A Novel Classification Approach for Credit Scoring based on Gaussian Mixture Models
This provides a computationally efficient tool for banks and financial institutions to assess consumer default risk, though it appears incremental.
The paper tackles credit scoring by introducing a new method based on Gaussian Mixture Models to classify borrowers as good or bad, achieving performance comparable to others and avoiding over-fitting without standard cross-validation.
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.