AI that detects fraud, drives AML and monitors for compliance

As a leader in the payments industry, Brighterion AI is used by acquirers, payment service providers (PSP) and other processors. Connected with cardholders, merchants or terminals, Brighterion’s self-learning capabilities create personalized, adaptable profiles that automatically update in real time, continuously refining with each transaction. One-to-one analysis provides unprecedented behavioral insights, from the spending behavior of customers to the constantly evolving tactics used by fraudsters. Backed by Mastercard, Brighterion AI is stable, proven and experienced.


  • Increased efficiency: Augments employees’ efforts and efficiency via automation
  • Immediate ROI: Reduces back office costs and increases ROI
  • Gained autonomy: Customers dynamically incorporate changes and test each change
  • Know what’s happening in the moment: Real-time insights provide unprecedented, omnichannel visibility into the behavior of each entity
  • Never run out of space: Infinite scalability, more than 2x the rate of our nearest competitor (Aite Group analysts)
  • Be ready in 6–8 weeks with AI Express
  • Peace of mind: Backed by Mastercard experience and infrastructure

Success story
Worldpay’s customer journey

Ian Belsham, Global Head of Transaction Monitoring for the world’s largest acquirer, knew that Worldpay’s next fraud and AML system had to be artificial intelligence led. What he didn’t expect was all the advantages and cost savings of self-learning, interactive AI. Listen to Ian Belsham explain:

  • What’s needed to build an accurate system
  • The advantages of AI over rules-based monitoring platforms
  • The depth of knowledge and insight available
  • The easily recognizable return on investment

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Use case: AML & 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. Payment companies face regulatory scrutiny and potentially heavy fines, sometimes measured in the billions of dollars for non-compliance. As companies broaden channels and increase transaction volumes, transaction monitoring is becoming 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
  • During transactions: ensure parties in a transaction are not sanctioned, including the merchant, the consumer and possibly the recipient bank (cross-border payments) or card issuer
  • 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%.

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Brighterion creates artificial intelligence models that identify behavior indicative of money laundering. Brighterion AI models compliance rules and uses supervised and unsupervised learning to keep the model flexible, providing adaptive learning and continuous updates to better reflect the evolving nature of techniques used by money launderers.


For one major company, Brighterion completed the AML compliance model in 6 weeks. Brighterion 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 payments company reported increased detection of money laundering schemes and built a case for law enforcement.







Use case: omnichannel fraud


With multiple touchpoints with each customer, there’s a strong risk for fraud. Customers use many siloed channels to a payment card by using different apps and devices. These may include in-person purchases with payment cards and smart device apps, online purchases, and other forms of remote payment. With information in different systems, payment companies can’t always link the payment source.

There is also the risk of intervention by a third party who subsequently acquires credit fraudulently in a customer’s name.


Brighterion AI provides robust virtual profiles from all data sources and multiple channels regardless of type, complexity and volume. Seamlessly accepting data from myriad sources, Brighterion enriches the data (regardless of format or source), saving fraud investigators hours of tedious manual labor to parse data and investigate false positives.

This one-to-one behavioral profiling provides unprecedented, omnichannel visibility to identify fraud and security breaches in real time.


Brighterion AI breaks down channels into one wholistic view, allowing cross-channel anomaly detection and prediction ability. This will dramatically decrease false positives while increasing detection rates. Brighterion improves overall customer experience and reduces friction.

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