Application of a binary choice model for bank scoring.
Keywords:
scoring, default, logistic regression, binary choice model, bank.Abstract
This article describes a binary choice model and justification of its choice for the construction of the scoring model in banks. Among the many models that are able to determine the influence of factors on a single variable, in this very case the logistic regression was considered. That is exactly the logistic regression that is the traditional statistical tools to calculate coefficients scorecard, and ROC-analysis provides a risk management, depending on the credit policy and strategy of the organization. Logistic regression is used for a wide range of functions, including the analysis of the dependence between certain number of independent variables the dependent variable. This is a binary logistic regression, which means that the dependent variable can take only two values. In other words logistic regression helps to assess the probability that an event occurs or does not occur for a particular case, in our case, a return of the loan or default. According to the results, you can build a dependence between the behavior of the client and his ability to pay, and subsequently apply this model to the banks when issuing loans.References
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2 Fawcett T. ROC Graphs: Notes and Practical Considerations for Researchers. Kluwer Academic Publishers: 2004. – 85 c.
3 Zweig M.H., Campbell G. ROC Plots: A Fundamental Evaluation Tool in Clinical Medicine // Clinical Chemistry. – 1993. – Vol. 39. – No. 4. – P. 22-27.
4 Davis J., Goadrich M. The Relationship Between Precision-Recall and ROC Curves // Proc. of 23 International Conference on Machine Learning. – Pittsburgh, PA, 2006. – P. 14-18.
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