As merchant acquiring models evolve, fraud management must expand beyond traditional transaction monitoring toward a broader view of ecosystem governance

Merchant acquirers now demand sophisticated onboarding automation, dynamic pricing intelligence, and predictive analytics to win and retain profitable merchant relationships

Improve customer management and reduce losses from check fraud by managing payment hold strategies using analytics

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

Enhance your customer management strategies to boost enterprise revenue through overlimit authorisations

The benefits of active vs. passive use of FICO customer management technology are clear

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

Decision science and predictive analytics can enable businesses to make the most of all available data to anticipate customer needs and prescribe the best action.

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.

Adopting machine learning to enhance not just the accuracy of models but also drive efficiency is critical to amplify a key asset - the analytic team – and stay competitive.

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.

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.