Building an AI-Powered Fraud Prevention Program for Your Insurance Company

Deploying AI fraud detection isn’t a technology project. It’s an organisational transformation.

The technology is the easy part. The harder part — and what determines whether the investment delivers returns — is the organisational design: aligning stakeholders, redesigning workflows, managing the transition, measuring outcomes, and scaling what works.

This guide is written for insurance executives responsible for making the decision and overseeing the implementation. It’s strategic, not technical.

The Strategic Case

The Problem Is Accelerating

Insurance fraud is not a static problem. Two forces are converging to make it worse:

AI-enabled fraud is growing exponentially. Sumsub’s 2024 Identity Fraud Report documented that global identity fraud rates more than doubled from 1.10% in 2021 to 2.50% in 2024, driven primarily by AI-generated deepfakes. Pindrop’s 2025 Voice Intelligence and Security Report estimated US$12.5 billion in contact center fraud losses in 2024, with 2.6 million fraud events.

Legacy detection isn’t keeping up. Rules-based fraud flagging and predictive scoring — the backbone of most current programs — were designed for pre-AI fraud. They analyze claims data patterns, not evidence authenticity. When a fraudster submits AI-generated damage photos with a normal-looking claim, these tools don’t flag it.

The result: a growing volume of sophisticated fraud that current programs are structurally unable to detect.

The Financial Case

The Coalition Against Insurance Fraud estimates that insurance fraud costs American consumers at least US$308.6 billion annually, with roughly 10% of P&C losses attributable to fraud.

For an individual insurer, the calculation is straightforward (detailed in our ROI calculator article):

Estimate your undetected fraud exposure
Estimate the portion attributable to AI-generated evidence
Apply a realistic detection improvement rate
Subtract platform and operational costs

For most insurers processing more than 10,000 claims annually, the ROI is positive — often by an order of magnitude.

The Competitive Case

Insurers who reduce fraud losses can offer more competitive premiums, achieve better loss ratios, and demonstrate stronger risk management to investors and regulators. The Coalition Against Insurance Fraud estimates that fraud adds US$400-700 per year to the average American household’s premiums. Reducing your fraud burden is a direct competitive advantage.

Organisational Readiness Assessment

Before selecting technology, assess your organization’s readiness across five dimensions:

1. Data Maturity

Questions to answer:

Are claims photos, videos, and documents stored digitally and accessible via API?
Do you retain original media files, or only compressed/processed versions?
Can you trace media files from submission through the claims lifecycle?
Do you have historical claims data (including known-fraud cases) available for model validation?

Minimum requirement: Digital storage of claims media accessible via API. Without this, no AI detection tool can be integrated effectively.

2. Technology Infrastructure

Questions to answer:

What claims management platform(s) do you use? (Guidewire, Duck Creek, Majesco, custom?)
Does your platform support API integration and event-driven workflows?
Do you have cloud infrastructure for AI model hosting, or do you require vendor-hosted SaaS?
What are your data residency and security requirements?

Minimum requirement: API-capable claims management with the ability to trigger external services on claim events.

3. Process Readiness

Questions to answer:

Do you have a functioning SIU with defined escalation procedures?
Are fraud reporting workflows documented and consistently followed?
Can your claims workflow accommodate a new data input (detection results) without significant redesign?
Do you have a feedback loop from investigation outcomes back to detection tuning?

Minimum requirement: A functioning SIU and documented fraud escalation process. AI detection generates flags — you need an organization that acts on them.

4. Talent and Skills

Questions to answer:

Does your team include data science or analytics capability?
Is your SIU staff experienced with digital evidence and forensic analysis?
Do you have IT resources to support integration and ongoing maintenance?
Is there executive sponsorship for fraud technology investment?

Minimum requirement: Executive sponsorship and IT integration capability. Data science depth helps but isn’t essential — the detection vendor provides the models.

5. Culture and Change Readiness

Questions to answer:

Are claims staff receptive to technology-assisted decision-making?
Is there organisational willingness to act on AI-generated findings (deny claims, refer to SIU)?
Do claims managers trust analytics, or do they prefer purely manual processes?
Is there a track record of successful technology adoption in claims?

Minimum requirement: Willingness at the adjuster and manager level to incorporate detection findings into their workflow. Without this, the technology will be ignored.

Technology Selection

What You’re Buying

AI fraud detection for insurance typically comes as one or more of:

Platform (full-stack). Comprehensive fraud analytics including predictive scoring, network analysis, and media detection. Examples: Shift Technology, FRISS, SAS. These platforms provide broad capability but may not have deep insurance-specific media detection.

Specialized media detection. Purpose-built AI for analyzing photos, videos, documents, and audio for manipulation and generation. This is what deetech provides. Integrates with your existing fraud analytics and claims platform via API.

Claims platform modules. Fraud detection modules offered by your claims management vendor (Guidewire, Duck Creek). These provide integration convenience but may lag in specialized capabilities like deepfake detection.

For most insurers, the optimal approach is specialized media detection layered onto existing fraud analytics — adding the deepfake capability without replacing your current stack.

