Financial fraud detection using a hybrid deep belief network and quantum optimization approach

Abstract

In the contemporary global economic landscape, financial fraud represents a significant challenge, resulting in substantial losses for market participants, including business enterprises and financial institutions. This phenomenon has a profound impact on market stability, significantly affecting the management of the economy. To address this issue, this paper proposes a novel financial fraud detection algorithm that integrates deep belief networks (DBN) with quantum optimisation algorithms. The proposed model employs a hybrid model optimisation strategy that integrates convolutional neural networks (CNNs), long short-term memory networks (LSTMs) and graph neural networks (GNNs).Conventional detection methods depend on manual rules and statistical analyses, which are inadequate for handling large-scale, high-density and complex financial market data. Recent advancements in deep learning have demonstrated potential in addressing these challenges; however, they are often hindered by issues related to computational efficiency and training time. The proposed integrated approach in this paper combines deep learning with quantum computing to overcome these limitations. The hybrid model utilises the parallel processing power of quantum computing to improve the training efficiency of DBNs, while CNNs, LSTMs and GNNs extract features from multiple dimensions of financial market data. Experimental results demonstrate the proposed model's advantages in terms of accuracy, training speed and robustness, providing a promising solution for financial fraud detection.

Article highlights

Quantum-Enhanced Fraud Detection: A novel quantum-optimized deep belief network achieves 88.7% precision and 86.5% recall, outperforming traditional methods in fraud detection efficiency and accuracy.

Hybrid Model for Robust Fraud Detection: Integration of CNN, LSTM, and GNN extracts spatial, temporal, and relational features to enhance detection robustness for complex fraud patterns.

Economic Benefits and Cost-Effective Deployment: The model reduces fraud-related economic losses and deployment costs, offering a cost-effective solution with high computational efficiency for financial institutions.