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

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.

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

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.

As a result of the rise in application fraud due to EMV adoption in the US, Citi is exploring new authentication protocols and new analytics.

FICO conducted a research project to see just how much lift unconstrained, state-of-the-art ML techniques might offer over the FICO Score.

When it comes to application fraud, machine learning analytics must be a critical part of a financial institution’s control strategy.

How is application fraud evolving? And what should fraud leaders be doing to manage fraud risk in a digitally connected world?

It’s raining data left and right, and you need to start by understanding what data you already have to fight application fraud and where there are gaps.

When we talk about brute force application fraud and bot-net attacks, what are we really talking about?

Fraud and security are a top priority for financial institutions — but just stopping fraud isn’t enough. Managing fraud is a balancing act that starts with knowing your fraud risk appetite.

Identity theft, also known as third-party fraud losses, is just the tip of the application fraud iceberg. First-party fraud can be 10 times as much.

FICO regularly tracks the national FICO Score distribution as an important gauge of US consumer credit behavior.