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

Understand what fraud analytics entail and how they leverage AI, machine learning, business intelligence and data science to improve fraud detection

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

FICO® Focused Language Model and FICO® Focused Sequence Model set global standards for Responsible AI, delivering measurable enterprise value

A unified decisioning platform enforces development standards to ensure AI systems are built responsibly from the ground up

A full view of the customer journey is critical to detecting and preventing digital wallet fraud, as are enhanced communications and segmented strategies

Which works better for building credit risk models: using AI and machine learning or using traditional scorecards?

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

Here's how to get the full power of machine learning without losing the transparency that’s important from credit risk scorecards

The FICO European Fraud Map shows how Card Not Present fraud is rising across Europe, and why a unified approach to fighting fraud is vital

UK Finance’s Fraud Report 2025 shows changes in fraud typologies – underlying much of this shift is evolving techniques in social engineering

GenAI hallucinations are primarily caused by the ways that large language models (LLMs) are built

We have built a data science organization that prizes creativity and operationalizes innovation for demonstrable business benefit and positive customer impact

Ethical AI means digging into what machine learning models are learning – which is a complex task

Money mules are causing significant problems across the globe. Organizations must take proactive steps to identify and disrupt them.