As blockchain becomes increasingly integrated into traditional financial infrastructure through stablecoins, tokenized real world assets, and exchange traded products, on chain financial crime has evolved beyond an exchange security issue into a broader risk for financial institutions, VASPs, payment providers, and regulators.

This report, jointly published by Lambda256 and Decipher, the blockchain research group at Seoul National University, examines how modern laundering schemes combine privacy protocols, cross chain bridges, decentralized trading, memecoins, and NFT activity within a single attack lifecycle. It also highlights the limitations of conventional address based detection methods when investigating increasingly complex fund flows.

The report introduces FRAML (Fraud Detection and Anti Money Laundering) as an integrated framework for identifying and responding to on chain financial crime. It further outlines the operational architecture of CLAIR FRAML, including its ontology based knowledge graph, multi layer transaction tracing, and entity centric analysis, supported by real world implementation examples.

Designed for compliance, risk, security, and investigative teams, this report provides practical guidance for organizations developing institutional grade on chain monitoring, AML, and financial crime investigation capabilities.

Table of Contents

1. Why On-Chain Financial Crime Compliance Matters Now

• -1.1 Blockchain Is Being Integrated Into Financial Infrastructure
• -1.2 The Scope of Financial Crime Is Expanding
• -1.3 The Risk Flow From Fraud to Money Laundering

2. How On-Chain Money Laundering Works

• -2.1 The Fundamental Structure of On-Chain Money Laundering
• -2.2 Emerging Laundering Techniques and Red Flags (Privacy Tools, Cross-Chain Swaps, Memecoins, PerpDEX, NFTs)
• -2.3 Case Study: How Individual Signals Connect Into a Single Laundering Pattern
• -2.4 Why On-Chain Data Alone Is Not Sufficient as Evidence

3. How Detection Needs to Evolve

• -3.1 From Address-Based Detection to Flow-Based Detection
• -3.2 Looking at Scenarios, Not Just Individual Events
• -3.3 FRAML: Treating Fraud and AML as a Single Incident
• -3.4 How On-Chain FRAML Analysis Works
• -3.5 CLAIR FRAML: A Real-World Implementation of On-Chain FRAML

4. Conclusion