Overcoming AML Compliance Disruptions in FIs

There is no question that financial service providers frequently struggle to find the time and resources to recruit and train staff, manage volume surges and comply with emergent regulatory demands.

These anti-money laundering (AML) compliance disruptions require that teams be resilient and adaptable. How do teams smoothly navigate these disturbances and overcome them?

The answer is to adopt a technology-enabled risk-based approach that ensures organizations are using resources efficiently. According to the Financial Action Task Force (FATF), “a risk-based approach should be the cornerstone of an effective AML/CFT [combating the financing of terrorism] program and is essential to properly managing risks.”¹ One of the most effective ways to quickly enable a risk-based approach is to augment existing technologies with advanced automation.

Leveraging Advanced Automation

Organizations are adopting solutions designed to automate a variety of AML compliance operations using artificial intelligence (AI). The key objective is to lower risk while making the work of compliance teams more efficient, so they spend less time performing manual tasks and can be more agile when program disturbances occur.

Automated data gathering and risk analysis speed up what is typically a time-consuming process by analyzing the correct data and presenting that analysis in a way that empowers investigators to make quicker decisions. Leveraging AI-enabled solutions speeds up processes, such as alert reviews, case investigations, excluding false positives, resolving backlogs, tackling remediation and keeping up with high-risk customer reviews.

FIs should first understand where the biggest opportunities for efficiency are in their AML programs

Automation accomplishes these tasks efficiently by reducing a team’s research workload and allowing investigators to spend time with the cases that most demand their attention. For example, a solution that scores risk so that less experienced analysts can manage basic cases and more seasoned investigators can tackle
complex alerts.

How to Select a Technology Solution

Financial institutions (FIs) should first understand where the biggest opportunities for efficiency are in their AML programs. Once they assess what steps in procedures take the most time, they can begin to search for technology solutions that can automate them. Additional points to consider include:

Ensure the technology provider understands both financial crime and technology. Working with a technology partner who does not understand how to use a tool against financial crime will require FIs to teach the partner the domain and help them figure out how to best apply said tool. A financial crime-focused technology provider will offer purpose-built solutions and know how to get them configured to match program specifications and quickly operational.
Look for solutions that can complement existing core systems and processes, at least initially. Doing a wholesale replacement of a transaction monitoring system (TMS), case management system, know your customer system, or other core technology platform is expensive and time-consuming. To begin, select systems that can integrate with your core platforms and automate your current processes without replacing them. After developing a mature understanding of the capabilities and benefits of the new technology solution, an FI can consider replacing legacy tech, such as replacing rule-based TMS with a risk detection engine that generates fewer false positives while evaluating a broader set of risk-bearing characteristics and behaviors.

With respect to advanced AML technology solutions, FIs should look for the following capabilities:

AI and machine learning: These technologies automate many of the steps an investigator conducts such as entity resolution and profiling, entity reputational analysis, transaction reviews and economic purpose derivation.
Data clustering: This allows teams to efficiently divide data into discrete clusters. Cluster definitions can be predetermined by the user, or solutions can form clusters based on what the data says. For example, the technology can identify when a customer should shift from a low-risk cluster to a high-risk cluster. It can also call out when risk segmentation has shifted and automatically trigger high-risk customer reviews as part of enhanced due diligence efforts.
Automating rules: While a TMS rule created by AML compliance team members or suggested by the industry can be the right one for the job, advanced technology can confirm or recommend rulemaking. Advanced systems complement TMS rulemaking by determining when a rule is ineffective or indicating when an algorithmic approach or a neural network should supplement rule-based TMS.
Automated reporting: As it has been broadly demonstrated of late by ChatGPT and Google Bard, AI technologies can devise clear narratives based on the data examined. By auto-compiling summary descriptions, AI-enabled AML solutions can speed reporting when explaining why an alert was cleared. This capability also increases the consistency and accuracy of reporting across AML compliance teams.
Achieving resilience and agility: Adopting advanced automation allows AML compliance teams to save time and gain powerful insights to reduce inefficient, manual tasks and make better operational decisions.

The primary goal for financial crime prevention professionals is to select one or more solutions that address the biggest opportunities for efficiency and effectiveness gains in current processes and systems. Each solution added to the mix comes with its own change management, training, vendor management and other efforts necessary for a successful implementation, so select as few as necessary to meet goals. Further, avoid solutions that overlap in their functionality and ensure the workflow you are creating for AML investigators is well understood. The fewer solutions needed to master and access daily, the more efficient they will be in their work.

Concerning training FIs (including smaller FIs with limited budgets) on new technology, the solution that is ultimately selected should not require much education; it should be intuitive and fit into existing processes and risk priorities. For example, the AML RightSource Financial Crime Investigation Report (FCIR) can be read by investigators of almost any level of experience and immediately make them more efficient by presenting supporting documentation needed to analyze cases efficiently and to create suspicious activity reports when indicated.

Conclusion

Increasing risks and regulatory expectations and a tight labor market are challenging financial crime compliance leaders to improve the productivity of their teams. With AI-enabled AML technology, FIs can automate compliance operations, helping teams become more efficient and channel investigators’ talents to the most complex alerts.

This combination of advanced technology and an efficient compliance team provides FIs with the awareness and the tools to continually combat risk more effectively and overcome AML compliance disruptions.

Phil McLaughlin, chief technology officer, AML RightSource