Leveraging the Variational Autoencoder with the Blockchain Smart Contracts Model for Strengthening Fraud Detection in Financial Sectors - Allehaibi, Khalid Hamed (Engineering, Technology & Applied Science Research, 2026)
Financial fraud has increased and evolved alongside recent technological developments, raising the need for systems that are able to detect and prevent it. One such system is Blockchain (BC), which enables computerized functionality and on-the-spot verification, while being both cost-efficient and adequately productive, and it is expected to influence domains such as accountancy and its related services. Additionally, combining the security and transparency of BC with the analytical capabilities of Machine Learning (ML) can offer a more promising system for detecting and preventing financial fraud than a standalone BC. Motivated by this idea, this study presents the Leveraging Variational Autoencoder with Smart Contracts to Strengthen Fraud Detection (LVAESC-SFD) model, which aims to examine the contribution of Smart Contracts (SCs) in enhancing security and fraud detection in financial applications. The model operates by employing the Correlation-based Feature Selection (CFS) technique for selecting an optimal subset of features, and then a Variational Autoencoder (VAE) for performing fraud detection and classification. The LVAESC-SFD model was evaluated using a financial fraud detection dataset listing millions of transactions and achieved an accuracy of 98.24%, outperforming existing models.