Evaluation Criteria

Rank vendors on:

Criterion	Weight	What to Evaluate
Detection accuracy on your data	Critical	PoC on your anonymised claims
Insurance-specific capability	Critical	Vehicle damage, property, documents, not just faces
False positive rate	Critical	On real claims, not benchmarks
Forensic evidence output	High	Court-ready reports with heatmaps and methodology
Integration capability	High	API quality, claims platform connectors
Update frequency	High	How quickly new generation methods are covered
Cost model	Medium	Per-claim, per-volume, or flat fee
Implementation support	Medium	Vendor assistance with integration and tuning
Scalability	Medium	CAT event burst handling

The Proof of Concept

Never buy without a PoC. Provide the vendor with anonymised claims from your portfolio — including both genuine claims and (if available) known-fraud cases. Measure:

True positive rate on manipulated evidence
False positive rate on genuine claims
Processing latency
Quality and usability of forensic reports
Integration effort required

A vendor unwilling to run a PoC on your data is either unconfident in their performance or unable to handle insurance-specific content. Either way, move on.

Implementation Roadmap

Phase 1: Foundation (Months 1-2)

Objective: Establish the detection capability and prove it works.

Complete PoC and confirm detection performance on your data
Finalise vendor selection and contract
Implement API integration with claims management platform
Configure automated analysis on new claims (initially one line of business)
Train SIU on interpreting detection results
Establish baseline metrics (current fraud detection rates, false positive rates)

Success criteria: Detection running on live claims in one line of business; SIU receiving and acting on alerts.

Phase 2: Expansion (Months 3-4)

Objective: Expand coverage and refine operations.

Roll out to additional lines of business
Tune detection thresholds based on Phase 1 false positive/negative data
Implement feedback loop (investigation outcomes fed back to vendor for model improvement)
Build adjuster-facing workflows (how detection results appear in the claims interface)
Establish compliance reporting integration (automated fraud report generation)

Success criteria: Detection covering all major lines; threshold-tuned to acceptable false positive rate; adjusters incorporating results into workflow.

Phase 3: Optimisation (Months 5-6)

Objective: Maximize ROI and build long-term capability.

Implement batch analysis of historical claims (identify previously undetected fraud)
Build management dashboards (detection volumes, hit rates, fraud prevention value)
Conduct first ROI analysis (compare fraud prevention value against program costs)
Optimize SIU workflows based on detection patterns
Plan for model updates and capability expansion (new media types, new generation methods)

Success criteria: Demonstrable ROI; optimized workflows; management visibility into program performance.

Phase 4: Scale (Ongoing)

Objective: Continuous improvement and expansion.

Regular model updates incorporating new generation methods
Expansion to additional media types (video, audio, medical imaging)
Integration with broader fraud analytics ecosystem
Industry intelligence sharing (NICB, Coalition Against Insurance Fraud)
Regulatory compliance adaptation as requirements evolve

Change Management

Technology adoption fails when people don’t use it. Key change management strategies:

For Claims Adjusters

Frame it as assistance, not oversight. Detection helps adjusters make better decisions — it doesn’t question their judgment. Present it as a forensic tool that catches things no human could see, not as a check on their work.

Make it invisible. Detection results should appear automatically in the claims interface. No extra steps, no separate systems. If it requires effort, it won’t be used.

Show wins early. When detection catches a manipulated claim in the first weeks, publicise it internally (appropriately anonymised). Nothing builds adoption like demonstrated value.

For SIU Investigators

Frame it as intelligence. Detection provides forensic intelligence that would be impossible to obtain manually. Investigators start cases with evidence, not just suspicion.

Invest in training. Investigators need to understand what detection results mean, how to interpret forensic reports, and how to present findings in legal proceedings.

For Executives

Frame it as risk management. The deepfake fraud threat is growing. Detection is the organisational response. Frame the program as risk mitigation with positive ROI, not as a cost center.

Measure and report. Regular reporting on detection rates, fraud prevented, and ROI keeps executive sponsorship engaged and funding secure.

Measuring Success

Leading Indicators (First 90 Days)

Claims analyzed per day (should reach 100% of target lines)
Average analysis latency (should be under target SLA)
Alert volume (baseline for tuning)
SIU review rate of alerts (adoption indicator)

Lagging Indicators (6-12 Months)

Additional fraudulent claims detected (versus pre-deployment baseline)
Estimated fraud prevented (detected claims × average claim value)
False positive rate (should decrease as thresholds are tuned)
SIU conversion rate (proportion of alerts confirmed as fraud)
Net ROI (fraud prevented minus program costs)
Loss ratio impact (measurable improvement in fraud-sensitive lines)

Strategic Indicators (12+ Months)

Deterrence effect (reduction in fraud attempt rates)
Regulatory positioning (proactive compliance demonstrated)
Competitive premium advantage (fraud reduction enabling better pricing)
Industry leadership (recognized capability in fraud prevention)

Common Pitfalls

Piloting forever. Some organizations run indefinite pilots without committing to production deployment. Set a decision timeline and criteria at the start.

Over-engineering the integration. Start simple: API call on claim submission, results written to claim record, threshold-based alerting. Add sophistication later.

Ignoring false positives. A detection tool with a high false positive rate will be ignored by adjusters. Monitor and tune actively in the first 90 days.

No feedback loop. If investigation outcomes aren’t fed back to the detection vendor, models can’t improve. Establish this from day one.

Treating it as IT’s problem. This is a business initiative with an IT component, not the other way around. Claims operations and SIU must own the program.

deetech partners with insurers from PoC through production deployment. We provide integration support, model tuning on your claims data, and ongoing capability updates as the fraud landscape evolves. Request a demo to start with a proof of concept.

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