Enhanced data science and AI solutions are available today for FIs to leverage for anti-money laundering (AML), counter-terrorist financing (CTF) and the identification of other financial crime risks. These forward-thinking solutions provide end-to-end program coverage to identify pseudo-client, intermediary, and internal FI risks, while simultaneously identifying false negatives and false positives with a feedback mechanism to improve the existing models and rules. Additionally, AI is assisting FIs to automate significant portions of the investigative process, allowing AML investigators to focus their attention on otherwise unintentionally overlooked red flags or suspicious account activity. The end result is a finely tuned enterprise compliance program that operates more cost efficiently, and more importantly, functions more effectively than traditional systems.
It’s well known that financial institutions operate under strict and high-pressure state and federal regulatory controls, fueled by constant examination and scrutiny. To put the situation into perspective, the Boston Consulting Group (BCG) reported in its report, 2017 Staying the Course in Banking, that the number of individual regulatory changes FIs must monitor globally has more than tripled since 2011. In 2015, the BCG estimated that there were more than 51,600 global compliance regulations. This is a staggering statistic for global FIs.
According to the Federal Financial Institutions Examination Council (FFIEC), “FinCEN and the federal banking agencies recognize that, as a practical matter, it is not possible for a bank to detect and report all potentially illicit transactions that flow through the bank. Examiners should focus on evaluating a bank’s policies, procedures, and processes to identify, evaluate, and report suspicious activity.” Committed to innovation and strategic risk mitigation efforts, today’s FIs are working feverishly to proactively identify, evaluate, and report suspicious activity to regulators.
FIs are beginning to leverage data science, AI and machine learning to give their compliance programs a tune-up to identify TMS scenarios that are missing high-risk transactions. FIs are then using the AI data to procure valuable input for rule scenario/model tuning. Model governance, rule/scenario tuning, enterprise risk assessments, product risk assessments and ad-hoc regulatory mandates challenge FIs daily to comply for holistic risk management. AI is capable of providing FIs with an affordable, focused, and surgical risk examination to find the risk they are not catching today in static TMS scenarios.
Packaged financial crime solutions are built from the ground up by proven AI experts working alongside experienced AML professionals. Unlike other AI for AML approaches, packaged AI solutions are quick to implement and are continuously updated by the solution provider based on input from multiple customers and regulators.read more
Serving almost 20 million customers, the bank was concerned about the risks associated with false negatives that its current AML compliance technology was missing. Intent on driving financial crime out of its operation, the bank began searching for a solution that could enhance its existing rules-based transaction monitoring system (TMS) and minimize the risk related to undiscovered financial crime.read more
While all agree the promise of AI for AML is great, taking the steps to select and implement AI within an institution is relatively new. So, what is the best approach for implementing AI and machine learning into your efforts to combat financial crime?read more