Soft-Hard Data Fusion Using Uncertainty Balance Principle – Evidence from Corporate Credit Risk Assessment in Commercial Banking

Sabina Brkić, Migdat Hodzic, Enis Džanić


This study introduces Uncertainty Balance Principle (UBP) as a new concept/method for incorporating additional soft data into probabilistic credit risk assessment models. It shows that soft banking data, used for credit risk assessment, can be expressed and decomposed using UBP and thus enabling more uncertainty to be handled with a precise mathematical methodology. The results show that this approach has relevance to credit risk assessment models in the sense that it proved its usefulness for the purpose of soft-hard data fusion, it modified Probability of Default with soft data modeled using possibilistic (fuzzy) distributions and fused with hard probabilistic data via UBP and it obtained better classification prediction results on the overall sample. This was demonstrated on a simple example of one soft variable, two experts and a small sample and thus this is an approach/method that requires further research, enhancements and rigorous statistical testing for the application to a complete scoring and/or rating system


Soft Data Hard Data Credit Default Uncertainty Balance Principle Expert Opinion Data Fusion

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Copyright (c) 2019 Migdat Hodzic, Sabina Brkić, Enis Džanić

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ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License