Identificação das Características dos Clientes Associadas ao Risco de Crédito

Authors

  • Luís N. Pereira ESGHT/Universidade do Algarve
  • Luís R. Chorão Director do Banco BPI

Keywords:

Credit Scoring, Logistic Regression, Non-hierarchical Clustering Method, Kaplan-Meier Estimators, Correspondence Analysis.

Abstract

The decision making process of evaluating the creditworthiness of a loan is sometimes difficult to the human mind because of the great number of variables and interrelations among them. What we propose here, is to identify the characteristics related to high and low risk, and this is made by using an applicant model. So, with a credit card database with categorical and continuous variables, in order to make the decision process more streamlined and quantifiable, we performed a binary logistic model. Applying the non-hierarchical clustering method (K-means) to the logit output vector we identified eight risk classes. Each class was evaluated temporarily by the pro-duct-limit estimators (Kaplan-Meier estimators) for 70 months, showing that low probability of default is indeed associated with low risk classes. The statistic technique applied to identify the client risk characteristics was the correspondence analysis.

Author Biographies

  • Luís N. Pereira, ESGHT/Universidade do Algarve

    Master in Statistics and Information Management

    Adjunct Professor, ESGHT/University of Algarve

  • Luís R. Chorão, Director do Banco BPI

    Master in Statistics and Information Management

    Director of the Bank BPI

Downloads

Published

31.12.2007

Issue

Section

Business/Management: Research Papers

How to Cite

Pereira, L. N., & Chorão, L. R. (2007). Identificação das Características dos Clientes Associadas ao Risco de Crédito. Tourism & Management Studies, 3, 12-26. https://tmstudies.net/index.php/ectms/article/view/82