According to the Fraud Readiness Benchmark 2026, about 90% of surveyed financial institutions expect AI to speed up fraud investigations and reduce their cost.
As fraud and scam volumes surge, so does the workload for fraud teams. Every case comes with work: pulling data from different sources, tracing where the money went, reviewing customer communications, documenting what happened... Multiply that work across hundreds or thousands of cases, and the pressure quickly mounts.
That pressure is only set to increase as fraud reimbursement becomes more and more common. Reimbursement mandates, such as PSD3/PSR rules, add more work to every case, often against tight regulatory timelines. Among surveyed European institutions operating in markets where reimbursement is already live or rapidly taking shape, 72% reimbursed 500 or more fraud cases in the past year, while one in five dealt with more than 10,000. That’s roughly 200 cases every week that need to be investigated and refunded.
With that much workload coming through, fraud teams need something close to assembly-line efficiency just to keep up.
No wonder banks and fraud teams are increasingly turning to AI. The same technology powering today’s fraud, from deepfakes and voice cloning to personalized phishing, can also become the fraud team’s MVP, helping institutions fight back while keeping costs manageable.
Speed is one of AI’s clearest advantages in fraud investigations, and 91% of surveyed institutions agree that AI can significantly shorten investigation times. Identifying linked transactions or gathering evidence can take an analyst hours; AI can do much of that work in seconds.
Those saved hours can make a real difference. The faster teams can detect what’s happening, the better their chances of stopping fraud in time or recovering stolen funds before they disappear. That becomes even more critical as instant payments increasingly mean instant fraud.
Speed matters after the fraud is detected, too. Reimbursement requirements put fraud teams on the clock, turning faster investigation and resolution from an operational advantage into a compliance necessity. The same speed matters to customers: faster decisions mean quicker answers and, where applicable, getting their money back sooner.
There’s one problem, though, with focusing solely on speed: the work is still there. A faster investigation is still an investigation. The bigger AI opportunity is reducing how much manual work fraud teams have to do in the first place.
About 90% of surveyed institutions believe AI can significantly reduce the cost of fraud investigations. And that means more than simply doing the same work faster.
Before an analyst gets involved, AI can do much of the heavy lifting: prioritizing cases, filtering out false positives, connecting related alerts, and pulling together the information needed. Instead of spending time assembling the pieces, analysts can focus on understanding what those pieces mean and investigating what actually needs their judgment.
The value becomes even clearer when the same fraud operation shows up across multiple cases. One analyst might be looking at a suspicious transaction while another reviews a related account or behavior elsewhere. AI can spot those connections rather than leaving teams to find them manually.
And there’s a knock-on effect for the whole team. If AI takes care of more of the groundwork, rising case volumes don’t necessarily have to mean growing headcount. Automation leaves more analyst time for complex investigations, new fraud patterns, and decisions where having a human in the loop actually adds value.
That distinction matters. The goal isn’t to automate the investigator out of the investigation. It’s to automate the work that doesn’t need an investigator.
Taking work off an investigator’s desk helps under one condition: you can actually trust what’s being done instead. In fraud, automation cannot come at the expense of control, explainability, or accuracy.
That’s the principle behind how ThreatMark applies AI to fraud investigation:
Keeping the analyst in control: AI acts as an enabler, not an autonomous decision-maker. Fraud teams keep the final word, backed by ThreatMark’s real-time risk scores, alerts, reason codes, and contextual signals.
Making AI decisions explainable: Rather than relying on broad black-box decisions, ThreatMark shows analysts why something was flagged. Version-controlled, tested models also support the governance and compliance requirements around AI.
Reducing false positives: False positives create their own operational cost. ThreatMark uses machine learning to refine risk scoring, helping fraud teams stop fraud without unnecessarily blocking legitimate customers.
Those safeguards make it possible to put AI to work with confidence. ThreatMark’s AI also keeps learning. By continuously analyzing behavior, device usage, and transaction context, it can spot anomalies and recognize new patterns as they develop. That helps fraud teams adapt as fraud tactics change, rather than constantly playing catch-up.
The near parity between those seeing speed benefits (91%) and cost benefits (90%) shows just how closely the two are connected, and how widely that value is recognized. AI is becoming part of the infrastructure fraud teams need to operate at scale.
However, preparing for what comes next is an important part of the AI case. Yes, AI can make today’s investigation workload faster and more economical to handle. But ultimately, the goal is to move fraud prevention further upstream, identifying and disrupting threats before they turn into losses. Here, too, AI has an increasingly important role to play.
See how AI-driven detection speeds investigations and cuts cost. Talk to ThreatMark.