A Novel Classification Approach for Credit Scoring based on Gaussian Mixture Models

16 Pages Posted: 23 Nov 2020 Last revised: 27 Nov 2020

See all articles by Hamid Arian

Hamid Arian

RiskLab

Seyed Mohammad Sina Seyfi

Aalto University, School of business

Azin Sharifi

University of Toronto - RiskLab

Date Written: August 1, 2020

Abstract

Credit scoring is a rapidly expanding analytical technique used by banks and other financial institutions. Academic studies on credit scoring provide a range of classification techniques used to differentiate between good and bad borrowers. The main contribution of this paper is to introduce a new method for credit scoring based on Gaussian Mixture Models. Our algorithm classifies consumers into groups which are labeled as positive or negative. Labels are estimated according to the probability associated with each class. We apply our model with real world databases from Australia, Japan, and Germany. Numerical results show that not only our model's performance is comparable to others, but also its flexibility avoids over-fitting even in the absence of standard cross validation techniques. The framework developed by this paper can provide a computationally efficient and powerful tool for assessment of consumer default risk in related financial institutions.

Keywords: Credit Scoring, Classification, Gaussian Mixture Models, Consumer Default Risk

JEL Classification: C02, C13, G21

Suggested Citation

Arian, Hamid and Seyfi, Seyed Mohammad Sina and Sharifi, Azin, A Novel Classification Approach for Credit Scoring based on Gaussian Mixture Models (August 1, 2020). Available at SSRN: https://ssrn.com/abstract=3696216 or http://dx.doi.org/10.2139/ssrn.3696216

Hamid Arian (Contact Author)

RiskLab ( email )

1 Spadina Crescent
Toronto, ON M5S 3G3
Canada

Seyed Mohammad Sina Seyfi

Aalto University, School of business ( email )

Finland

Azin Sharifi

University of Toronto - RiskLab ( email )

1 Spadina Crescent
Toronto, ON M5S 3G3
Canada

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