As APAC financial institutions scale AI, leading firms are moving beyond siloed tools to capture a true AI Dividend. Grounded in new Atlassian & Mandala research, learn how frontier institutions are unifying business teams and using AI capacity gains to accelerate incident response, automate audit-readiness, and achieve operational resilience.

Financial crime doesn't operate in silos, yet most fraud detection tools do. Today's landscape is filled with point solutions: KYC platforms that stop at onboarding, AML engines focused on transaction monitoring, and network graph tools that excel technically. Fraud signals are everywhere, but data remains fragmented. AI Fraud Surveillance unifies financial, qualitative, and network signals into a single view, enabling fraud detection before it happens, not after.

In a world of synthetic everything, proof is becoming the scarcest commodity in finance. As AI-generated identities, deepfakes, and convincing digital deception become routine, institutions can no longer assume authenticity; they have to verify it. This session frames the problem and then works through the answer.

It opens with the shift underneath the shift: once trust can no longer be assumed, the burden moves from catching fakes after the fact to proving authenticity before a transaction happens at all. That reframes what needs to be provable rather than assumed, and who is positioned to build the systems that prove it. The discussion then gets specific about the different bets being placed on how to solve this: catching synthetic fraud at the point of verification, proving identity cryptographically without exposing the underlying data at all, and building the credential infrastructure that ties it together.

Attendees hear where each approach actually holds up against a motivated forger, and where the gaps between them still leave room for one to slip through.