Kount is the pioneer in using machine learning in transactional fraud prevention, with supervised and unsupervised solutions dating back to the company’s inception over a decade ago. Kount’s latest advancement creates the closest simulation of the decision process of an experienced fraud analyst, yet in a faster, more accurate, and more scalable manner. Kount’s AI uses both supervised and unsupervised machine learning along with additional calculations to deliver a near-human decision, allowing companies to control business-driven outcomes such as higher revenue, reduced fraud losses, and lower operational costs.

Kount’s AI emulates an experienced fraud analyst by taking into account both historical fraud patterns as well as anomalies. When fraud analysts consider historic data for known fraud patterns, they look at the company’s data and their own experience to identify whether or not the person or transaction can be trusted. When Kount’s AI considers historic data, it turns to supervised machine learning, which is trained on Kount’s universal data network that includes billions of transactions over 12 years, 6,500 customers, 180+ countries and territories, and multiple payment networks.

Then, fraud analysts look for anomalies—something in a transaction that doesn’t look right. This is where emerging fraud trends are detected. Kount’s AI uses unsupervised machine learning that employs advanced algorithms and models to detect anomalies much faster, more accurately, and on a more scalable basis than human judgment alone.

An experienced fraud analyst weighs the risk and safety of the transaction to make a decision based on the business’ risk tolerance, whether that be controlling chargeback rates, accept or declines rates, or manual reviews. Kount’s solution allows the analyst to set policies for these thresholds based on a new score: Omniscore. The new enhancement is twice as effective as existing models at detecting payments fraud, while maintaining Kount’s 250 millisecond response rate.

“The supervised machine learning aspect of Omniscore reflects the historical experience that seasoned fraud analysts possess, while the unsupervised features simulate the instinct or ‘spidey sense’ of the very best analysts to detect that a new type of attack is underway,” says Tricia Phillips, Kount’s SVP of Product and Strategy. “More than any other model we’ve seen, Omniscore truly behaves as a human would in the risk assessment of a payment transaction, which is the very definition of artificial intelligence.”

“The next generation of AI in fraud prevention is much more than machine learning - supervised or unsupervised. It is the ability to simulate, augment, and scale the decision process of an experienced fraud analyst to greatly increase the accuracy and effectiveness of fraud prevention and to deliver desired business outcomes,” stated Steven D’Alfonso, Research Director with IDC Financial Insights. “The ability to quickly identify complex and emerging fraud patterns by Kount’s new AI solution, along with customizable controls for the business, will play an important role in allowing businesses to achieve their financial goals without sacrificing customer experience.”

The next frontier in AI-driven fraud prevention, Kount’s AI quickly and accurately detects existing or emerging, automated, and complex fraud. Kount provides the customizable control companies need to protect against fraud and confidently achieve specific business objectives, such as balancing chargeback rates, decline rates, and operational costs.