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Profit Maximizing Logistic Regression Modeling for Credit Scoring

Citation Author(s):
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme
Submitted by:
Arnout Devos
Last updated:
30 May 2018 - 8:30pm
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Arnout Devos
Paper Code:
1024

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 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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