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

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.