A new model averaging approach in predicting credit risk default

• Published in: Risks (Basel) Vol. 9; no. 6; pp. 1 - 30
• Main Authors: Jha, Paritosh Navinchandra, Cucculelli, Marco
• Format: Journal Article
• Language: English
• Published: Basel MDPI 01.06.2021; MDPI AG
• Subjects: Algorithms; Artificial intelligence; Bias; Classification; classification model; Credit risk; credit risk default; Datasets; Default; ensemble modeling; Financial institutions; Literature reviews; Machine learning; model averaging; Regression analysis; Risk assessment; weighted-linear combination
• ISSN: 2227-9091; 2227-9091
• DOI: 10.3390/risks9060114

The paper introduces a novel approach to ensemble modeling as a weighted model average technique. The proposed idea is prudent, simple to understand, and easy to implement compared to the Bayesian and frequentist approach. The paper provides both theoretical and empirical contributions for assessing credit risk (probability of default) effectively in a new way by creating an ensemble model as a weighted linear combination of machine learning models. The idea can be generalized to any classification problems in other domains where ensemble-type modeling is a subject of interest and is not limited to an unbalanced dataset or credit risk assessment. The results suggest a better forecasting performance compared to the single best well-known machine learning of parametric, non-parametric, and other ensemble models. The scope of our approach can be extended to any further improvement in estimating weights differently that may be beneficial to enhance the performance of the model average as a future research direction.