Leveraging the Variational Autoencoder with the Blockchain Smart Contracts Model for Strengthening Fraud Detection in Financial Sectors

Khalid Hamed Allehaibi

doi:10.48084/etasr.13371

Authors

Khalid Hamed Allehaibi Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

Volume: 16 | Issue: 1 | Pages: 32712-32717 | February 2026 | https://doi.org/10.48084/etasr.13371

Received: 14 July 2025 | Revised: 30 July 2025, 21 August 2025, 2 September 2025, and 9 September 2025 | Accepted: 11 September 2025 | Online: 9 February 2026

Corresponding author: Khalid Hamed Allehaibi

Abstract

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.

Keywords:

fraud detection, smart contracts, financial sector, variational autoencoder, blockchain, correlation-based feature selection

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References

L. Liu, W.-T. Tsai, Md. Z. A. Bhuiyan, H. Peng, and M. Liu, "Blockchain-enabled fraud discovery through abnormal smart contract detection on Ethereum," Future Generation Computer Systems, vol. 128, pp. 158–166, Mar. 2022. DOI: https://doi.org/10.1016/j.future.2021.08.023

C. Ubagaram, R. R. Mandala, B. S. Jayaprakasam, V. Garikipati, N. R. Dyavani, and G. A. Ogunmola, "An Improved Fedformer-Dilated Residual CNN Framework with Cross-Attentive Fusion for Cloud-Based Cyber Threat Detection and Financial Fraud Prediction," International Journal of Innovation and Technology Management, Oct. 2025. DOI: https://doi.org/10.1142/S021987702540005X

S. Sh. Taher, S. Y. Ameen, and J. A. Ahmed, "Advanced Fraud Detection in Blockchain Transactions: An Ensemble Learning and Explainable AI Approach," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12822–12830, Feb. 2024. DOI: https://doi.org/10.48084/etasr.6641

H. O. Bello, C. Idemudia, and T. V. Iyelolu, "Integrating machine learning and blockchain: Conceptual frameworks for real-time fraud detection and prevention," World Journal of Advanced Research and Reviews, vol. 23, no. 1, pp. 056–068, July 2024. DOI: https://doi.org/10.30574/wjarr.2024.23.1.1985

S. Lakkaraju, "Using Machine Learning to Combat E-Commerce Fraud," International Journal of Information Technology and Management Information Systems, vol. 16, no. 1, pp. 844–859, Feb. 2025. DOI: https://doi.org/10.34218/IJITMIS_16_01_060

A. A. Alhashmi, A. M. Alashjaee, A. A. Darem, A. F. Alanazi, and R. Effghi, "An Ensemble-based Fraud Detection Model for Financial Transaction Cyber Threat Classification and Countermeasures," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12433–12439, Dec. 2023. DOI: https://doi.org/10.48084/etasr.6401

A. Louja, A. Jamali, and N. Naja, "Blockchain-Powered Artificial Intelligence for Healthcare Systems Data Orchestration," in Proceedings of the Third ICMDS’24: Machine Learning, Inverse Problems and Related Fields, vol. 1466, A. Laghrib and A. Ghazdali, Eds. Cham: Springer Nature Switzerland, 2025, pp. 155–163. DOI: https://doi.org/10.1007/978-3-031-94802-2_15

B. Annane, A. Alti, and A. Lakehal, "A Blockchain Semantic-based Approach for Secure and Traceable Agri-Food Supply Chain," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 18131–18137, Dec. 2024. DOI: https://doi.org/10.48084/etasr.8908

S. C. Prabanand and M. S. Thanabal, "Advanced financial security system using smart contract in private ethereum consortium blockchain with hybrid optimization strategy," Scientific Reports, vol. 15, no. 1, Feb. 2025, Art. no. 6764. DOI: https://doi.org/10.1038/s41598-025-89404-3

F. Yuan et al., "AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection," Algorithms, vol. 18, no. 5, May 2025, Art. no. 263. DOI: https://doi.org/10.3390/a18050263

H. Louati et al., "Adopting Artificial Intelligence to Strengthen Legal Safeguards in Blockchain Smart Contracts: A Strategy to Mitigate Fraud and Enhance Digital Transaction Security," Journal of Theoretical and Applied Electronic Commerce Research, vol. 19, no. 3, pp. 2139–2156, Aug. 2024. DOI: https://doi.org/10.3390/jtaer19030104

A. Alenizi, S. Mishra, and A. Baihan, "Enhancing secure financial transactions through the synergy of blockchain and artificial intelligence," Ain Shams Engineering Journal, vol. 15, no. 6, June 2024, Art. no. 102733. DOI: https://doi.org/10.1016/j.asej.2024.102733

O. S. Adanigbo, F. S. Ezeh, U. S. Ugbaja, C. I. Lawal, and S. C. Friday, "Advances in Blockchain and IoT Applications for Secure, Transparent, and Scalable Digital Financial Transactions," International Journal of Advanced Multidisciplinary Research and Studies, vol. 4, no. 6, pp. 1863–1869, Dec. 2024. DOI: https://doi.org/10.62225/2583049X.2024.4.6.4158

