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Binning of variables: A compromise between the efficiency of the model and regulation

Vol. 25, Iss. 9, SEPTEMBER 2019

Received: 5 June 2019

Received in revised form: 19 June 2019

Accepted: 3 July 2019

Available online: 30 September 2019

Subject Heading: Banking

JEL Classification: G21, G28

Pages: 2040–2053

https://doi.org/10.24891/fc.25.9.2040

Roskoshenko V.V. Lomonosov Moscow State University (MSU), Moscow, Russian Federation
roskoshenkoeco@mail.ru

ORCID id: not available

Subject The research discretizes default factors of claims to loan repayment, as an aspect of statistical modeling in banking.
Objectives The research identifies multiple valid discretization algorithms for credit scoring and choose the most appropriate one. It also demonstrates that discretization is necessary for building a predictive model given the logistic regression method is applied.
Methods For purposes of research, I conducted the statistical analysis, content analysis of sources.
Results As part of statistical analysis, the proposed algorithm (TreeR) was proved to be most appropriate among algorithms that are compliant with Basel II requirements and existing criteria. TreeR splits the continuous variable as a result of the algorithm raising decision trees for a binary dependent variable. The algorithm is a brand new solution to the discretization of the continuous variable. What distinguishes TreeR is that it sits on the open access software and relies upon publicly available libraries.
Conclusions and Relevance The findings can be used for credit scoring as well as for statistical modeling based on the logistic regression.

Keywords: credit scoring, logistic regression, discretization, data preprocessing, continuous variable

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