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Reverse Engineering Banks Financial Strength Ratings Using Logical Analysis of Data

by Peter L. Hammer of Rutgers University,
Alexander Kogan of Rutgers University, and
Miguel A. Lejeune of Carnegie Mellon University

January 2007

Abstract: We study the problem of evaluating the creditworthiness of banks using statistical, as well as combinatorics, optimization and logic-based methodologies. We reverse-engineer the Fitch credit risk ratings of banks using ordered logistic regression and Logical Analysis of Data (LAD). It is shown that LAD provides the most accurate rating model. The obtained ratings are successfully cross-validated, and the derived model is used to identify the financial variables most important for bank ratings. The study also shows that the LAD rating approach is (i) objective, (ii) transparent, (iii) generalizable. It can be used to develop internal rating systems that (iv) have varying levels of granularity, allowing their use at various stages in the credit granting decision process (pre-approval, determination of pricing policies), and (v) are Basel 2 compliant.

JEL Classification: G15, F34, H81, C60, H60.

Keywords: credit risk rating, bank creditworthiness, Logical Analysis of Data, combinatorial.

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