Application of blockchain-driven big data analytics in financial fraud detection in engineering management

Abstract

This work proposes a blockchain-based computing model with differential privacy for fraud detection in mobile edge computing (MEC) environments. The model enables edge nodes to collaborate securely, ensuring accurate and trustworthy fraud diagnosis. A top-down structure is recommended for financial records, adaptively partitioned for efficiency. The differential privacy mechanism employs randomized responses, while the blockchain-secured computational model (BSCM) safeguards customer and transaction privacy. Traditional fraud detection may delay detection and response due to real-time detection restrictions. The proposed system identifies fraud in real time, saving financial losses. The blockchain paradigm provides transparent, tamper-proof transaction records and secure data storage, unlike current systems. The proposed solution employs oversampling and undersampling to handle imbalanced datasets with more fraudulent transactions than lawful ones. Theoretical analysis shows real-time fraud detection is achievable with minimal error rates while preserving privacy. Compared to client-server models, BSCM reduces data write execution time by 2.6x and improves data retrieval efficiency by 20x. Experiments also evaluate the impact of larger financial datasets. The financial record selection and information extraction models achieved 96.5% and 95.2% accuracy, respectively, with F1-scores of 94.6% and 93.1%, balancing precision and recall. The edge-sharing network demonstrated strong performance in response time, throughput, packet loss, and latency. Additionally, the blockchain-based transaction verification system achieved 99.9% accuracy, excelling in verification speed, network latency, and throughput.