Real-Time Fraud Detection Inside Your Contact Center

Insurance fraud isn’t a back-office problem anymore. It shows up right in the middle of customer conversations—during first notice of loss (FNOL), when emotions are high, details are fresh, and your team is trying to help fast.

Here’s the number that should change how you think about contact center workflows: U.S. insurance fraud losses are estimated at $308.6 billion annually (2025). When fraud detection happens days or weeks after intake, you don’t just miss suspicious patterns—you also pay for slower cycle times, higher handling costs, worse customer experience, and bloated open reserves.

This post (part of our AI in Insurance series) takes a clear stance: fraud detection belongs in the contact center, not just in downstream claims analytics. I’ll walk through a practical architecture pattern—using an AI-powered intake assistant plus real-time machine learning scoring—then add the operational playbook you’ll need to make it work in the real world.

Why fraud detection should start at FNOL (not after)

Answer first: You catch more fraud and create less friction when you assess risk while the claim is being reported—not after it’s already in the system.

Traditional fraud controls are often built around post-submission review: batch scoring, adjuster notes, SIU triage after the fact. That’s useful, but it’s late. By then, you’ve already:

• Opened reserves and triggered downstream work
• Paid for multiple touches (intake, follow-up, clarifications)
• Set customer expectations around timelines
• Potentially paid vendors (towing, rental, repair estimates)

Real-time fraud scoring at FNOL changes the operating model. Instead of “process then investigate,” you move to “verify, then accelerate or route” based on risk.

Two things get better at once:

1. Customer experience: low-risk claims can move faster with fewer handoffs.
2. Loss and leakage control: high-risk claims get early verification and SIU-ready documentation.

The contact center becomes the front line of trust.

The AI-native contact center pattern: intake → extract → score → route

Answer first: The most reliable pattern combines generative AI for conversation and extraction with traditional ML for scoring, then uses the contact center platform to route work based on risk.

A modern approach uses three building blocks:

1. Conversational intake (voice/chat/WhatsApp/SMS/email) to capture FNOL details consistently
2. Structured data extraction from the conversation (dates, locations, policy details, incident description)
3. Real-time fraud risk scoring that returns a probability or alert within seconds

In AWS terms, the RSS article demonstrates this with:

• Amazon Connect for omnichannel contact center flows
• Amazon Q in Connect for the self-service intake conversation (LLM-driven)
• AWS Lambda as the orchestration layer
• Amazon SageMaker AI hosting a fraud detection model (example: XGBoost)

The result is a workflow where the customer gets a natural intake experience, and the agent receives a compact, actionable view of:

• Who the customer is (profile/policy context)
• What happened (claim details)
• What the model thinks (fraud risk flag or score)

What makes this different from “just adding a chatbot”

A lot of teams stop at self-service deflection. That’s fine, but it’s not where the biggest value is.

The bigger play is: use the AI conversation to produce better inputs for better decisions.

If your intake process reliably captures the same fields in the same format (incident date, plate number, loss type, location, injury involvement, third-party details), you can:

• Reduce rework for adjusters
• Improve model signal quality
• Build consistent fraud triage rules
• Speed up honest claims without sacrificing controls

That’s customer service innovation with measurable operational impact.

Getting the intake prompt right: prompts, guardrails, and “extractable truth”

Answer first: Your intake assistant succeeds or fails based on how well it collects specific, usable information—not on how friendly it sounds.

Amazon Q in Connect uses a prompt-and-guardrails approach that’s worth copying even if you’re not on AWS:

• Prompts define the assistant’s role and the exact questions to ask
• Guardrails constrain behavior and reduce risky outputs
• Agents (configuration objects) determine which prompts/guardrails apply to each scenario

The subtle but crucial design goal: convert messy conversation into structured claim data.

A practical prompt design checklist for FNOL

If you’re building an AI intake flow, I’ve found these rules prevent most production failures:

1. Ask in a fixed sequence when sequence matters (date/time → location → what happened → involved parties)
2. Validate formats in real time (date format, vehicle plate patterns, phone/email)
3. Separate “narrative” from “fields”
   • Let the customer talk naturally
   • Then summarize into normalized fields your downstream systems can use
4. Capture uncertainty explicitly
   • “Customer unsure of exact time” is a valid value
   • Don’t force hallucinated precision
5. Design an escalation moment
   • When all mandatory fields are collected
   • Or when the customer is distressed/angry
   • Or when risk score crosses a threshold

Why this matters for fraud detection

Fraud models don’t score vibes. They score features.

