Original scientific paper
https://doi.org/10.2478/bsrj-2025-0010
Default Prediction in the Finance Industry Based on Ensemble Learning: Combining Machine Learning and Deep Learning
Hoanh-Su Le
orcid.org/0000-0002-3132-2550
; University of Economics and Law, Ho Chi Minh City, Vietnam Vietnam National University, Ho Chi Minh City, Vietnam
Quang Chan Phong Le
; University of Economics and Law, Ho Chi Minh City, Vietnam Vietnam National University, Ho Chi Minh City, Vietnam
Cong Vinh Truong
; University of Economics and Law, Ho Chi Minh City, Vietnam Vietnam National University, Ho Chi Minh City, Vietnam
Mai Minh Nhat Ho
; University of Economics and Law, Ho Chi Minh City, Vietnam Vietnam National University, Ho Chi Minh City, Vietnam
Jong-Hwa Lee
orcid.org/0000-0002-1213-6365
; Dong-Eui University, Busan City, South Korea
Abstract
Background: Financial institutions face significant challenges in predicting loan defaults, which directly impact the non-performing loan (NPL) rate. Incorrect predictions can lead to misinformed decisions and substantial financial losses. Objectives: This study aims to enhance default prediction by employing advanced ensemble learning techniques in machine learning and deep learning. Methods/Approach: Instead of relying on transformation, fine-tuning, or single algorithm models, this research focuses on combining multiple models using voting and stacking techniques, particularly highlighting a stacking model combining Light Gradient Boosting Machine (LGBM) and Artificial Neural Networks (ANN). Results: The ensemble learning methods, especially the LGBM-LSTM and XGB-LSTM stacking models, showed higher precision in identifying borrowers who defaulted, while the LGBM-LSTM and XGB-LSTM voting models excelled in recall and achieved an F1-score 0.1% higher. Both the stacking and voting models attained AUC values close to 90%, indicating strong overall classification performance. Conclusions: The findings not only contribute to the fields of lending and peer-to-peer financial operations but also offer crucial insights that aid financial organizations in making well-informed decisions regarding loan processing and management.
Keywords
default prediction; risk assessment; machine learning; deep learning; ensemble learning; online lending
Hrčak ID:
331492
URI
Publication date:
26.1.2025.
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