Combating fraud and money laundering with graph analytics

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graph analyticsDirty money and money laundering have been around since the existence of currency itself. On a global level, as much as $2 trillion is washed annually, estimates the United Nations. Today’s criminals are sophisticated, using ever-adapting tactics to bypass traditional anti-fraud solutions. Even in cases where enterprises do have enough data to reveal illicit activity, more often than not they are unable to conduct analysis to uncover it.

As the fight against money laundering continues, AML (anti money laundering) compliance has become big business. Global spending in AML alone weighs in at more than $8 trillion, says WealthInsight. This figure will continue to grow, considering how any organization facilitating financial transactions also falls within the scope of AML legislation.

But combating crime is never easy. Especially when organizations face pressing needs for cost reduction and faster time to AML compliance in order to avoid regulatory fees. Legacy monitoring systems have proven burdensome and expensive to tune, validate and maintain. Often involving manual processes, they are generally incapable of analyzing massive volumes of customer, institution and transaction data. Yet it is this type of data analysis that is so critical to AML success.

New ideas have emerged to tackle the AML challenge. These include: semi-supervised learning methods, deep learning based approaches and network/graph based solutions. Such approaches must be able to work in real time and handle large data volumes – especially as new data is generated 24/7. That’s why a holistic data strategy is best for combating financial crime, particularly with machine learning (ML) and AI to help link and analyze data connections.

Graph analytics for AML

Graph analytics has emerged at the forefront as an ideal technology to support AML. Graphs overcome the challenge of uncovering the relationships in massive, complex and interconnect data. The graph model is designed from the ground up to treat relationships as first-class citizens. This provides a structure that natively embraces and maps data relationships, even in high volumes of highly connected data. Conducted over such interconnected data, graph analytics provides maximum insight into data connections and relationships.

For example, “Degree Centrality” provides the number of links going in or out of each entity. This metric gives a count of how many direct connections each entity has to other entities within the network. This is particularly helpful for finding the most connected accounts or entities which are likely acting as a hub, and connecting to a wider network.

Another is “Betweenness,” which gives the number of times an entity falls on the shortest path between other entities. This metric shows which entity acts as a bridge between other entities. Betweenness can be the starting point to detect any money laundering or suspicious activities.

Today’s organizations need real-time graph analytic capabilities that can explore, discover and predict very complex relationships. This represents Real-Time Deep Link Analytics, achieved utilizing three to 10+ hops of traversal across a big graph, along with fast graph traversal speed and data updates.

Let’s take a look at how Real-Time Deep Link Analytics combats financial crime by identifying high-risk transactions. We’ll start with an incoming credit card transaction, and demonstrate how this transaction is related to other entities can be identified:

New Transaction → Credit Card → Cardholder → (other) Credit Cards → (other) Bad Transactions

This query uses four hops to find connections only one card away from the incoming transaction. Today’s fraudsters try to disguise their activity by having circuitous connections between themselves and known bad activity or bad actors. Any individual connecting the path can appear innocent, but if multiple paths from A to B can be found, the likelihood of fraud increases.

Given this, more hops are needed to find connections two or more transactions away. This traversal pattern applies to many other use cases – where you can simply replace the transaction with a web click event, a phone call record or a money transfer. With Real-Time Deep Link Analytics, multiple, hidden connections are uncovered and fraud is minimized.

By linking data together, Real-Time Deep Link Analytics can support rules-based ML methods in real time to automate AML processes and reduce false positives. Using a graph engine to incorporate sophisticated data science techniques such as automated data flow analysis, social network analysis, and ML in their AML process, enterprises can improve money laundering detection rates with better data, faster. They can also move away from cumbersome transactional processes, and towards a more strategic and efficient AML approach.

Example: E-payment company

For one example of graph analytics powering AML, we can look towards the #1 e-payment company in the world. Currently this organization has more than 100 million daily active users, and uses graph analytics to modernize its investigation methods.

Previously, the company’s AML practice was a very manual effort, as investigators were involved with everything from examining data to identifying suspicious money movement behavior. Operating expenses were high and the process was highly error prone.

Implementing a graph analytics platform, the company was able to automate development of intelligent AML queries, using a real-time response feed leveraging ML. Results included a high economic return using a more effective AML process, reducing false positives and translating into higher detection rates.

Example: Credit card company

Similarly, a top five payment provider sought to improve its AML capabilities. Key pain points include high cost and inability to comply with federal AML regulations – resulting in penalties. The organization relied on a manual investigative process performed by a ML team comprised of hundreds of investigators, resulting in a slow, costly and inefficient process with more than 90 percent false positives.

The company currently is leveraging a graph engine to modernize its investigative process. It has moved from having its ML team cobble processes together towards combining the power of graph analytics with ML to provide insight into connections between individuals, accounts, companies and locations.

By uniting more dimensions of its data, and integrating additional points – such as external information about customers – it is able to automatically monitor for potential money laundering in real time, freeing up investigators to make more strategic use of their now-richer data. The result is a holistic and insightful look at its colossal amounts of data, producing fewer false positive alerts.

As we continue into an era of data explosion, it is more and more important for organizations to make the most in analyzing their colossal amounts of data in real time for AML. Graph analytics offers overwhelming potential for organizations in terms of cost reduction, in faster time to AML compliance and most importantly, in their ability to stop money laundering fraudsters in their tracks.