Home/Problem Statements/SIH26183

SIH26183SoftwareBlockchain & Cybersecurity

₹1,50,000 Prize (₹75k Joint)

•

Deadline: 20 September 2026

•

Submitted Ideas: 0/500

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

Ministry / Organization: Ministry of Home Affairs

Department: Indian Cyber Crime Coordination Centre (I4C),CIS Division

SIH Official Portal

Technical Complexity

8/10

Dataset Accessibility

7/10

36h Hackathon Viability

9/10

Step 0 • Zero-Jargon Layman Summary

“When cyber fraud victims report stolen cryptocurrency, police struggle to track where the funds went because scammers hide them through multiple dummy wallets. This platform automatically traces those stolen crypto tokens across different blockchains to pinpoint the exact commercial exchange where the criminal is about to cash out, allowing police to freeze the assets immediately.”

The DecodeX PS Decoder™

Deconstructed Problem Blueprint

Step 1

WHAT TO BUILD?

An enterprise-grade Real-Time Crypto Fraud Attribution and Blockchain Forensics Software Platform.

Step 2

WHO IS IT FOR?

The problem belongs to the Ministry of Home Affairs (Indian Cyber Crime Coordination Centre - I4C). The primary users are law enforcement agency (LEA) investigators, cybercrime cell officers, and financial intelligence units in India.

Step 3

WHY DOES IT EXIST?

Manual blockchain investigation requires specialized expertise and hours of laborious manual work across block explorers. Meanwhile, cybercriminals rapidly move funds across cross-chain bridges and privacy mixers to cash out on centralized exchanges before police can issue legal freeze notices.

Step 4

INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE

01 • Inputs & Telemetry

Victim-reported scam suspect wallet addresses, associated transaction hashes, complaint metadata from NCRP (National Cyber Crime Reporting Portal) and SAHYOG systems.

02 • Processing & AI Core

Multi-chain transaction graph indexing, heuristic and ML-based clustering of exchange deposit addresses, heuristic identification of laundering hops, cross-chain bridge mapping, and risk-scoring algorithms.

03 • Actionable Deliverables

Actionable investigator dashboards, visual fund-flow dependency graphs, automated VASP (Virtual Asset Service Provider) identification alerts, and standardized mutual legal assistance / exchange freeze report documents.

Step 5

CONSTRAINTS

Must support high-throughput real-time indexing across multiple EVM and non-EVM chains (Bitcoin, Ethereum, Tron, Polygon, etc.)
Strict data privacy and compliance standards for handling sensitive law enforcement data
API rate-limiting constraints of public blockchain nodes and block explorer APIs

Step 6

JURY METRICS

End-to-end tracing and VASP identification latency (under 30 seconds per query)
Accuracy of exchange deposit address clustering (precision and recall)
UI/UX simplicity and clarity for non-technical law enforcement officers
Seamless mock integration with NCRP/SAHYOG data structures

Step 7

CORE BOTTLENECK

Accurately clustering thousands of anonymous exchange deposit addresses without having proprietary internal exchange datasets, and handling multi-hop obfuscation through mixers and cross-chain bridges.

Why You Should Choose This PS (Pros)

Extremely high national security and social impact directly solving a major mandate for the Ministry of Home Affairs
Clear, structured deliverables with obvious visual components (dashboards and interactive graphs)
Strong 'wow factor' during live demos via dynamic fund-flow visualizations

Challenges & Risks to Watch (Cons)

Requires handling complex graph databases and real-time streaming data architectures
Public blockchain RPC nodes can be unstable or rate-limited during heavy usage
Crowded domain if teams only build a basic block explorer wrapper without proprietary intelligence logic

Hackathon Blueprint

Recommended Tech Stack & Grand Finale Features

Frontend & Mobile

React.js / Next.jsTailwind CSSCytoscape.js or D3.js for interactive fund-flow graph visualization

Backend & APIs

FastAPI / PythonCelery for asynchronous background blockchain indexing tasksWebSockets for real-time alerts

AI / ML & Models

Graph Neural Networks (GNNs) or heuristic clustering models for wallet attributionIsolation Forest for anomaly and risk scoring of transaction patterns

Database & Cloud

PostgreSQL with PostGIS/pgvectorNeo4j or NetworkX for blockchain transaction graph traversalRedis for caching wallet labels and session data

Killer Features to Impress the SIH Jury

1-Click 'Freeze-Ready' Legal Notice Generator that automatically formats wallet intelligence into formal notices for exchanges

AI-powered automated typology classification (instantly tagging whether a scam is an investment trap, task scam, or digital arrest)

Interactive time-slider on the graph visualization to replay fund movement chronologically step-by-step

Cross-chain bridge tracker that automatically links source chain transactions to destination chain deposits

View Official SIH 2026 Portal Raw Text

▼

•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.