When the intake assistant captures consistent features—like incident timing, prior claim frequency, mismatch between policy inception and loss date, or repeated vehicle identifiers—the fraud scoring model becomes more reliable.

A good conversational design is quietly doing data engineering.

Real-time fraud scoring: how to make ML useful to agents

Answer first: A fraud score is only valuable if it changes routing, questioning, or documentation while the customer is still engaged.

The RSS architecture uses a SageMaker AI endpoint for real-time inference. In the example, an XGBoost model is trained on a sample vehicle insurance fraud dataset and uses SMOTE to address class imbalance.

That’s the technical part. The contact center part is where many programs stumble.

Don’t show agents a score—show them an action

Agents don’t need a lecture on machine learning. They need clarity.

Instead of presenting “Fraud probability: 0.73,” the agent workspace should translate model output into a recommended next step, such as:

• Low risk → proceed with standard claim filing
• Medium risk → ask two additional verification questions
• High risk → warm transfer to a specialist queue or SIU intake

This is where contact center automation becomes a control surface for fraud operations.

Three verification plays that work (without wrecking CX)

When a claim is flagged, you want higher assurance with minimal friction. These are practical options:

1. Dynamic follow-up questions (based on loss type)
   • Example: for auto loss, confirm location, direction of travel, and point of impact
2. Out-of-band verification
   • Send a one-time link or code via SMS/email to confirm identity
3. Document-first routing
   • Prompt for photos, police report number, or repair estimate before payment workflows

The key is consistency. If customers feel singled out randomly, they’ll complain. If verification is framed as routine (“We do this to protect your account”), honest customers accept it.

Contact flows that drive ROI: routing, thresholds, and feedback loops

Answer first: Real-time fraud detection pays off when you tie the model output to measurable workflow changes—then improve the model using investigation outcomes.

A strong deployment uses contact center flow logic to implement handling paths like:

1. Route high-risk calls to a specialized fraud team
2. Trigger extra verification steps only when needed
3. Notify supervisors for sensitive escalations
4. Create a post-call review work item automatically

Setting thresholds: start conservative, then tune

Most companies get thresholds wrong because they optimize for one metric.

Start with what the business can operationally support:

• If your SIU can only handle 50 additional cases/day, calibrate the threshold to generate ~50/day.
• If agent handle time is a priority during the year-end surge (December is often brutal for staffing), prioritize low-friction verification over long interrogations.

Then tune based on outcomes.

The feedback loop you need (and many teams skip)

Fraud models drift. Fraudsters change tactics. Your data changes.

You need a closed loop:

• Model flags claim
• Investigation outcome is recorded (confirmed fraud / not fraud / inconclusive)
• Outcome is fed back into training data
• Thresholds and features are reviewed quarterly (or faster during spikes)

Without this loop, your “real-time fraud detection” becomes stale automation.

Common questions teams ask before rolling this out

How do we avoid bias or unfair targeting? Focus on behavioral and claim-consistency signals (timelines, contradictions, claim velocity, policy tenure) rather than demographic proxies. Use governance: monitor false positives by segment and require human review for adverse actions.

Will this slow down every customer? Not if designed correctly. The goal is selective friction: add steps only when the risk score warrants it, and speed up low-risk claims.

Do we need generative AI and ML, or can we pick one? If you want a natural intake experience across channels, generative AI is the best interface. If you want repeatable scoring, traditional ML is still hard to beat for structured risk classification. Together, they cover both sides: conversation and decisioning.

What to do next (if you want leads, not a science project)

Real-time fraud detection with an AI-powered contact center isn’t a “nice innovation.” It’s a practical way to reduce leakage, control operating cost, and protect customer trust—especially as fraud pressure stays high going into 2026.

If you’re evaluating AI in customer service for insurance, start with a narrow pilot:

• One claim type (auto FNOL is a good starting point)
• One or two channels (chat + voice)
• A clear routing plan for low/medium/high risk
• A monthly review of outcomes and false positives

Want a simple benchmark? If your pilot can reduce rework touches per claim and improve SIU hit rate without hurting CSAT, you’re on the right track.

The bigger question to leave you with: as AI gets better at real-time decisioning, which contact center workflows should still wait for back-office review—and which should move to the moment the customer first reaches out?