What’s inside?
AI agents are closing the gap between spotting fraud and stopping it. Here's what that looks like in practice.
Fraud teams have never lacked for data. What they've lacked is time. By the time a pattern is identified, a rule written, tested, and deployed — the attack has often moved on.
AI agents change that. This ebook breaks down five capabilities that are compressing the distance between detection and action — without adding headcount or sacrificing the human judgment that consequential decisions require.
Inside you'll learn how AI agents are helping fraud teams:
- Build and deploy detection rules from a plain-language description — no engineering handoff required
- Test and optimize rules against real data before anything goes live
- Automatically triage alerts so analysts arrive at every case already oriented
- Guide investigations with adaptive checklists that make every case consistent and auditable
- Act on findings immediately, closing the loop between investigation and execution in a single workflow
Continuously Optimizing Fraud Decisions Faster
Identifying a suspicious pattern is only half the job. The other half — writing the rule, testing it, deploying it, investigating the alert, and acting on the finding — has always required more steps, more people, and more coordination than the threat waits for. That lag is where fraud scales.
AI agents don't just surface risk faster. They compress the distance between spotting a problem and doing something about it. Detection, strategy, investigation, and action happen in a single continuous workflow, not across separate tools, teams, and queues.
This ebook breaks down five agent capabilities that are changing how fraud teams operate in practice today.
What Are AI Agents?
Fraud teams are being pitched a lot of AI right now. Before evaluating any of it, it helps to understand the difference between two fundamentally different categories: AI Chat and AI Agents.
AI Chat generates responses to user prompts using natural language. It's designed for information retrieval, summarization, and content generation. It responds to prompts but does not take action independently. It can inform a decision, but the analyst still has to execute it.
AI Agents are systems that take action to achieve a defined goal. They analyze data, make decisions, and trigger workflows. They're designed to complete tasks, not just answer questions. When you describe what you need, an agent carries it through from analysis to execution.
The distinction that matters: AI chat answers questions. AI agents take action.
Most fraud tools marketed as "AI assistants" today are AI chat. They can help summarize a case or draft text. But they don't change the workflow: they still leave the work to the analyst. AI agents begin to shift that dynamic by completing specific tasks within the investigation and detection process.
The five capabilities in this ebook are all examples of AI agents at work.
Pre-Built or Build Your Own: Two Ways to Deploy AI Agents
Running an AML program?
See how AI agents are transforming compliance operations in our companion ebook
- Instantly triage alerts with auto-generated summaries that explain triggering logic in plain language
- Guide every investigation with adaptive checklists that produce consistent, auditable outcomes
- Draft complete, regulator-ready SAR narratives directly from case data
Download the ebook to get a clear vision of how you can start using Agentic AI.
Year-After-Year,
the Industry’s Choice
.png)
.png)