O. Odeyemi, C. C. Okoye, O. C. Ofodile, O. B. Adeoye, W. A. Addy, and A. O. Ajayi-Nifise, "Integrating AI with Blockchain for Enhanced Financial Services Security," Finance & Accounting Research Journal, vol. 6, no. 3, pp. 271–287, Mar. 2024. DOI: https://doi.org/10.51594/farj.v6i3.855

T. K. Vashishth, V. Sharma, V. Kaushik, and K. K. Sharma, "Blockchain-Driven Innovations in the Banking and Financial Sectors: Harnessing the Power of Automated Machine Learning," in Advances in Finance, Accounting, and Economics, D. Darwish and S. Kumar, Eds. IGI Global, 2024, pp. 555–578. DOI: https://doi.org/10.4018/979-8-3693-8507-4.ch029

M. F. Manzoor and A. Muhammad Faran, "Enhancing Banking Fraud Detection: Role of Machine Learning and Deep Learning Methods," Premier Journal of Artificial Intelligence, 2025, Art. no. 100014. DOI: https://doi.org/10.70389/PJAI.100014

B. R. Gudivaka, M. Almusawi, M. S. Priyanka, M. R. Dhanda, and M. Thanjaivadivel, "An Improved Variational Autoencoder Generative Adversarial Network with Convolutional Neural Network for Fraud Financial Transaction Detection," in 2024 Second International Conference on Data Science and Information System (ICDSIS), Hassan, India, May 2024, pp. 1–4. DOI: https://doi.org/10.1109/ICDSIS61070.2024.10594271

H. Abbassi, S. El Mendili, and Y. Gahi, "Adaptive, Privacy-Enhanced Real-Time Fraud Detection in Banking Networks Through Federated Learning and VAE-QLSTM Fusion," Big Data and Cognitive Computing, vol. 9, no. 7, July 2025, Art. no. 185. DOI: https://doi.org/10.3390/bdcc9070185

S. R. Peddinti, A. Tanikonda, and S. R. Katragadda, "Deep Learning for Anomaly Detection in E-commerce and Financial Transactions: Enhancing Fraud Prevention and Cybersecurity," SSRN Electronic Journal, vol. 10, no 30s, pp. 70-77, Feb. 2025. DOI: https://doi.org/10.52783/jisem.v10i30s.4776

H. Elsharkawi, E. Elbeltagi, M. S. Eid, W. Alattyih, and H. Wefki, "Construction Payment Automation Through Scan-to-BIM and Blockchain-Enabled Smart Contract," Buildings, vol. 15, no. 2, Jan. 2025, Art. no. 213. DOI: https://doi.org/10.3390/buildings15020213

E. Godini, H. Zareiforoush, A. Bakhshipour, Z. Lorigooini, and S. H. Payman, "Intelligent Grading of Green Cardamom Using Data Fusion of Electronic Nose and Computer Vision Methods," Food Science & Nutrition, vol. 13, no. 4, Apr. 2025, Art. no. e4645. DOI: https://doi.org/10.1002/fsn3.4645

H. Hamdaouy, E. M. Benghoulam, M. Chaibi, M. Berrada, and A. E. Hmaidi, "Estimating daily global solar radiation using deep learning," Results in Engineering, vol. 27, Sept. 2025, Art. no. 106132. DOI: https://doi.org/10.1016/j.rineng.2025.106132

Financial Fraud Detection Dataset. (2023), S. H. Eedala. [Online]. Available: https://www.kaggle.com/datasets/sriharshaeedala/financial-fraud-detection-dataset.

M. Akouhar, M. Ouhssini, M. El Fatini, A. Abarda, and E. Agherrabi, "Dynamic oversampling-driven Kolmogorov–Arnold networks for credit card fraud detection: An ensemble approach to robust financial security," Egyptian Informatics Journal, vol. 31, Sept. 2025, Art. no. 100712. DOI: https://doi.org/10.1016/j.eij.2025.100712

K. H. Ahmed, S. Axelsson, Y. Li, and A. M. Sagheer, "A credit card fraud detection approach based on ensemble machine learning classifier with hybrid data sampling," Machine Learning with Applications, vol. 20, June 2025, Art. no. 100675. DOI: https://doi.org/10.1016/j.mlwa.2025.100675

C. Huot, S. Heng, T.-K. Kim, and Y. Han, "Quantum Autoencoder for Enhanced Fraud Detection in Imbalanced Credit Card Dataset," IEEE Access, vol. 12, pp. 169671–169682, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3496901

S. Shi, W. Luo, and G. Pau, "An attention-based balanced variational autoencoder method for credit card fraud detection," Applied Soft Computing, vol. 177, June 2025, Art. no. 113190. DOI: https://doi.org/10.1016/j.asoc.2025.113190