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Explaining Deep Learning Models for Credit Scoring with SHAP: A Case Study Using Open Banking Data.
- Source :
- Journal of Risk & Financial Management; Apr2023, Vol. 16 Issue 4, p221, 19p
- Publication Year :
- 2023
-
Abstract
- Predicting creditworthiness is an important task in the banking industry, as it allows banks to make informed lending decisions and manage risk. In this paper, we investigate the performance of two different deep learning credit scoring models developed on the textual descriptions of customer transactions available from open banking APIs. The first model is a deep learning model trained from scratch, while the second model uses transfer learning with a multilingual BERT model. We evaluate the predictive performance of these models using the area under the receiver operating characteristic curve (AUC) and Brier score. We find that a deep learning model trained from scratch outperforms a BERT transformer model finetuned on the same data. Furthermore, we find that SHAP can be used to explain such models both on a global level and for explaining rejections of actual applications. [ABSTRACT FROM AUTHOR]
- Subjects :
- BANKING industry
CREDIT ratings
DEEP learning
LANGUAGE models
TRANSFORMER models
Subjects
Details
- Language :
- English
- ISSN :
- 19118066
- Volume :
- 16
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Journal of Risk & Financial Management
- Publication Type :
- Academic Journal
- Accession number :
- 163436957
- Full Text :
- https://doi.org/10.3390/jrfm16040221