2020/11/16 by Hamidreza Arian, Arian, Hamidreza, Seyed Mohammad Sina Seyfi +3
Business, Management and Accounting · Computer Science · #FOS: Economics and business #Financial Distress and Bankruptcy Prediction #General Economics (econ.GN) #Imbalanced Data Classification Techniques #Machine Learning in Healthcare
paper · pdf · doi:10.48550/arxiv.2011.07906
openalex publication_date 2020/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Credit scoring is an essential tool used by global financial institutions and\ncredit lenders for financial decision making. In this paper, we introduce a new\nmethod based on Gaussian Mixture Model (GMM) to forecast the probability of\ndefault for individual loan applicants. Clustering similar customers with each\nother, our model associates a probability of being healthy to each group. In\naddition, our GMM-based model probabilistically associates individual samples\nto clusters, and then estimates the probability of default for each individual\nbased on how it relates to GMM clusters. We provide applications for risk\nmanagers and decision makers in banks and non-bank financial institutions to\nmaximize profit and mitigate the expected loss by giving loans to those who\nhave a probability of default below a decision threshold. Our model has a\nnumber of advantages. First, it gives a probabilistic view of credit standing\nfor each individual applicant instead of a binary classification and therefore\nprovides more information for financial decision makers. Second, the expected\nloss on the train set calculated by our GMM-based default probabilities is very\nclose to the actual loss, and third, our approach is computationally efficient.\n