AI agents are quietly transforming fraud prevention and securing the enterprise from the inside out.
Sydney Scott
Editorial Strategist, AI
Workday
A few years ago, fraud prevention still felt like a rulebook problem. Build enough rules, set enough thresholds, and you could usually keep the bad actors out. Banks and enterprises layered system after system on top of one another: if a purchase was too big, flag it; if an account looked unusual, pause it; if a location seemed off, send it to review.
It worked—until fraud got faster and smarter.
Today, criminals change methods as quickly as companies can update their software. They jump between devices, coordinate across accounts, and mimic normal users with eerie precision. Many older defenses are still built on static logic, and those rules break down the moment an attacker tries something new. According to research from FICO, fraud-detection systems degrade quickly as fraudsters adapt their tactics to evade them, leaving companies spotting attacks only after money or data has already disappeared.
That changed when AI agents arrived. What started as a handful of helpful tools quickly became something larger: a new layer of intelligence that works across the business, watching, predicting, and stopping threats in real time. These agents aren’t reactive. They’re proactive, independent, and constantly learning. They don’t just flag problems—they prevent them. For many companies, that shift is nothing short of transformational.
Agents are reshaping enterprise safety from the inside out and the new responsibilities leaders must confront as these systems grow more capable.
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In the past, fraud detection worked like a traffic stop. Data moved through the system first, then got inspected later. By the time analysts saw a suspicious payment, it had already settled. Many traditional fraud-monitoring systems run on analytical data warehouses that use batch processing — which builds in a delay, because transactions are only analyzed after they’re stored, making such systems slow to catch fraud in real time.
AI agents flipped the model. Instead of reviewing history, they watch streams of data as they flow through the system. They evaluate behavior, timing, location, device fingerprints, and financial patterns as they occur. If a payment looks wrong—say a new device, at an unusual hour, with amounts outside the customer’s behavior—the agent can pause the transaction immediately or challenge the user before funds move.
This capability, known as real-time interdiction, is something rules-based systems simply can’t match. TELUS Digital reported that companies using agentic AI to watch transfers mid-stream have seen fraud detection accuracy rise by up to 45%, while false alarms fall by nearly 80%, dramatically reducing customer friction.
For businesses handling thousands of payments a minute, these changes don’t just tighten security—they reshape operations. Fewer false alerts mean fewer customer complaints, fewer support calls, and fewer analysts drowning in manual reviews. The same TELUS report showed a 50% to 60% drop in fraud-related call volume after deploying autonomous agents to handle frontline checks.
The agents don’t work harder. They simply work faster than humans can.
The future of security will be an agent-versus-agent battle.
What’s surprising is just how organized modern fraud has become. Attacks rarely come from one person. They often come from groups—fraud rings—moving money through clusters of accounts, using recycled devices, or coordinating behavior across multiple platforms.
Traditional systems look at transactions one at a time. They miss the connections.
AI agents don’t.
They trace digital fingerprints—shared phone numbers, repeated IP addresses, subtle timing patterns, and cross-account relationships. They perform continuous link analysis, merging signals from across channels, devices, accounts, and historical behavior. When the agent sees a cluster that doesn’t match normal customer patterns, it flags the network—not just the transaction.
Once the system identifies a fraud ring, it can shut it down in minutes. According to a white paper by Lynx, a financial crime prevention company, AI-driven real-time mule-detection systems use transaction- and network-based analytics to flag suspicious accounts — potentially allowing banks to block or freeze activity as mule networks are uncovered.
This is the kind of coordinated response only an autonomous system can execute. And it matters, because criminals themselves are starting to coordinate through AI. A working paper from the Bank for International Settlements, shows that AI agent interactions can pose systemic financial-stability risks — including the potential for collusion and deception that could be exploited for large-scale fraud.
The future of security will be an agent-versus-agent battle. And enterprises need to prepare for that now.
Fraud isn’t always external. Sometimes the biggest vulnerabilities sit inside the company—especially in finance operations like accounts payable. Invoice scams, vendor-change fraud, and manipulated payouts cost organizations billions each year.
AI agents are increasingly deployed as internal financial guardians. They review every invoice and payment request, checking for anomalies in amounts, vendor details, approval timing, and formatting. Because they compare these signals across months or years of transaction history, they can spot subtle inconsistencies that a human would never catch.
