FICO’s new machine learning models are much more sensitive to abnormal single transactions, allowing CNP fraud to be detected quickly.

Origination scores add significant value above and beyond the FICO® Score, which is based solely on the data found in a consumer’s credit bureau file.

Today’s post covers three categories of unsupervised learning that FICO has investigated, refined and put into our anti-money laundering (AML) solutions.

FICO's new CNP fraud detection models have been developed to detect fraudulent e-commerce fraud transactions as quickly as possible, to prevent significant fraud loss from an account.

This two-part blog unpacks the mysteries of two very different AI techniques: supervised and unsupervised learning.

In my new post on TechTarget's IoT Agenda, I explore how behavioral analytics could change driving for the better.

Here are some of the top things I think we'll see among the 31+ flavors of AI in 2018.

My talk focused on using machine learning and AI-driven analytics to take AML beyond what transaction monitoring, rules-based systems can do.

My recent work at FICO to make AI better has drawn upon my background in theoretical physics to create what we call defensive AI. This is needed, because AI-based attacks are not science fiction — they are happening today.

Explainable AI has become a hot topic in light of the European Union’s GDPR and society’s increasing reliance on AI systems. How does one of FICO's original Explainable AI innovations stack up?