Modelos de credit scoring: qué, cómo, cuándo y para qué

Gutierrez Girault, Matias Alfredo (2007): Modelos de credit scoring: qué, cómo, cuándo y para qué.

Abstract

Introduced in the 70’s, credit scoring techniques became widespread in the 90’s thanks to the development of better statistical and computational resources. Nowadays almost all the financial intermediaries use these techniques, at least to originate credits.

Credit scoring models are algorithms that in a mechanical way assess the credit risk of a loan applicant or an existing bank client, by means of statistical, mathematic, econometric or artificial intelligence developments. They are focused on the borrower’s creditworthiness or credit risk, regardless of his interaction with the rest of the portfolio. Although all of them yield fairly similar results, those most commonly used are probit and logistic regressions, and decision trees. In general they are used to evaluate the retail portfolio; corporate obligors are typically assessed with rating systems. Besides using different explanatory variables, the assessment of corporate borrowers implies revising qualitative aspects of their business that are difficult to standardize. Therefore the result of their assessment is better expressed with a rating.

To clarify how credit scores are constructed and used, with the information contained in the BCRA’s public credit registry (Central de Deudores del Sistema Financiero (CENDEU)) we estimate a sample credit score and show how it operates with a probit model. The only purpose of this model is to show some stylized facts of credit scores, and by no means seeks to establish or indicate what are the best practices in their use, construction or interpretation.

• Item Type: MPRA Paper
• English Title: Credit scoring models: what, how, when and for what purposes
• Language: Spanish
• Keywords: credit risk; credit scoring; binary probit
• Subjects: G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C25 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities

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