Reduce Fraud and False Positives with Machine Learning

Machine Learning
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Balancing user experience with strong transaction and login protection is a constant battle for financial institutions – one that can have tangible consequences for both bank and customer.

Take, for example, User X. It’s Cyber Monday, and User X wants to buy the latest smartphone on his favorite discount online store. The site has limited stock, so he logs in at 4am to ensure he’ll be able to get a phone. He excitedly places the item in his cart, goes to check out, and clicks pay – only to receive a message that the purchase was flagged as suspicious and blocked.

After time-consuming calls with his bank to sort out the problem, User X navigates back to the store to find that they have completely sold out of his dream phone. Furious that a false positive ruined his Cyber Monday plans, the user begins to consider changing banks to one that better adapts to his digital lifestyle.

Intelligent Transaction Monitoring

The consequences to banks for using antiquated fraud protection solutions are concrete: either fail to keep up with the needs of modern users or fall victim to fraud – and in both cases, risk losing customers and business. In a digital world, users expect to be able to complete almost all of their transactions quickly, easily, and without a lot of friction. The solution? Artificial intelligence (AI)-based transaction monitoring.

AI and machine learning (ML) have long been buzzwords in the anti-fraud world – and for a good reason. Cyxtera researchers have shown that intelligent fraud detection systems are able to detect criminal behavior with incredible efficiency, achieving rates of over 98% accuracy. Here at Cyxtera, our research team is constantly investigating how this technology can be used to improve our products and better protect our customers.

DetectTA, Cyxtera’s transaction and login monitoring solution, combines strong rules with dynamic ML algorithms to provide secure coverage of all digital transactions. DetectTA’s advanced analysis engine uses contextual information, such as time and location, and compares it to an individual user’s typical behavior as well as the general behavioral trends of an organization’s user population. The result? New users are always protected right from their first transaction, and existing customers enjoy fewer false positives and increased protection. Financial institutions have the benefit of increased security and lower operational costs while ensuring that they remain relevant in an ever-changing digital banking environment.

To learn more about how machine learning improves user experience while reducing false positives, click here.

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