Conquering Crypto Crime

Introduction

The current cryptocurrency anti-money laundering landscape is dominated by Blockchain Analytics Tools (BATs) like Chainalysis, Scorechain, or Elliptic. Although their capabilities are undeniably impressive, they are not sufficient for comprehensive money laundering detection.

This paper proposes the combination of BATs with Anti-Money Laundering (AML) transaction monitoring solutions. We anticipate that this approach will lead to more effective AML compliance measures in the crypto space.

Blockchain technology and cryptocurrency have gained considerable attention in recent years. However, pseudonymity – and in some cases anonymity – features make these technologies susceptible to misuse for money laundering activities.

Constraints of Current Methods

For instance, if a BAT identifies a blockchain address connected to a ransomware attack, it absorbs and stores the associated risk information with the address. Still, despite their effectiveness, the current methods and tools have certain limitations:

1. End-to-End Fiat & Crypto Monitoring: BATs often lack the ability to track the exchange of fiat money into cryptocurrency and vice versa – a critical aspect of money laundering schemes.

2. Pattern Analysis: BATs usually cannot identify suspicious transactional or behavioral patterns beyond simplistic rule-based scenarios, which is a key regulatory requirement and a necessity for identifying money laundering.

3. Indirect Risk Scoring: BATs often assign indirect risk scores, as only a limited number of blockchain addresses are directly connected to identified crimes.

Apart from low detection rates (and thus an increased risk of being misused for money laundering), these limitations can also lead to technical shortcomings in complying with applicable money laundering laws and regulations.

Complementing Blockchain Analytics with Real-Time Transaction Monitoring

The identified constraints underscore the necessity to complement BATs with AML transaction monitoring solutions already applied to fiat transactions. These solutions are designed to process fiat transactions and analyze them based on complex scenario models. Together, a combination of BAT and crypto-ready AML transaction monitoring solutions can overcome the shortcomings.

Combining Fiat and Crypto Transactions

For effective risk management, monitoring systems should link fiat and crypto transactions to display overall customer behavior, e.g., based on a customer or account ID. Particularly at crypto exchanges, which have data available about both types of transactions, this can uncover relevant insights.

Analysis of Complex Transaction Patterns

To achieve effective and efficient money laundering detection, it is imperative to recognize known (yet unidentified) intricate money laundering behavioral patterns. More complex rule sets, or even Artificial Intelligence (AI)-based models, allow operators to uncover complex suspicious behaviors such as money muling, account passthrough, and other common money laundering techniques.

Inclusion of Customer Risk Data

Modern AML transaction monitoring solutions provide for a combined view. This allows operators to utilize all available customer data for optimal risk detection. The connection between customer and transaction data is critical in cryptocurrency, due to the pseudonymity of blockchain transactions.

The Way Forward

We recommend integrating BATs with modern financial crime technology systems. We advocate for the harmonized and comprehensive use of BATs along with modern traditional fiat money monitoring tools for efficient and effective anti-money laundering compliance.

Integrated User Interfaces

Integrating user interfaces fills the need for a unified platform where operators can seamlessly access and present relevant information for analyzing and verifying transactions.

AI Models for Detection and Result Prioritization

AI models can increase efficiency and accuracy by detecting anomalous behavior and prioritizing alerts based on comparative risk analysis. Notably, these AI-generated decisions should include explanations in human-understandable language.

Case Studies

Deloitte Case Study: From Independent Review to Integration Support

Deloitte supports clients in an independent review of their transaction monitoring landscape. This includes assessing coverage of relevant risks and typologies.

Hawk Case Study: Integrated Crypto-Fiat Monitoring

Hawk has developed a crypto monitoring solution that integrates its own AML Transaction Monitoring technology with the capabilities of BATs. Hawk can identify patterns across all sources, thereby recognizing complex money laundering schemes.

Combining Blockchain Analytics and Customer Risk Information

Hawk has developed a standard integration with BAT providers, incorporating BAT risk scores into their rules and models for additional money laundering risk insights. This adds a contextual layer to alerts, aiding operators in decision-making.

Putting AI on Top to Enter a New Era of Crypto Transaction Monitoring

Hawk’s AI models can integrate crypto signals into detection and false positive reduction models, harnessing the combination of available transactional and customer data.

Summary

In summary, leveraging both BATs and traditional AML transaction monitoring solutions allows for effective analysis of complex transaction patterns and improved detection rates. We advocate for a comprehensive use of BATs with traditional fiat money monitoring detection tools to achieve effective crypto-related AML compliance.