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Strengthening financial services with AI fraud detection

How financial institutions are using GenAI and real-time detection to stop fraud before it happens

By

Kelly Manrique

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In financial fraud, AI plays a paradoxical role as both shield and sword. During our recent Financial Services Summit, Anthony Scarfe, deputy CISO at Elastic, joined Ludwig Adam, CTO at petaFuel, to examine AI's growing impact on fraud prevention. petaFuel is a leading MasterCard processor and payment solutions provider.

On the defensive side, Scarfe explains how “LLMs are going to enable a very fast summarization of those events into more of a story, more of a big picture, so that an analyst confronted with that event has the instructions of what to do.” Yet Adam warns that criminals are wielding the same tools: “The same way we can use large language models to reduce our mean time to react, the fraudsters use the same technology to reduce time and cost while scaling their attacks.”

AI adoption is rising — but so is the threat

Expert consultants agree with this sobering reality. According to Deloitte, potential fraud losses for FSIs in the United States alone could reach US$40 billion by 2027, highlighting why financial services are racing to strengthen their defenses. The response has been decisive: 91% of US banks currently use AI for fraud detection, while 83% of anti-fraud professionals plan to incorporate GenAI into their systems by 2025. Yet, this rapid adoption of AI brings its own set of challenges. Gartner emphasizes that success depends heavily on proper governance and security management. Financial services that get this right are projected to achieve significantly higher customer trust ratings and better regulatory compliance scores than their competitors.

According to Adam, the scale and speed of modern fraud demands a fundamental shift in detection approaches. The payment ecosystem is complex, so, “We need to react in real time; we need to analyze new fraud patterns that pop up instantaneously, within minutes, in order to mitigate the risk.” Traditional batch processing and manual checks are no longer sufficient given the volume of transactions and sophistication of attacks. 

This challenge is what PSCU — a network of 1,500 credit unions in the United States — has addressed in partnership with Elastic. The organization faced significant challenges with its legacy fraud detection system, including delayed data processing and limited data sources. After implementing Elastic's AI-driven platform, the results were dramatic. “Over the first 18 months, [they] saved about $35 million in fraud across those 1,500 credit unions,” Scarfe reports. “They also reduced their mean time to respond to fraud by about 99%.” Most importantly, this meant protecting customers from fraud before they even realized they were at risk. The success hinged on the ability to process vast amounts of data in real time and apply AI to detect anomalies.

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Combining GenAI with human context is the future

The conversation paints a clear picture of an industry racing to harness AI's potential for real-time fraud detection while grappling with sophisticated criminals equally quick to adopt these technologies. Success, according to Adam, lies in “a mix of technologies: the classical machine learning-based approaches and the GenAI approach,” while always incorporating the human factor.

Fraud doesn’t wait, and neither should your fraud detection tools. See how Elastic and industry leaders are combining automation, GenAI, and data to protect financial institutions and their customers. 

Watch the full session now to learn how Elastic and petaFuel are applying GenAI to real-world fraud detection.

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.

In this blog post, we may have used or referred to third party generative AI tools, which are owned and operated by their respective owners. Elastic does not have any control over the third party tools and we have no responsibility or liability for their content, operation or use, nor for any loss or damage that may arise from your use of such tools. Please exercise caution when using AI tools with personal, sensitive or confidential information. Any data you submit may be used for AI training or other purposes. There is no guarantee that information you provide will be kept secure or confidential. You should familiarize yourself with the privacy practices and terms of use of any generative AI tools prior to use. 

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