Real-Time Identification of Fraud-Linked Cryptocurrency Exchanges from Victim-Reported Suspect Wallet Addresses through Automated Blockchain Analytics

• •Background Cyber fraud victims increasingly report suspect cryptocurrency wallet addresses used by fraudsters for collection of funds in cases involving:
• •investment scams,
• •task-based frauds,
• •sextortion,
• •ransomware,
• •phishing,
• •darknet transactions,
• •and organized cyber-enabled financial crimes.

During investigations, the reported wallet addresses are often:

• •non-custodial wallets,
• •temporary burner wallets,
• •or intermediary wallets used for layering and laundering.

The inability to quickly identify the cryptocurrency exchange or VASP associated with these wallets delays:

• •freezing of assets,
• •preservation of evidence,
• •tracing of fund flows,
• •and victim fund recovery.

Manual blockchain tracing requires significant technical expertise and time, particularly in cases involving:

• •multi-chain transfers,
• •DeFi protocols,
• •mixers/tumblers,
• •bridges,
• •and privacy-enhancing mechanisms.
• •Description The proposed solution envisages a Real-Time Crypto Fraud Attribution System capable of automatically analyzing victim-reported wallet addresses and identifying the nearest exchange or VASP receiving direct deposits.

The system should:

• •ingest wallet addresses reported through cybercrime complaint systems,
• •automatically perform blockchain tracing,
• •identify associated exchanges or VASPs,
• •detect fund movement patterns,
• •and generate actionable intelligence for investigators.

Key features may include

• •blockchain transaction graph analysis,
• •clustering of exchange wallets,
• •detection of intermediary laundering wallets,
• •identification of cross-chain fund movement,
• •integration with SAHYOG and NCRP platforms,
• •automated alert generation,
• •and risk categorization of wallets.

The system should support multiple blockchain ecosystems and provide:

• •real-time tracing capability,
• •automated investigative recommendations,
• •and analytics dashboards for law enforcement agencies
• •Expected Solution A software platform capable of:
• •real-time blockchain intelligence generation,
• •automated VASP identification,
• •tracing of suspect wallets,
• •cross-chain transaction analytics,
• •fund-flow visualization,
• •integration with LEA systems,
• •and generation of standardized investigation reports.

The system should:

• •reduce response time in cyber fraud investigations,
• •improve freezing of proceeds of crime,
• •enhance coordination with VASPs,
• •and strengthen digital evidence collection capabilities.

The platform should further support:

• •API integrations,
• •scalable blockchain indexing,
• •AI/ML-assisted risk detection,
• •and automated pattern recognition for fraud typologies.