Most of us have probably secured a great ‘win’ at something in our personal or professional lives only to realize that tomorrow is a new day that will present new challenges. In law enforcement, a classic example of this is the culmination of a great investigation, arresting numerous defendants, and seizing massive amounts of money; but then shortly after, your boss walks in and asks, “What have you done for me today?”

Today’s compliance leaders face the same, if not steeper, challenges. The key to success in the Anti-Money Laundering (AML) compliance space is solving today’s challenges while simultaneously making strategic moves to address tomorrow’s unforeseen AML problems. Data science is a viable solution that arms investigators with the necessary resources to solve tomorrow’s AML complications.

The daily influx of issues flooding compliance leaders’ offices are complex, urgent, and sobering. Risk assessments, de-risking, client exits, transaction monitoring, suspicious activity reporting, model governance, monitoring & testing, personnel, regulatory exams, internal audits, are just a few of the daily tasks on the compliance calendar. In order for compliance leaders to succeed, and to not simply tread water, they must solve problems quickly and strategically plan for improvements three to five years down the road.

A main issue of concern for financial institutions’ (FI) is their transaction monitoring (TM) systems and the required tuning of the systems’ scenarios or rules used as input in the TM model. The U.S. Department of the Treasury, Office of the Comptroller of the Currency’s 2011-12 policy document, titled Supervisory Guidance on Model Risk Management (“OCC 2011-12”) provides guidance on model validation and testing, which includes TM system scenario tuning. The OCC defines a model as “a quantitative method, system, or approach that applies statistical, economic, financial, or mathematical theories, techniques, and assumptions to process input data into quantitative estimates.” A model is comprised of three elements: informational input, a processing function, and a reporting element. From the TM systems’ perspective, the reporting element produces alerts and the informational input contains the rules/scenarios that require constant tuning.

Additionally, OCC 2011-12 cites the importance of on-going monitoring and testing of all models, including the TM system. The OCC stated that “on-going monitoring is essential to evaluate whether changes in products, exposures, activities, clients, or market conditions necessitate adjustment, redevelopment, or replacement of the model and to verify that any extension of the model beyond its original scope is valid.” The OCC and other regulators indicate that the monitoring and testing should be based on a frequency appropriate to the accompanying model risk because data inputs change frequently. This is especially true for TM since the local, regional, national, and international banking landscapes are changing daily.

Rules-based TM systems require constant tuning and maintenance to prevent increased false positive alerts and missed opportunities to identify risk. The work done in the AML space is arguably the most important function within financial institutions. Being profitable in a compliant manner is the goal. Progressive FIs are moving towards adding advanced data science solutions with artificial intelligence (AI) to their compliance tool kits. Proven AI solutions are telling FIs what risk they should have found while providing immediate feedback to the TM system model governance teams to implement rule scenario tuning. The feedback is also assisting AML investigative teams to recognize new and emerging trends and to improve suspicious activity reporting. Solving today’s money laundering problems while fixing future issues is vital to sustaining a viable AML program.

 

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