pages (from-to)7808-7821
Number of pages14
JournalExpert Systems With Applications
Volume39
Issue number9
DOIs
StatePublished - 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

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