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Credit scoring is an essential tool used by global financial institutions andcredit lenders for financial decision making. In this paper, we introduce a newmethod based on Gaussian Mixture Model (GMM) to forecast the probability ofdefault for individual loan applicants. Clustering similar customers with eachother, our model associates a probability of being healthy to each group. Inaddition, our GMM-based model probabilistically associates individual samplesto clusters, and then estimates the probability of default for each individualbased on how it relates to GMM clusters. We provide applications for riskmanagers and decision makers in banks and non-bank financial institutions tomaximize profit and mitigate the expected loss by giving loans to those whohave a probability of default below a decision threshold. Our model has anumber of advantages. First, it gives a probabilistic view of credit standingfor each individual applicant instead of a binary classification and thereforeprovides more information for financial decision makers. Second, the expectedloss on the train set calculated by our GMM-based default probabilities is veryclose to the actual loss, and third, our approach is computationally efficient.

Forecasting Probability of Default for Consumer Loan Management with Gaussian Mixture Models