Profit maximizing logistic regression modeling for credit scoring

Devos, Arnout, Dhondt, Jakob, Stripling, Eugen, Baesens, Bart, Broucke, Seppe Vanden and Sukhatme, Gaurav (2018) Profit maximizing logistic regression modeling for credit scoring. In 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings. IEEE. pp. 125-129 . (doi:10.1109/DSW.2018.8439113).

Abstract

Multiple classification techniques have been employed for different business applications. In the particular case of credit scoring, a classifier which maximizes the total profit is preferable. The recently proposed expected maximum profit (EMP) measure for credit scoring allows to select the most profitable classifier. Taking the idea of the EMP one step further, it is desirable to integrate the measure into model construction, and thus obtain a profit maximizing model. Therefore, in this work we propose a method based on the ProfLogit classifier, which optimizes the coefficients of a logistic regression model using a genetic algorithm. The proposed implemented technique shows a significant improvement compared to regular maximum likelihood based logistic regression models on real-life data sets in terms of total profit, which is the ultimate goal for most businesses.

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Contributors

Author: Bart Baesens

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