Citation

Chohan, Muhammad Ali and Ramakrishnan, Suresh and Abrar, Mohammad and Butt, Shamaila and Kamal, Shahid (2026) AFT-Attentive BiLSTM: Improving Early Warning of Firm Financial Distress with Temporal Attention in an Accelerated Failure Time Framework. International Journal of Advanced Computer Science and Applications, 17 (4). ISSN 2158107X

Text Paper_86-AFT_Attentive_BiLSTM.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Early warning systems (EWS) for firm-level financial distress are essential for identifying potential bankruptcies or insolvencies before their realization. While traditional statistical models such as Z-score and logistic regression offer interpretability, they lack the ability to capture nonlinear and temporal dependencies in financial data. Recent deep learning approaches improve predictive accuracy but often sacrifice interpretability. The purpose of this study is to develop and evaluate a novel deep learning-based early warning model for firm-level financial distress that integrates temporal attention with parametric survival analysis to improve both predictive accuracy and interpretability. Therefore, this study proposes an AFT-Attentive BiLSTM model that integrates a Bidirectional Long Short-Term Memory (BiLSTM), a temporal attention mechanism, and a lognormal Accelerated Failure Time (AFT) survival framework. The model predicts time-to-distress distributions rather than binary outcomes, enabling probabilistic early warnings with calibrated survival probabilities. Empirical results demonstrate that the proposed model outperforms Cox Proportional Hazards, DeepSurv, and prior AFT-BiLSTM models without attention. The inclusion of temporal attention improves concordance index (C-index), Integrated Brier Score (IBS), and time-dependent AUC, and provides interpretable insights by identifying critical financial periods preceding distress. Kaplan–Meier analysis confirms strong separation between high- and low-risk groups. The findings suggest that combining temporal attention with parametric survival modeling enhances both predictive accuracy and interpretability in financial distress early warning systems.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, corporate bankruptcy
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD2321-4730.9 Industry Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Jun 2026 02:43
Last Modified:	30 Jun 2026 02:43
URII:	http://shdl.mmu.edu.my/id/eprint/16117

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