When something looks suspicious, the agent doesn’t wait for approval. It automatically holds the payment, triggers a second review, or requires extra verification before funds move. This prevents everything from innocent errors to sophisticated internal fraud attempts.
Even after money moves, agents streamline dispute and chargeback cases by gathering documents, building evidence packets, and tracking recovery stages automatically. Only the most complex cases ever reach humans.
The result is a safer financial operation with fewer bottlenecks—and fewer quiet leaks.
In breaches involving shadow AI, the percentage of exposed customer personally identifiable information jumped from 53% to 65%.
For all their strengths, AI agents also introduce new kinds of risk. These aren’t the dramatic firewall breach events most leaders imagine. They’re smaller and more invisible.
Because agents work autonomously, they often exchange data with one another to complete tasks. A customer support agent may call a fraud-detection agent. A payments agent may query a know your customer (KYC) agent. And if those exchanges are not tightly controlled, the system may share more personal data than is necessary. This is called untraceable data leakage, a form of internal spillage that doesn’t appear in traditional logs.
IBM found that in breaches involving shadow AI—systems deployed without proper governance—the percentage of exposed customer personally identifiable information jumped from 53% to 65%.
The danger isn’t always malicious. Sometimes it’s an employee uploading the wrong document to the wrong agent. Sometimes it’s an agent accidentally forwarding sensitive information to another agent handling a different task. Because of the speed and scale of autonomous systems, a single mistake can ripple outward quickly.
Enterprises are responding by treating agents like digital insiders—privileged users with access controls, identity checks, and strict behavioral limits. Every agent must have defined permissions, monitored interactions, and authenticated communication with every other agent. Data visible to an agent is masked or tokenized by default, and input/output guardrails prevent manipulation through harmful prompts.
This isn’t traditional cybersecurity. It’s something closer to workforce governance—only the workforce is made of software.
As agents take on more responsibility, regulators expect companies to prove not just what an agent did, but why. That means traceability is no longer optional.
Modern agent platforms now log everything: the prompt, the context, the intermediate reasoning steps, the internal “state changes,” and the final action. This end-to-end visibility supports root-cause investigations, compliance, and audit requirements across GDPR, AML regulations, the EU AI Act, and industry-specific standards.
Without these logs, companies can’t legally use high-autonomy agents in critical workflows. But with them, the partnership between humans and AI becomes far stronger. Analysts can understand how the system made a decision, refine it, and build safeguards around it.
And that’s important, because for all their speed, agents still need human judgment. Humans validate edge cases, review high-risk decisions, and adjust policies based on context only people understand. The human role shifts from fraud hunter to policy architect, focusing on strategy instead of whack-a-mole detection.
In many ways, autonomy strengthens human responsibility rather than replacing it.
Deploying AI agents is not just a technical project—it’s an organizational transformation. Leaders must rebuild their governance frameworks so agents can operate safely. That includes updating identity systems to handle non-human actors, rewriting third-party risk programs to cover autonomous tools, and establishing central registries for every agent in production.
It also requires realistic planning. High-autonomy agents must operate in a variety of environments, with instant shut-off switches and fallback modes if they behave unexpectedly. Every agent must be patched regularly, monitored for anomalies, and evaluated continuously against human benchmarks.
Most importantly, companies need a strategy for when agents interact with other agents—both internally and across partner systems. That includes authenticated “handshakes,” permissioned communication, and full logging of every exchange.
Done well, this governance doesn’t slow innovation. It accelerates it by ensuring companies can adopt powerful systems without risking the trust of customers, regulators, or internal teams.
Deploying AI agents is not just a technical project—it’s an organizational transformation.
AI agents are not just faster fraud detectors. They represent a new kind of intelligence in the enterprise—autonomous, connected, and capable of acting at a speed no human team can match. They stop real-time payment fraud, break apart organized crime rings, protect internal financial systems, and tighten compliance workflows that once required armies of analysts.
But their autonomy means companies must treat them as powerful insiders, not simple tools. The next few years will separate organizations that deploy agentic AI with careful governance from those that rush ahead without guardrails.
The future of fraud prevention won’t be built on bigger rulebooks. It will be built on smarter agents, stronger oversight, and human teams that understand how to direct an intelligent workforce they can’t always see.
With the right controls, AI agents don’t just keep businesses safe—they make safety itself smarter, faster, and far more resilient than anything that came before.
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