HACKATHON PS

[ABHINAVGUPTA00]

• 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.