Some $442bn has been lost globally to financial fraud in the last year and as banks around the world accelerate their shift to data-driven fintech models, the complexity of these systems is creating new blind spots for fraud.

As Michael Down tells RBI, fraud now moves through coordinated networks of mule accounts, synthetic identities, and transactions, rather than in isolation.

As AI is helping to increase the speed, scale and sophistication of attacks, the real-world impact is one in which financial firms aren't currently set up to connect the dots within these invisible networks. And he explains how graph intelligence goes beyond detection, to change what's operationally possible for the teams behind the scenes and is successful in uncovering suspicious patterns that traditional systems miss.

RBI: In your opinion, how is AI increasing the speed, scale and sophistication of attacks in the FS sector?

Michael Down, Global Head of Financial Services at Neo4j:

AI has fundamentally changed the economics of fraud. What once required coordinated human effort – creating fake identities, crafting convincing phishing messages, and manipulating documents – can now be automated and deployed at scale in minutes with AI.

We're seeing this play out across multiple fronts: deepfakes are used to bypass identity verification systems through cloned voices, manipulated video and facial imagery. Separately, AI-generated synthetic identities combine fabricated personal information and manufactured digital footprints to create accounts that appear entirely legitimate, while AI-powered phishing campaigns continuously test and refine the approaches most likely to succeed. The result is fraud that is more convincing, more coordinated, and more sophisticated than anything the industry has seen before.

The challenge for financial institutions is that legacy detection systems were built for a different threat model - one where anomalies appeared individually and could be caught through rule-based controls. Today's AI-enabled fraud deliberately spreads activity across accounts, devices, and institutions to avoid triggering those rules. Traditional systems, therefore, need to adapt.

RBI: You have referenced work your firm did with BNP Paribas Personal Finance to reduce fraud by 20%...can you give some information on how the solution achieved this?

Down: We helped BNP Paribas Personal Finance fundamentally shift from treating fraud as an isolated event to helping them identify connected fraud networks.