Journal of Engineering, Project, and Production Management, 2026, 16(5), 2025-189
Deep Learning and Metaheuristic Approaches for Financial Risk Prediction in Digital Economy
Lecture, School of Computer and Artificial Intelligence, Henan Finance University, Zhengzhou, 450046, China, E-mail: Guoqz.hang@outlook.com
Project Management
Received August 27, 2025; revised December 1, 2025; December 10, 2025; accepted January 6, 2026
Available online June 17, 2026
Abstract: Predicting financial risk has become increasingly important as digital-economy firms generate highly volatile and complex data. Existing Deep-Learning (DL) models struggle with high-dimensional inputs, unstable optimization, and limited generalization. To address these issues, this study proposes a hybrid framework combining Quantum-controlled Long Short-Term Memory (Q-LSTM) networks with a Multi-Objective Artificial Bee Colony optimizer (MOABC). Using quarterly data from twelve Chinese e-commerce firms (2012–2022), the model optimizes learning rate and hidden-layer configuration after reducing 14 financial indicators to 5 latent factors via factor analysis. Experimental results show that MOABC-QLSTM achieves superior accuracy, with a Mean Squared Error (MSE) of 0.0067, a Mean Absolute Percentage Error (MAPE) of 3.88%, and an R² of 99.78%. It outperforms Particle Swarm Optimization (PSO), Differential Evolution (DE), and Genetic Algorithm (GA) tuning, LSTM models, and other state-of-the-art approaches. The findings demonstrate that integrating quantum-inspired temporal modelling with swarm-based multi-objective optimization provides a stable and effective early warning system, supporting regulators, auditors, and financial institutions in risk monitoring within the digital economy.
Keywords: Financial risk prediction, deep learning, digital economy, meta-heuristic approach, LSTM, quantum, artificial bee colony, parameter tuning.
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Citation: Zhang, G. (2026). Deep Learning and Metaheuristic Approaches for Financial Risk Prediction in Digital Economy. Journal of Engineering, Project, and Production Management, 16(5), 2025-189.
