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Profit maximizing logistic regression modeling for credit scoring

Profit maximizing logistic regression modeling for credit scoring
Profit maximizing logistic regression modeling for credit scoring

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.

Credit, EMP, Genetic, Logistic, Profit
125-129
Institute of Electrical and Electronics Engineers Inc.
Devos, Arnout
94bb3cac-41db-4113-a2c2-795d94760080
Dhondt, Jakob
8bce55f8-e48c-41d3-a523-ca3e607e2f11
Stripling, Eugen
10c20791-45b8-48da-941f-3b3afb926fa9
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Broucke, Seppe Vanden
0b17d31c-7378-4aa6-a1a8-715ddd08b3b5
Sukhatme, Gaurav
b6bd2382-ce04-4656-945a-0ce8427fc600
Devos, Arnout
94bb3cac-41db-4113-a2c2-795d94760080
Dhondt, Jakob
8bce55f8-e48c-41d3-a523-ca3e607e2f11
Stripling, Eugen
10c20791-45b8-48da-941f-3b3afb926fa9
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Broucke, Seppe Vanden
0b17d31c-7378-4aa6-a1a8-715ddd08b3b5
Sukhatme, Gaurav
b6bd2382-ce04-4656-945a-0ce8427fc600

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. Institute of Electrical and Electronics Engineers Inc. pp. 125-129 . (doi:10.1109/DSW.2018.8439113).

Record type: Conference or Workshop Item (Paper)

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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More information

e-pub ahead of print date: 4 June 2018
Published date: 20 August 2018
Venue - Dates: 2018 IEEE Data Science Workshop, DSW 2018, Switzerland, 2018-06-03 - 2018-06-05
Keywords: Credit, EMP, Genetic, Logistic, Profit

Identifiers

Local EPrints ID: 423588
URI: http://eprints.soton.ac.uk/id/eprint/423588
PURE UUID: 391b90d8-8051-44c2-8d27-94f2ca64dce1
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 27 Sep 2018 16:30
Last modified: 07 Oct 2020 01:50

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Contributors

Author: Arnout Devos
Author: Jakob Dhondt
Author: Eugen Stripling
Author: Bart Baesens ORCID iD
Author: Seppe Vanden Broucke
Author: Gaurav Sukhatme

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