Fraud detection and prevention of financial crime using blockchain is a rapidly growing research area that leverages blockchain’s transparency, immutability, and decentralized nature to combat illegal activities such as money laundering, identity theft, insider trading, and fraudulent transactions. Research papers in this domain explore blockchain-based frameworks for real-time monitoring, audit trails, and anomaly detection that enable regulators and financial institutions to identify suspicious patterns across distributed ledgers. Studies highlight the role of smart contracts in automating compliance checks, Know Your Customer (KYC), and Anti-Money Laundering (AML) protocols to reduce human error and manipulation. Recent works integrate blockchain with artificial intelligence, machine learning, and data analytics to build predictive models for fraud detection while preserving privacy through cryptographic techniques like zero-knowledge proofs and homomorphic encryption. Hybrid blockchain architectures—combining permissioned and permissionless models—are also studied to balance scalability, transparency, and regulatory compliance. Applications include secure digital payments, cross-border transactions, insurance claims, and decentralized finance (DeFi), where fraud prevention is critical for trust and adoption. Overall, research in fraud detection and financial crime prevention using blockchain provides secure, auditable, and intelligent mechanisms to protect global financial ecosystems from misuse and exploitation.