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

2020/10/26 by Hamidreza Arian, Arian, Hamidreza, Seyed Mohammad Sina Seyfi +3
Business, Management and Accounting · Computer Science · #FOS: Computer and information sciences #Financial Distress and Bankruptcy Prediction #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare

paper · pdf · doi:10.48550/arxiv.2010.13388

openalex publication_date 2020/10/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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

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