AI that prevents money laundering while ensuring regulatory compliance
Anti-money laundering (AML) compliance can be extremely complex for global organizations; countries have their own regulations and unique sanctions laws. The estimated amount of global money laundering is US$2 trillion a year, according to the UN Office on Drugs and Crime. Financial institutions, acquirers, healthcare payers and more rely on Brighterion AI for unprecedented insights into the constantly evolving tactics used by fraudsters.
Solutions: AML & Compliance
Banks, credit unions, payment companies and lenders face regulatory scrutiny and potentially heavy fines, sometimes measured in the billions of dollars for non-compliance. Banks globally were fined $14.21 billion in 2020. U.S. banks were fined $11.11 billion, the largest of which was $3.90 billion.
As companies broaden channels and increase transaction volumes, transaction monitoring is increasingly difficult. Failure to put a comprehensive compliance program in place also means risking depreciated share value, costly legal battles and reputational damage, as will failure to accurately monitor and report detected activity in a timely manner.
Four critical actions require specific controls for AML and sanctions laws while transacting and operating:
- Onboarding: verify the identity of new customers while ensuring they are not on a relevant sanctions list, and perform a risk assessment
- While transacting: ensure parties in a transaction are not sanctioned, including the senders of funds, and recipients or depositors
- Post-transaction: monitor all transactions for indications of money laundering or terrorist finance activity
- During a customer’s lifecycle: use customer risk rating (CRR) to properly monitor based upon customer’s risk profile with proper adjustments made based upon observed behavior/change
The most commonly used tool, rules-based technology, is inefficient. Criminals often change behaviors, requiring multiple resources to modify rules-based systems and manually dispose of AML alerts with false positive ratios as high as 96%.
Brighterion creates artificial intelligence models that identify behavior indicative of money laundering. Brighterion’s AI models monitor for compliance, using machine learning tools that continuously update to better reflect the evolving nature of techniques used by money launderers.
With unparalleled speed and ability to scale, the model delivers personalized results for each organization’s needs.
For one major financial institution, Brighterion completed the AML compliance model in six weeks. Brighterion AI reduced 50,000 rules from the rules-based system to 12, and developed one-to-one relationships that provided individual account views for each user.
Alerts were reduced from 8,300 per month to 300, significantly reducing the cost of investigating false positives. The company reported increased detection of money laundering schemes and built a case for law enforcement.