Statistical Qualification for Approval of Commercial Credits through Generalized Additive Models
Abstract
This article presents the application of a methodological procedure for the construction of a statistical qualification model for the approval of commercial credits in a public financial institution. In this line, the main aim is to reveal the benefits of using generalized additive models (GAM), whose functional structures contemplate the possible non-linearity of the explanatory variables of credit risk in relation to compliance with the payment obligations of borrowers, compared to linear models like the logit. This topic becomes relevant in view of the need for financial institutions to have the right tools and information management systems that allow them to de-establish strategies to improve the placement of their loan portfolio with clients who can fulfill their agreed obligations within the established deadlines, without incurring partial or total delays; in short, minimizing your credit risk. Additionally, in order to meet the stated need, the methodological procedure is applied through programming in the R software, with which the modeling is easily replicable.
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