The case for purpose-built generative AI in fraud prevention

Financial crime has never stood still.

In my nearly three decades in financial services, I have watched fraud move in lockstep with the technology built to stop it, and at times, even stay a step ahead of it​,​ given the speed fraudsters adopt new technology.

I have witnessed decades of AI innovation ​that raised​ the bar on fraud detection, yet criminals continue to test those boundaries.

Fraud remains a top consumer concern, with over ​87.5 million American adults experiencing a scam or financial fraud each year.

That’s roughly one in every three American adults.

The question for financial institutions isn't whether AI has a place in fraud prevention​,​ but whether the AI they've deployed is built for the threat landscape they're facing today – and the one that's coming.

Compute has finally caught up with mathematical vision For decades, data scientists working on fraud prevention had theories they couldn't implement.

The ideas were sound, but the computers at the time weren't powerful enough to get the math done.

That constraint no longer exists.

Historically, most fraud detection has been carried out by building a profile summarizing a customer's typical behavior using sophisticated features, a neural network, and the customer’s current transaction to flag transactions that are suspicious.

It's an approach constrained by the computational limitations of time.

Today, access to GPU and other high-performance compute is changing the art of the possible.

Rather than analyzing a transaction in the context of a profile, GPUs allow us to implement entirely new algorithms that can evaluate a customer's extensive transaction history in real-time as the transaction happens.

The result is a significantly sharper, more accurate prediction and far fewer false alarms that can delay or stop legitimate purchases​,​ eroding customer trust.