How does progress in artificial intelligence stack up to the FICO credit score model?

Waiting for your regulator to require improved customer communications on scams places you at risk of distinct competitive disadvantage vis-à-vis your competitors

Defining and understanding different scam typologies yields results when identifying and preventing authorized push payment fraud

Latin America needs to get ready for the consequences of the growth of real-time payments

As regulators around the world roll out new rules to deal with APP fraud, these four tips can help payment service providers adapt to the new fraud prevention regimes

Blending artificial intelligence, machine learning and domain expertise in the design of credit scores - a video discussion with Agus Sudjianto

Naomi Palmer, a fraud consultant at FICO, won silver in the ‘Team Player of the Year’ category

FICO Scores R&D has empirically investigated potential benefits and risks associated with using some of the latest AI and machine learning approaches for credit scoring.

My presentation on “Transparency by Design: An Explainable AI Approach to Credit Risk Scoring” discusses our application of recent machine learning technologies to challenge the US FICO® Score.

The National Board for Customer Disputes in Sweden have ruled that banks should be liable for so-called “push payment” fraud losses over a certain amount.

Counterfeit cards are relatively simple to spot. So how were the criminals in the European road tolls scheme successful?

How are advances in artificial intelligence and machine learning changing credit risk assessment? That’s a topic I’ll be exploring in three presentations at FICO World 2018.

Want to know which consumers will more likely default when they encounter an unpredicted financial stress situation in the future? Traditional risk scores won’t give you the full picture.