Today, criminals are using extremely sophisticated, ever-adapting tactics to bypass traditional anti-fraud solutions for money laundering. Any organization facilitating financial transactions – including non-bank money service businesses such as digital/mobile payment services, life insurers and retailers, to name a few – falls within the scope of anti-money laundering (AML) legislation. The challenge continues to grow, presenting a need for cost reduction and faster time to AML compliance to avoid regulatory fees.
Many enterprises do have access to the data that could reveal the illicit activity, but they are unable to link the data and the relationships together. Legacy monitoring systems are burdensome and expensive to tune, validate and maintain. Such solutions involve manual processes and are generally incapable of analyzing the massive volume of customer, institution, and transaction data stored across various locations, formats, and protocols. A number of new ideas are emerging to tackle this problem, including semi-supervised learning methods, deep learning based approaches and network/graph based solutions. These approaches must be able to work in real-time and handle large volumes of data – not only existing data, but new data being generated every single hour of every day. A holistic data strategy therefore is best for combating financial crime, particularly with Machine Learning (ML) and AI to help link and analyze data connections.
TigerGraph powers AML by linking data together, incorporating rules-based ML methods to automate the process and reduce false positives – all in real-time. 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. Organizations can move away from cumbersome transactional processes, towards a more strategic and efficient approach.
TigerGraph is used by the #1 e-payment company in the world, with more than 100 million daily active users, to modernize how it conducts investigations. Previously, the 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 TigerGraph, 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.
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.
Using TigerGraph, the company 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 are in 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. TigerGraph’s Deep Analytics graph platform can help.