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

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

Factors driving credit score decreases observed among student loan holders involved behaviors such as delinquency on credit products including bankcards, auto loans, and mortgages

Research shows balance aggregation results in modest impact to FICO® Scores

Analysis identified modest impacts to FICO Scores from forbearance-related potential increases in mortgage balance.

The set-up of a score validation can greatly influence model performance results — and sometimes inflate them

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.

FICO’s capabilities in a broad range of anti-fraud functions resulted in consistently good scores across the defined categories for the Chartis enterprise fraud report.

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.

Enterprises need an identity fraud solution that meets the dynamic challenges of digital maturation, threat landscape and emerging authentication data.

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.