The case for purpose-built generative AI in fraud prevention | Tech Radar

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The case for purpose-built generative AI in fraud prevention

Purpose-built models are essential for transaction analytics and fraud prevention

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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.

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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.

Thanks to greater GPU availability and computational power, we're now seeing new opportunities to support fraud prevention with a sequence-modeling transformer – not a generic transformer, but one purpose-built for transaction analytics and financial crime.

With a purpose-built transformer architecture that’s trained exclusively on financial transaction data and engineered for a single focused task, financial services institutions can detect financial crime in real time using deep personalization and the context of the customer transaction history.

These are not overarching ‘do everything’ models. They are focused foundation models purpose-built to deliver auditable, high-performing, low-latency generative AI for the fight against financial crime. Each of these models specializes in distinct areas, such as account takeover, scams, mule detection, and first-party misuse.

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Independent models focused on their specialty allow for a more complete, accurate, and transparent picture than any single model working alone. And this is just the start, as the same methodology applies to risk decisions, hardship, collections, and any application where understanding our customers leads to better engagement, protection, and service.

The fraud prevention capabilities that will define the next five years are being built right now. Enterprises investing in protecting customers from fraud – both now and in the future – understand that purpose-built models, use of specialized compute, and AI agents are the path to protecting their customers.

The math protecting consumers today was invented decades ago by AI scientists who saw the potential in algorithms even before the compute and infrastructure existed. I've spent much of my career continually chasing that goal to ensure that the industry is ready to bring the best algorithms when compute shows up.

Some of my proudest work came from refusing to settle and trust that technology will catch up with AI invention and math. Every patent, every model, every AI experiment has served the same purpose: making sure the industry is ready to lead with the best AI tools once compute catches up with scientific invention.

That work is happening right now. The only question is whether financial institutions are part of it or racing to catch up.

This article was produced as part of Tech Radar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of Tech Radar Pro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

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