SIH26183: Real-Time Identification of Fraud-Linked Cryptocurrency Exchanges from Victim-Re — SIH 2026 | Wynit

SIH26183: Real-Time Identification of Fraud-Linked Cryptocurrency Exchanges from Victim-Re — SIH 2026 | Wynit

What they'll ask you

14

Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.

Which specific blockchain networks must be supported?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
What exact data schemas are used by the SAHYOG and NCRP platforms for integration?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
What specific performance or speed metrics define real-time tracing?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.

11 morethe ones a panel gets to if there is time

What classification criteria constitute the risk categorization of wallets?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for wallet ingestion module. How would you build that?
Decoded from SIH26183 itself: Ingests suspect cryptocurrency wallet addresses reported through cybercrime complaint systems. The brief asks for it by name.
The brief asks for blockchain tracing engine. How would you build that?
Decoded from SIH26183 itself: Automatically traces transactions, multi-chain transfers, and fund flows across multiple blockchain ecosystems. The brief asks for it by name.
The brief asks for exchange identification module. How would you build that?
Decoded from SIH26183 itself: Identifies the associated cryptocurrency exchanges or VASPs receiving direct deposits and clusters exchange wallets. The brief asks for it by name.
The brief asks for visualization and analytics. How would you build that?
Decoded from SIH26183 itself: Provides blockchain transaction graph analysis, fund-flow visualization, and analytics dashboards for investigators. The brief asks for it by name.
The brief asks for system integration module. How would you build that?
Decoded from SIH26183 itself: Integrates with SAHYOG, NCRP platforms, and other law enforcement agency systems via APIs. The brief asks for it by name.
The brief asks for intelligence and reporting. How would you build that?
Decoded from SIH26183 itself: Generates automated alerts, risk categorizations, AI/ML-assisted risk detection, and standardized investigation reports. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.

The government's original description (353 words)

• 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.