Revolutionizing Management with AI and Blockchain for Smarter Anomaly Detection and Fraud Prevention

Abstract

In today’s rapidly evolving digital landscape, businesses and financial institutions face increasingly sophisticated fraudulent activities, cyber threats, and operational anomalies. Traditional rule-based fraud detection methods often fail to counter these evolving threats effectively. This article explores the integration of Artificial Intelligence (AI) and Blockchain technology as a revolutionary approach to fraud detection and anomaly prevention in dynamic management systems. AI-driven models, including machine learning, deep learning, and predictive analytics, play a crucial role in identifying fraudulent patterns in real-time. The proposed framework employs advanced AI techniques such as tree-based models (Random Forest, XGBoost), deep learning architectures (autoencoders, fully connected neural networks), and sequential models (LSTM, GRU) to enhance fraud detection capabilities. Additionally, statistical methods, including the Five Number Summary, Z-Score Analysis, and Chi-Square Tests, further refine anomaly detection by identifying deviations in transaction behaviors. Blockchain technology reinforces security through its decentralized, tamper-resistant ledger, preventing unauthorized data alterations and ensuring transparent auditing. By integrating AI and Blockchain, this framework enhances fraud detection accuracy, minimizes false positives, and strengthens risk management. This synergy offers a comprehensive, intelligent, and secure solution for modern financial and business management systems, effectively safeguarding operations against evolving fraud tactics.