Legacy, rules-based TMS engines won’t trigger alerts if normal business activities are not present because, simply, they can’t identify what they can’t see. On the other hand, advanced data science techniques, including artificial intelligence (AI) and machine learning are new technologies at the forefront of information gap analysis which aims to identify why data and information are missing. (see examples below).
While financial crime red flags are familiar to compliance professionals, we often forget that a lack of business activity can sometimes hold untold secrets about what is actually occurring. The following are hypothetical scenarios whereby AI could identify possible financial crimes as stemming from of a lack of business activity:
- A bank’s correspondent customer reports itself to be a small Asian fishing company with two medium-sized vessels. The customer’s account includes payments to vendors, owners, and regulatory agencies. The customer’s account does not include any noticeable salary payments for the past 36 months, while other similar-sized fishing companies’ accounts reflect salary payments to international employees. The lack of payments to employees could be indicative of slavery or forced labor and should be investigated further by the bank and, ultimately, law enforcement.
- Criminals are increasingly taking advantage of global trade to move money around the globe by utilizing complex and sometimes falsified documents that are typically associated with legitimate trade. In this scenario, vendors and shipping companies create false and inflated invoices and bogus bills of lading to conceal illicit funds through a trade-based money laundering scheme. But in the spirit of “it’s a tangled web we weave, when we practice to deceive,” many of these schemes fail to show the required supporting transactions including forwarding bills, warehouse fees, customs clearing taxes, or other government fees. Through cognitive-based AI solutions, technology can understand that the lack of these transactions may indicate significant risks.
Unlike traditional rules-based TMS, today’s powerful AI and machine learning solutions can easily and quickly identify the lack of expected business activity. Predictive analytics, coupled with supervised and non-supervised learning, give compliance teams the ability to detect the absence of normal business activities which could then inform compliance departments of possible financial crime activities taking place.
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.
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.
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?