Quantitative Methods in Credit Management: A Survey

Eric Rosenberg and Alan Gleit
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Eric Rosenberg: AT&T Bell Laboratories, Middletown, New Jersey
Alan Gleit: Citicorp, New York, New York

Operations Research, 1994, vol. 42, issue 4, 589-613

Abstract: Many static and dynamic models have been used to assist decision making in the area of consumer and commercial credit. The decisions of interest include whether to extend credit, how much credit to extend, when collections on delinquent accounts should be initiated, and what action should be taken. We survey the use of discriminant analysis, decision trees, and expert systems for static decisions, and dynamic programming, linear programming, and Markov chains for dynamic decision models. Since these models do not operate in a vacuum, we discuss some important aspects of credit management in practice, e.g., legal considerations, sources of data, and statistical validation of the methodology. We provide our perspective on the state-of-the-art in theory and in practice.

Keywords: finance; corporate finance: bankruptcy prediction; financial institutions; banks: credit analysis; statistics; data analysis: discriminant analysis (search for similar items in EconPapers)
Date: 1994
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Citations: View citations in EconPapers (48)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:42:y:1994:i:4:p:589-613

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