Assessment of Class Imbalance Data Handling with Attention-Based Deep Learning Approach for Robust Financial Distress Prediction in Enterprises

Alisher Sherov; Tukhtabek Rakhimov; Hafis Hajiyev; Maria Zelinskaya; Gavkhar Khidirova

doi:10.48084/etasr.14843

Authors

Alisher Sherov Department of Economics, Mamun University, Khiva, Uzbekistan | Department of Finance and Tourism, Termez University of Economics and Service, Termez, Uzbekistan
Tukhtabek Rakhimov Department of Economics, Urgench State University, Uzbekistan
Hafis Hajiyev Department of Finance and Audit, Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan
Maria Zelinskaya Department of Management, Kuban State Agrarian University named after I.T. Trubilin, Krasnodar, Russia
Gavkhar Khidirova Department of Tourism and Hotel Management, Bukhara State University, Bukhara, Uzbekistan

Volume: 15 | Issue: 6 | Pages: 30053-30058 | December 2025 | https://doi.org/10.48084/etasr.14843

Received: 16 September 2025 | Revised: 3 October 2025 and 16 October 2025 | Accepted: 19 October 2025 | Online: 8 December 2025

Corresponding author: Alisher Sherov

Abstract

Bankruptcy prediction and credit risk assessment are two of the most crucial problems in finance, remaining an advanced area of research in the financial sector. Financial distress is when an enterprise faces financial problems. Standard analytical methods often depend on limited financial indicators and expert opinions, which miss complex non-linear patterns. With the expansion of Artificial Intelligence (AI) and Deep Learning (DL), financial stress testing has experienced a fundamental change, utilizing progressive methods of computation to improve the precision of risk prediction. This study presents an Attention-based Deep Learning Approach for Robust Financial Distress Prediction in Enterprises (ADLA-RFDPE) method to improve the detection of financial risks in enterprise management. Initially, min-max normalization is used to maintain data consistency, and SMOTE is utilized to address class imbalance. Then, the Elephant Herding Lion Optimizer (EHLO) is used for the dimensionality reduction process. For financial distress classification, an Attention-based Gated Recurrent Unit (Attention-GRU) technique is employed, optimized with the Pied Kingfisher Optimizer (PKO) technique. The experimental results highlighted a superior accuracy of 98.68% over existing approaches on an Australian credit dataset.

Keywords:

financial distress prediction, deep learning, bankruptcy, data analysis, class imbalance, elephant herding lion optimizer

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