CRES-SMVI: Credit Risk Expert System Based on Selective Missing Value Imputation Using Machine Learning Analytics

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Abstract
Missing value imputation is one of the major problems for any data-driven model. Credit risk prediction isn't any exception either. However, the selection of appropriate missing value imputation techniques for credit default prediction remains an underexplored area in existing literature. The paper introduces ‘CRES-SMVI,' a ‘Credit Risk Expert System based on Selective Missing Value Imputation,' developed using ensemble learning techniques. The model integrates advanced preprocessing techniques, including SMOTE for handling imbalanced data and seven missing value imputation methods for effective comparison. By employing multiple classifiers such as Decision Tree (DT), Catboost (CB), and Extra Trees (ET), the system aimed to provide accurate credit default predictions. The study evaluated twenty-one models in total and finally found the best among them. As per the study, Multivariate imputation by chained equations (MICE)-based ET model performed better than all the other models, irrespective of the datasets.

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