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White Paper

The Impact of Machine Learning on Fraud Detection

The costs associated with payment fraud have been at the forefront of the payments industry for the past few years and unfortunately this is unlikely to change. As criminals find faster ways to commit fraud, it is important for fraud detection tools and technologies to evolve and innovate at a quicker pace.

With all of the information about machine learning, how does an organization cut through the noise and identify the best solution for payment fraud detection?

The Association of Certified Fraud Examiners (ACFE) estimates that the typical organization loses 5% of revenues to fraud in a given year.

Quickly detect fraud and prevent it from impacting you or your customer. ​​​​​​

Continuing Content

Customer Success Story

International Bank Reduces Fraud with Intelligent Machine Learning

According to the 2018 Report to the Nations by the Association of Certified Fraud Examiners, fraud cases cost organizations over $7 billion globally in 2017. It’s no wonder that banks are turning to the latest technology solutions to combat fraud, protect revenue and maintain consumer confidence.

Executive Brief

Why Your Bank Is Better Off With Fewer Fraud Alerts

A four-step systematic approach to reducing alert volume, segmenting alerts, and managing alerts can lower your institution’s overall risk profile. Such an approach has the potential not only to stem losses from fraud, but also to stop fraud at its source.

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