AML compliance: New technologies to fight back against money laundering networks

It’s estimated that between $800 billion to $2 trillion is laundered annually. Neutralizing money laundering networks is critical to fighting back against drug trafficking, human exploitation, corruption, and more. Traditional Anti-Money Laundering (AML) technologies can’t keep up with the scale and flexibility required to unmask criminal networks. This article will present the benefits of graph analytics to identify the complex schemes and suspicious relationships associated with sophisticated money laundering patterns. You will discover how Linkurious Enterprise helps speed up anti-money laundering investigations and drive down anti-money laundering risks.

Traditional AML and KYC applications use relational databases to store data. In relational databases, data is stored as rows within tables. This approach is well suited for simple analytics such as computing a sum or an average.

Unfortunately, the table-oriented approach makes it hard for AML analysts to understand the relationships within their data due to:

• Low performances: operations relying on identifying how one entity is connected to another become quickly impossible to perform in real-time when the number of entities increases.
• Silos: rigid schemas make it difficult to build a comprehensive view of the relationships across entities and lead to a fractured view of client data.

These limitations have 3 direct implications in terms of AML strategies:

• False positives: relational analytics encourage a focus on simple patterns (how many transactions in the last X days?) that generate a lot of low value alerts.
• False negatives: smart criminals are able to evade simple rules that expose financial institutions to compliance risks.
• Operational costs: simply gathering information scattered across different tools and tabs is time consuming and requires a lot of manual work.

These problems are becoming more and more acute as the level of sophistication of money launderers and the volume of data increases. Graph analytics allows us to tackle these challenges head on. Pioneered by the likes of Google and Facebook, the graph approach consists in thinking about data as a set of nodes and relationships (oftentimes called edges). It’s particularly suited to money laundering investigations since AML data is inherently a graph with nodes, such as clients and bank accounts, and relationships such as transactions (also known as link analysis).

Linkurious Enterprise is an investigation platform that leverages link analysis through graph analytics and graph visualization to detect and investigate criminal networks. It includes an alert system that leverages graph databases such as Neo4j or Cosmos DB to detect complex patterns hidden within networks of billions of nodes and relationships. Linkurious Enterprise also provides an intuitive interface through which analysts can dynamically explore complex networks to analyze links between data and uncover hidden relationships or better understand the context surrounding an entity of interest.

With Linkurious Enterprise, investigation teams can identify suspicious activities that would otherwise go unnoticed and act with confidence enabled by a holistic understanding of the context relevant to each investigation.

A few years ago, 350+ journalists throughout the world used Linkurious Enterprise for the Panama Papers investigation. They were able to uncover evidence of corruption, tax evasion and money laundering in one of the biggest data leaks in history. Today Linkurious Enterprise is used by thousands of analysts throughout the world in organizations such as Zurich Insurance, Dun & Bradstreet or the Indian Tax Office.

Linkurious Enterprise perfectly complements conventional AML technologies to modernize your arsenal and more efficiently fight against money laundering and financial crime. It’s used by compliance and AML teams in financial institutions that are struggling to stop money laundering networks faster and with limited resources. Linkurious Enterprise leverages graph analytics to make it intuitive to detect and investigate complex AML cases.

With Linkurious Enterprise, AML teams can identify new AML schemes and decrease the number of false negatives in their alerts. Its graph analytics capabilities are well suited to identify smurfing behavior or roundtrip flows, identify indirect connections between clients and Politically Exposed Persons (PEPs), detect synthetic identities, perform entity resolution, and track UBOs.

Linkurious Enterprise provides a 360° view of client data and makes the visual exploration of this information more intuitive. AML analysts and investigators can spend less time on gathering information scattered across different tabs and tools and more time focusing on making the right decisions.

Finally, Linkurious Enterprise helps prioritize your existing AML alerts. Within the large amount of alerts generated by traditional AML analytics, there are some low risk and/or low value alerts that may be overlooked. What if within these alerts, that are individually unimportant, there’s an entity (a client, a phone number) that appears multiple times? Linkurious Enterprise can help spot these connections across alerts so that these “connected” alerts are prioritized.

Linkurious Enterprise enables AML teams to detect money laundering networks and makes complex investigations more intuitive. It’s a critical asset for AML teams that want to support their company by lowering compliance risks, optimizing human resources and providing fast and accurate risk assessment to the business.