Intelligent Transaction Prediction and Fraud Detection in Crypto Markets Using Java and Generative AI

Abstract

Cryptocurrency markets face unprecedented fraud challenges with estimated annual losses exceeding $14 billion globally, while traditional detection systems struggle with the unique characteristics of blockchain transactions and rapidly evolving fraud patterns. This research develops an intelligent framework combining Java enterprise architecture with generative AI capabilities to predict fraudulent transactions and detect emerging fraud schemes in cryptocurrency markets. The system integrates on-chain transaction analysis, behavioral pattern recognition, and large language model reasoning to identify suspicious activities across multiple fraud categories including wash trading, pump-and-dump schemes, phishing attacks, and rug pulls. Through evaluation using Ethereum and Bitcoin blockchain data spanning 24 months, the framework achieves 91.7% fraud detection accuracy with 23% false positive reduction compared to rule-based systems. The implementation processes 45,000 transactions per hour while generating human-interpretable fraud alerts suitable for investigator review. Novel contributions include a cross-chain fraud pattern library learned from historical cases, adaptive detection thresholds responding to market volatility, and automated fraud narrative generation explaining suspicious patterns in investigator-friendly language. Performance analysis demonstrates the system scales to enterprise transaction volumes while maintaining sub-3-second alert latency. Results indicate that combining traditional transaction graph analysis with generative AI contextual reasoning substantially improves detection of sophisticated fraud schemes that evade conventional rule-based approaches.