Article: An optimisation framework for audit decision-making based on deep convolutional neural networks and reinforcement learning Journal: International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC) 2026 Vol.52 No.5 pp.101 - 114 Abstract: In contemporary auditing and risk management, transaction complexity and high-dimensional audit data challenge accurate risk assessment and efficient resource allocation. Traditional methods relying on manual expertise or static heuristics fail in large-scale, multimodal, dynamic audit scenarios. To solve these, this paper proposes RLMCN-Net, a framework combining multimodal convolutional neural networks and deep reinforcement learning for audit risk assessment and action optimisation. Its risk identification module estimates audit targets' underlying risk from heterogeneous data; the reinforcement learning module determines audit actions considering costs, resource constraints, and expected returns. Risk labels, audit actions, and decision outcomes are modelled separately. Experiments on two benchmark datasets show RLMCN-Net outperforms traditional baselines in risk identification accuracy, audit return, and resource efficiency; ablation studies verify its robustness and generalisation. These results indicate RLMCN-Net effectively supports risk-oriented target screening and dynamic resource allocation, advancing intelligent auditing systems. Inderscience Publishers - linking academia, business and industry through research

Title: An optimisation framework for audit decision-making based on deep convolutional neural networks and reinforcement learning

Authors: Bing He; Shan Jiang; Osama Sohaib

Addresses: Accounting College, Zhengzhou University of Economics and Business, Zhengzhou, 451191, China ' Accounting College, Henan Institute of Economics and Trade, Zhengzhou, 450000, China ' School of Computer Science, University of Technology Sydney, Australia; Department of Statistics and Business Analytics, College of Business and Economics, United Arab Emirates University, Al Ain, UAE

Abstract: In contemporary auditing and risk management, transaction complexity and high-dimensional audit data challenge accurate risk assessment and efficient resource allocation. Traditional methods relying on manual expertise or static heuristics fail in large-scale, multimodal, dynamic audit scenarios. To solve these, this paper proposes RLMCN-Net, a framework combining multimodal convolutional neural networks and deep reinforcement learning for audit risk assessment and action optimisation. Its risk identification module estimates audit targets' underlying risk from heterogeneous data; the reinforcement learning module determines audit actions considering costs, resource constraints, and expected returns. Risk labels, audit actions, and decision outcomes are modelled separately. Experiments on two benchmark datasets show RLMCN-Net outperforms traditional baselines in risk identification accuracy, audit return, and resource efficiency; ablation studies verify its robustness and generalisation. These results indicate RLMCN-Net effectively supports risk-oriented target screening and dynamic resource allocation, advancing intelligent auditing systems.

Keywords: audit strategy optimisation; deep reinforcement learning; multimodal feature extraction; risk identification.

DOI: 10.1504/IJAHUC.2026.154117

International Journal of Ad Hoc and Ubiquitous Computing, 2026 Vol.52 No.5, pp.101 - 114

Received: 28 Jan 2026
Accepted: 19 Apr 2026
Published online: 12 Jun 2026 *

Title: An optimisation framework for audit decision-making based on deep convolutional neural networks and reinforcement learning

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