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Credit Modeling Techniques

In: Practical Credit Risk and Capital Modeling, and Validation

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  • Colin Chen

    (Data Science and Analytics Consultants)

Abstract

Credit risk modeling techniques become mature over more than a half century of developments. While modeling for credit risk could be traced back much earlier, theoretical affirmation of statistical models, for example, the multinomial logit model as a special case of the more general conditional logit model, was first provided about a half century ago (McFadden, 1974) using the random utility maximization paradigm. Since then, statistical models like the generalized linear models (GLM) have become the most popular selection in modeling credit risks, though machine learning models start to challenge that dominance in some areas in recent years. Figure 3.1 outlines the structure of various models discussed in this chapter.

Suggested Citation

  • Colin Chen, 2024. "Credit Modeling Techniques," Management for Professionals, in: Practical Credit Risk and Capital Modeling, and Validation, chapter 3, pages 77-149, Springer.
    • Handle: RePEc:spr:mgmchp:978-3-031-52542-1_3
    DOI: 10.1007/978-3-031-52542-1_3
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