How to assess the effectiveness of the changes introduced to anti-money laundering programs

FIU analytics

The work-from-home model detracts some of the governance elements that were previously present in the at-office work model. Financial intelligence unit (FIU) managers are no longer able to hop between desks and conduct impromptu catchups with their analysts. In the new world, how can financial organizations monitor their FIU for effectiveness?

Enhanced FIU monitoring can be achieved by establishing a regular cadence with the FIU, such as a daily standup call, which can serve to monitor the analyst team for productivity, identify roadblocks and workflow inefficiencies, as well as assess the analysts’ mental wellbeing and job satisfaction.

While such cadences are important, they lack the ability to determine FIU effectiveness from a data-driven standpoint. To complement direct monitoring and governance activities, an organization may wish to apply augmented FIU analytics to determine the effectiveness of its FIU. This may be achieved in several ways:

Case management dashboard

Create a case management dashboard, or augment an existing one, using information from the case management system’s data repository or by other means.

Such a dashboard may display information relating to predetermined metrics to assess the organization’s case management capabilities, which may include:

• Aging work items by category, business unit and individual user
• Number of work items actioned by user and over time
• Number of suspicious activity reports (SARs) submitted as a percentage of overall work item activity, number of rejected SARs and other SAR-related metrics

Such metrics may help an FIU manager determine trends in alert and case processing, as well as pain points and challenges in the investigation and escalation process.

Case management reports

Case management reports may be sent to an FIU manager’s mailbox by an automated solution. For additional governance and auditing capabilities, such reports may be automatically created in a case management system on a periodic basis (e.g., weekly), reviewed by a designated person or team in the FIU, and signed off to facilitate tracking and accountability.

Model analytics and calibration

Enhanced model analytics may be established to determine ongoing model effectiveness considering changes to consumer spending and money laundering activity. Such measures may include:

Enhanced model monitoring

A model monitoring dashboard may be created leveraging data from the AML solution’s data repository, and can display metrics such as:

• True positive and false positive rates per model over time
• Rates of consolidation of multiple monitoring rules and types of activity per work item
• Number of events and alerts per model over time

Similar to FIU analytics, such metrics may also be sent to a designated mailbox or created as a work item in the case management tool for enhanced monitoring and governance purposes.

Enhanced model calibration

Considering economic uncertainty and rapidly changing activity patterns, a financial organization may wish to enable its AML modeling team to adjust and validate AML models with greater velocity, transparency and security. Such capabilities may be enabled by advanced model management and integration tools, such as EY’s Case Accelerator.

Collaboration and workflow

A financial organization may wish to assess the effectiveness of its current operating model as well as the currently established workflow for alerts and cases across different surveillance types, sources and business units. Such an exercise may be achieved by using an automated process discovery solution, such as EY’s Process Mining solution, and by conducting workshops with FIU managers to identify bottlenecks in the investigation and escalation process.

Further improvement may be achieved by automating certain case management activity such as escalation, information gathering, case creation and routing, and more.

Enhanced quality control

Work item quality control can be enhanced by introducing an automated process to escalate a predetermined percentage of randomly selected work items to a quality control queue. The quality control queue is monitored by a designated quality control team, who inspect the work items’ details as well as notes and actions taken by the analyst and determine whether they were escalated appropriately.

Conclusion

As organizations shift to a work-from-home model and adjust their AML programs to reflect changes in the AML regulation landscape and money laundering behaviour changes, they must maintain a high level of efficiency in the FIU to remain compliant and keep their AML function efficient. By applying the techniques described in this article, an organization may facilitate governance and transparency in the FIU, enable the AML modeling team to continuously improve and fine-tune the organization’s AML monitoring capabilities, and navigate the ever-changing AML landscape with greater control and sophistication.