  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.
