Experian: Targeting Fraud with an AI-Powered Analytics Layer

Experian has strengthened its position in the fraud prevention ecosystem by launching Transaction Forensics, an AI-powered solution designed to tackle increasingly complex financial crime in real time.

The product is the first major joint innovation since Experian’s July 2025 strategic investment in Resistant AI and is part of a broader industry shift toward layered, intelligence-led fraud detection. 

Built for UK financial institutions, Transaction Forensics combines Experian’s extensive consumer and commercial datasets with Resistant AI’s behavioural and transaction analytics to provide a granular, real-time view of risk across bank-to-bank payments.

Closing the detection gap

At its core, the platform uses more than 80 AI models to assess transaction intent as it happens. 

By enriching payment signals with identity, credit, fraud and anti-money laundering (AML) data – alongside historical behavioural insights – the system is designed to detect threats that evade traditional rule-based monitoring systems in growing quantities.

This comes at a time when financial institutions are grappling with a surge in AI-enabled fraud, from sophisticated authorised push payment (APP) scams to coordinated mule networks. 

These are attacks that operate at a speed and scale that legacy systems struggle to match, widening what’s known as the detection gap.

Paul Weathersby, Chief Product Officer for Experian UK&I, says: “Transaction Forensics marks a major step forward in fraud and financial crime prevention, one which is only possible thanks to our leading innovation and trusted, high-quality data.

“Financial services are facing a significant challenge in identifying and stopping fraud and financial crime attacks, which are increasingly enabled by AI and at a scale not seen before. 

“Transaction Forensics harnesses the power of AI to help businesses meet that challenge head on.”

However, efforts to tighten controls often introduce new challenges.

Higher false positive rates can frustrate legitimate customers and increase operational costs, as fraud teams are forced to manually review growing volumes of flagged transactions.

On top of this is increasing regulatory scrutiny, with the Financial Conduct Authority emphasising the need for explainable AI and alignment with Consumer Duty outcomes.

A layered approach to fraud prevention

Transaction Forensics is a direct response to these pressures.

Rather than replacing existing systems, it operates as an additional analytical layer, allowing institutions to enhance detection capabilities without overhauling their infrastructure. 

It can be deployed across Faster Payments, BACS and CHAPS, or selectively applied to high-risk transactions.

Early results suggest meaningful operational impact. Pilot testing has demonstrated a 200% increase in APP fraud detection, alongside an 80% reduction in false positives and a 50% decrease in total alert volumes – meaning more accurate detection and a sharper focus for investigation teams on genuine threats.

Martin Rehak, CEO of Resistant AI, says: “The use of AI in fraud and financial crime prevention is no longer optional but essential.

“By combining Resistant AI’s advanced models with Experian’s leading datasets, we are enabling financial institutions not just to address current attacks including APP fraud and money laundering but any new threats which will undoubtedly emerge in the years ahead.”

Industry partnerships for financial protection

With financial crime becoming more complex and technology-driven, no single organisation can tackle the challenge in isolation. 

This is why partnerships are emerging as a critical strategy for financial institutions and technology providers looking to stay ahead of increasingly sophisticated threats.

The collaboration between Experian and Resistant AI reflects this shift. 

By combining complementary strengths – deep data assets on one side and advanced AI modelling on the other – the partnership enables a more holistic approach to fraud detection. 

This is particularly important in an environment where signals of risk are fragmented across identity, behaviour and transaction data.

Beyond technical capability, partnerships are also accelerating innovation. These joint developments allow organisations to respond more quickly to emerging fraud typologies, regulatory expectations and customer demands for seamless digital experiences.