Binary logistic regression as an instrument for the prediction of default

Authors

Keywords:

probability of default, credit score, binary logistic regression, self-employment

Abstract

The objective of this article is the development of a prediction model of the non-payment of the self-employed sector in the Popular Savings Bank (BPA) of Santiago de Cuba province. It was used the analysis and synthesis method, and the binary logistic regression (BLR) for the data treatment. The analysis of the main methodologies of clients' classification used in the banking activity allowed identifying the BLR as a prognostic instrument. Default was considered a dependent variable and the Capacity to Pay, Credit History, Qualitative Evaluation, Tax History and Experience were considered independent variables, which led to an estimate of the probability of default of new applicants for financing.

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Published

2020-11-30

Issue

2020: Special Issue 2

Section

Número Especial

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