by Quantexa and Moody’s
Unsurprisingly, a nagging challenge for most firms, and especially FIs, is detecting and assessing their money laundering and terrorist financing risks by designing proper controls. Multiple truncated representations of the same client, lack a holistic view – making lines of business connections difficult to see and manage. There are many different hierarchical views of clients managed by different departments and sourced in different parts of the organization. And product data is often managed by separate businesses with different data formats and taxonomies. All these factors show a clear indication of incorporating AI and machine learning power into existing compliance solutions with world-class data.
According to the results of Japan’s Proof of Concept on Machine Learning and Artificial Intelligence ‘’the Shared Transaction Monitoring and Screening System with AI has enough potential to reduce workload, including the triage process of the detections and dealing with false positives.’’ FATF (2021)
The recent FATF report clearly shows the importance of Transaction Monitoring for FIs.
Transaction monitoring enables FIs to detect and mitigate the risk related to customer behaviour and the customer’s counterparty. It enables institutions to detect unusual transactions and act on it by conducting further investigation or filing suspicious activity report with the regulator.
Transaction monitoring definition: to detect layering by examining the transaction pattern of a customer to assess the financial profile; to follow-up on any abnormal activity detected across institutions; to better identify suspicious activity; to apply transaction thresholds.
There is no one-size-fits-all approach for Anti-money-laundering (AML) controls due to the complexity of different transactional behaviours and everchanging challenges related to data quality and evolving money laundering techniques. An FI provider must consider its risks holistically across all its different business units. Then, it must design controls for specific business profiles, customer bases, geographic footprints, and overall inherent risks.
The burden of innovation belongs to the FIs and they should seek providers to enable bringing together the right technology and innovation to support the FI in demonstrating forward thinking. The UK’s Financial Conduct Authority and U.S. regulators are encouraging technology innovation in AML and financial crime. Entity resolution and network analytics will prove critical to a successful risk-based, group–level approach.
By leveraging entity resolution and network generation technology, FI providers will be able to detect their risks, create context around suspicious activity to drive better SARs, and more effectively mitigate risks more precisely. These technologies also provide investigators quicker access to the intelligence necessary to conduct their investigations, resulting in significant cost savings because of decreases in time spent on each investigation and investigator headcount.
Many FIs are still solely relying on traditional rule-based transaction monitoring systems which are generating high alert volumes and false-positive rates – often as high as 99 per cent – and keeping costs high. These systems are not rooting out critical areas of risk and specific typologies buried within complex customer networks.
As a result of this reliance on traditional monitoring systems, most institutions have taken a pre-determined checklist approach to investigations. This process flies in the face of the complex schemes that criminals use to launder money.
Data quality also presents a key challenge. Often data is spread across a firm, specific fields can be uneven, and systems do not talk to each other. Even when data is available, it often requires a lot of organizing and normalizing to be useful for monitoring or investigations.
‘’A responsible use of new technologies, including digital identity and cutting-edge transaction monitoring and analysis solutions (including collaborative analytics) can assist effective, risk-based implementation of the FATF Standards by the public and private sectors, as well as promote financial inclusion.’’ FATF (2021)
To be able to see the bigger transaction monitoring picture and fully optimize all the data, however, investigations teams need to shift from a reactive detect-and-investigate process to a proactive approach of finding and disrupting financial crime. To do so, more relevant data or context is needed. As context becomes core to effective detection and investigation, a contextual monitoring solution, as offered by Quantexa, becomes the answer. Unlike traditional monitoring systems, a contextual monitoring platform uses entity resolution to resolve counterparties and better understand the relationships between them by using network analytics or graph theory. This platform continually asks the most pertinent questions:
By applying network analytics, investigators can better determine whether the entity has a direct or indirect connection to criminal activity. Contextual monitoring essentially takes all the data collated manually post-alert generation by investigators on a subset of customers and applies this at-scale to all customers and parties for detection. This advanced investigate-to-detect process leverages shared data and intelligence from law enforcement and police agencies and signals a new way of tackling financial crime and fraud.
Transaction Monitoring itself is essentially just math. It’s applying conditional logic to the payments and flagging those payments that meet the criteria for human review. Moody’s helps give that human something to actually work with by providing the information that allows them to see if something bad/criminal is actually happening or if it’s something legitimate but just unusual.
Together, the Moody’s and Quantexa approach get individuals to think beyond the silo of just Transaction Monitoring as a standalone process and have them think about it more holistically from rule creation to resolution of cases/investigations so that it doesn’t cause the sorts of backlogs it currently does for customers.
Now, for the first time, multiple world-class external data sets could be integrated into the TM process. These data sets empower the investigator to work better and faster by reducing manual effort, streamlining internal processes, and increasing effectiveness.
The external data sets include:
Moody’s datasets power Quantexa’s Dynamic Entity Resolution, delivering the Single Customer View necessary to understand complexities of the customers and counterparties across different business lines. A single customer view enables Institutions to target the risk and understand the context of the transactional activity they are monitoring.
Together Quantexa & Moody's provide a 360-degree view of an organization's client and third-party business relationships to enable a full spectrum of analytics and insights, helping investigators and leaders to identify opportunities and better manage the risks of doing business in today’s complex and constantly evolving world.
Benefits:
As systems are re-tooled, investigators need to up skill to transition from following pre-determined investigative steps, to following all the intelligence available to them. In this highly complex area, investigators must firmly understand the provider’s products and associated risk factors to effectively use the technology and make sound decisions.
Together, we help Financial Institution’s bridge what we call the data decision gap with world-class data, trusted models, best-in-class intelligence software, and proven analytics solutions. We help customers with a new solution approach in Master Data Management, Credit Risk, KYC, AML/CTF and Financial Crime, Compliance, and Fraud.
Please get in touch for more information—we would love to hear from you.