Data bias and untrained data scientists pose risks to AI models - and the trust we place in business decisions

Collaborative Profiling is one of the advanced techniques in our fraud models for prepaid card fraud

New FICO data shows high rate of card fraud as criminals hitch a ride on Uber and Lyft

Artificial Intelligence is encountering new demands in the areas of responsibility, advocacy and regulation.

Credit cards appear to be the main target of fraud, with an average fraud rate 1.5 times that of debit cards

FICO analytics can identify more low-value card fraud, an important next step in stopping criminals

The BoE's new report says explainable AI is necessary for machine learning to be applied in credit management

As CNP fraud targets the online growth in luxury spending, new analytics improve the velocity of detection

“I’m Scott Zoldi and I’ve authored 95 patents on AI, machine learning and various analytic techniques. I’m Chief Analytics Officer of FICO. Ask me Anything.*

Neural networks are a powerful form of machine learning, but their black box nature often defies explainability. We have found a solution to this problem.

Regarding fraud analytics for open banking, unsupervised machine learning can be a strong weapon for fighting financial crime events

My recent patent work has laid a foundation for what I predict will be the tech industry’s biggest development in 2019: ethical AI.

Behavioral profiling approaches are extremely important in tackling fraud that happens when banks share financial data with third parties through application programming interfaces.

Uncovering hidden patterns in money movement makes machine learning for AML a very attractive enhancement to existing AML operations.

With a view to stopping real-time payments fraud, FICO recently released the Retail Banking Consumer fraud model.