This holiday season should be a joyous one — for criminals. Holiday spending always makes it easier for fraudsters to steal your money, for two reasons.

Detective Jesse Gossman provides insights on how to better fight synthetic identity fraud and fake Social Security numbers through multiple layers.

In this video, fraud investigator Jesse Gossman discusses how synthetic identities are created, and why they look so real.

In this video, detective Jesse Gossman shares how synthetic identities are used, and the risks associated with Credit Profile Numbers (CPNs).

It’s a harsh truth that while data and technology in the fraud business abound, true domain expertise is a scarce resource. We speak to Andy Procter, FICO’s own fraud specialist about how to fight identity fraud,

Where in your fraud controls should you be considering post-book controls? And where can you work better with your partners in risk to mitigate losses?

Machine learning analytics must be a critical part of a financial institution’s application fraud control strategy.

The Falcon Intelligence Network is designed to drive fraud-focused AI innovation while providing a global perspective of fraud detection performance.

Want to learn why application fraud is on the rise? In this clip, I share insights into the three major drivers beyond the tremendous YoY growth rates.

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

I spoke with one of FICO’s principal scientists, Derek Dempsey, who shared how AI and machine learning are solving problems in application fraud.

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.

Machine learning in fraud detection performs better when we know the strengths and limitations of different analytic toolkits, and understand the factors that determine accuracy.