Determinants of non-performing loans: An explainable ensemble and deep neural network approach

Chioma Ngozi Nwafor and Obumneme Zimuzor Nwafor

Finance Research Letters, 2023, vol. 56, issue C

Abstract: Ensemble algorithms can learn complex nonlinear relationships in large datasets resulting in higher predictive accuracies than the conventional methods. Practitioners and regulators have shown substantial hesitance in adopting them in credit risk management because of their need for explainablity. Using five ensemble learning techniques and a one-dimensional convolutional neural network, we assess indicators to predict asset quality deterioration in a consumer loan dataset using the SHAP framework to achieve explainablity of the models' ranking of features significance. We implemented a novel model-agnostic aggregate ranking method to rank the importance of the overall features from each model in predicting NPLs.

Keywords: Non-performing loans; Credit risk; Ensemble methods; Explainable artificial intelligence (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612323004567
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:56:y:2023:i:c:s1544612323004567

DOI: 10.1016/j.frl.2023.104084

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().