Adaptive analytics technologies improve sensitivity to shifting fraud patterns by automatically adapting to recent confirmed case disposition.

You can improve predictive accuracy in fraud detection by expanding the dataset used to craft the predictive characteristics used in a machine learning model.

In order to maintain a positive consumer experience, specialized fraud analytics must be used to assess the “tough” questions.

A robust enterprise fraud solution combines a range of behavioral analytics models and profiles, which can understand evolving patterns in real time.

In the age of data breaches, self-broadcasting of personal information, and ever more-sophisticated fraudsters, here are four steps you can take to protect yourself.

As FICO reported today, there was a 10% increase in the number of payment cards compromised at U.S. ATMs and merchants in 2017.

I'm forecasting that in 2018, my wallet will become slimmer as physical (plastic) credit cards are replaced with payment apps on my smart phone.

In terms of detecting fraudulent card transactions, there’s only one data source that offers the most predictive power, hands down: other card transactions, and lots of them.

Will banks bolster their defensive line with additional mobile payments fraud protections, driving further adoption?

Response rates have dramatically improved from hours or even days to 11 minutes on valid transactions and 31 minutes when confirming card fraud.