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Mastercard to buy AI outfit Brighterion

18 July 2017  |  19175 views  |  0 MasterCard web screen shot

Mastercard is looking to boost its fraud detection capabilities through the acquisition of artificial intelligence specialist Brighterion. Financial terms of the deal were not disclosed.

San Francisco-based Brighterion claims to offer the world's deepest and broadest portfolio of AI and machine learning technologies, used in homeland security, AML and cross-channel fraud prevention, data breach detection, marketing, trading and healthcare.

The firm's Smart Agent technology will be added to Mastercard's suite of security products that already use AI, promising greater accuracy in making fraud decisions.

Ajay Bhalla, president, enterprise risk and security, Mastercard, says: "Our unprecedented use of artificial intelligence on our network is already proving successful. With the acquisition of Brighterion, we will further extend our capabilities to support the consumer experience."

Akli Adjaoute, CEO, Brighterion, adds: "We’ve worked with Mastercard over the years to identify patterns and trends to power their most advanced customers’ authorization and decisioning activities. We look forward to building on that foundation and providing an industry-leading, holistic and seamless security experience."

The acquisition is subject to customary closing conditions.

Mastercard's Johan Gerber recently sat down with Finextra to talk about the future of AI for financial services, how it can change the way providers interact with their customers and make decisions, the associated risks and the likely future for prescriptive analytics.

In November the firm outlined how it is using AI as a core component to its network in an effort to provide better risk scoring across all transactions and cut the number of false declines. Its Decision Intelligence tech goes beyond current risk assessment profiling by assessing, scoring and learning from each transaction.

It examines how a specific account is used over time to detect normal and abnormal shopping spending behaviors, using account information like customer value segmentation, risk profiling, location, merchant, device data, time of day, and type of purchase made. The data is fed back to the issuer and used to adjust risk scores for the next transaction.

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