A Synergistic Framework of Deep Learning and Blockchain for Immutable and Intelligent Fraud Detection in Financial Ecosystems

Abstract The escalating sophistication of financial fraud necessitates a paradigm shift from conventional detection systems toward frameworks characterized by heightened intelligence, security, and transparency. The present study addresses a critical lacuna in the extant literature by proposing a novel, synergistic architecture that integrates Deep Learning (DL) with Blockchain technology to manifest a robust ecosystem for fraud detection. A dual-core engine is introduced, comprising: (1) a Long Short-Term Memory (LSTM) network, optimized for the capture of temporal dependencies within transactional data, and (2) a permissioned Hyperledger Fabric blockchain, which serves as an immutable trust layer for data integrity and the automated execution of responses via Smart Contracts. The proposed model underwent rigorous evaluation utilizing the benchmark IEEE-CIS Fraud Detection dataset. The framework achieved an exceptional F1-Score of 0.98 and an AUC of 0.99, thereby significantly outperforming standalone DL models and traditional methodologies. It is demonstrated, crucially, that by ensuring data integrity, the blockchain layer enhances the model's resilience against data poisoning attacks—a critical vulnerability in modern artificial intelligence systems. Performance analysis reveals a mean transaction latency of 450ms under significant load, confirming the system's viability for real-time deployment. This research establishes a new benchmark for secure artificial intelligence in finance, providing evidence that the fusion of DL and blockchain can create a transparent, auditable, and highly accurate defense against sophisticated financial fraud, thereby paving the way for a new generation of trustworthy computational systems in critical sectors.

کلیدواژه ها:

Financial Fraud Detection ، Deep Learning ، Blockchain ، Smart Contracts ، Long Short-Term Memory (LSTM) ، Secure Artificial Intelligence.