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

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

The technology is the easier part. The harder part — and what determines whether the investment delivers — is organizational 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 fast. Sumsub’s Identity Fraud Report found that global identity fraud rates more than doubled, from 1.10% in 2021 to 2.50% in 2024, with deepfakes among the main trends — the number of deepfakes it detected rose fourfold from 2023 to 2024. Pindrop’s 2025 Voice Intelligence and Security Report found that deepfake fraud attempts rose more than 1,300% in 2024, and that synthetic-voice attacks on insurers rose 475%. More broadly, US consumers reported losing US$12.5 billion to fraud in 2024, according to the Federal Trade Commission.

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

The result: a growing category of fraud that current programs are not structured to detect.

The Financial Case

The Coalition Against Insurance Fraud estimates that insurance fraud costs American consumers at least US$308.6 billion a year across all lines (2022 study), and that fraud occurs in about 10% of property-casualty losses.

For an individual insurer, the calculation follows a simple structure (detailed in our ROI calculator article):

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

Any such model is illustrative — assumptions, not measured deetech outcomes — so run it with your own claims volumes, fraud rates and costs.

The Competitive Case

Insurers that reduce fraud losses can price more competitively, improve loss ratios and demonstrate stronger risk management to investors and regulators. The FBI has estimated that insurance fraud (excluding health) costs the average US family US$400–700 a year in higher premiums. Reducing your fraud burden is a direct competitive advantage.

Organizational 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 evaluation?

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 need vendor-hosted SaaS, or deployment in your own environment?
What are your data residency and security requirements?

Minimum requirement: API-capable claims management with the ability to call 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 input (detection results) without major redesign?
Do investigation outcomes feed back into how thresholds and escalation rules are tuned?

Minimum requirement: A functioning SIU and a 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?
Are SIU staff experienced with digital evidence and forensic analysis?
Do you have IT resources for 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 willingness to act on AI-surfaced findings (refer to SIU, request more evidence)?
Do claims managers trust analytics, or prefer purely manual processes?
Is there a track record of successful technology adoption in claims?

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

Technology Selection

What You’re Buying

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

Claims fraud platforms. Fraud analytics that score claims, entities and networks — predictive scoring, rules and network analysis. Examples include Shift Technology, FRISS and SAS. Some also offer media modules; FRISS, for example, offers Media Check for manipulated, reused and AI-generated images and PDFs.

Dedicated media authenticity. Specialized AI that analyzes photos, videos, documents and audio for signs of generation and manipulation. This is what deetech™ provides: a media-authenticity layer that sits alongside your claims fraud platform and connects to your claims systems via API. Fraud platforms score the claim; deetech™ verifies the media.

Claims platform ecosystems. Core claims systems such as Guidewire and Duck Creek typically make fraud capabilities available through partner integrations rather than as native modules.

For many insurers, the practical approach is dedicated media authenticity layered onto existing fraud analytics — adding deepfake detection without replacing the current stack.

Evaluation Criteria

Rank vendors on:

Criterion	Weight	What to Evaluate
Detection performance on your data	Critical	PoC on your anonymized claims media
Insurance-relevant coverage	Critical	Vehicle damage, property, documents — not just faces
False positive rate	Critical	On your genuine claims, not just public benchmarks
Forensic evidence output	High	Explainable reports with methodology, limitations and audit records
Integration capability	High	API and webhook quality, effort to integrate with your claims platform
Update practice	High	How the vendor responds to new generation methods
Cost model	Medium	Per-analysis credits, per-volume or flat fee
Implementation support	Medium	Vendor assistance with integration and threshold setting
Scalability	Medium	Handling claim surges after catastrophe events
Deployment options	Medium	SaaS, private or on-premises, to match your security requirements

The Proof of Concept

Never buy without a PoC. Provide the vendor with anonymized claims media from your portfolio — 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 the PoC and confirm detection performance on your data
Finalize vendor selection and contract
Implement API integration with the claims management platform
Configure automated analysis on new claims (initially one line of business)
Train the SIU on interpreting detection results
Establish baseline metrics (current fraud detection rates, referral volumes)

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 escalation thresholds based on Phase 1 outcomes
Establish a feedback loop that uses investigation outcomes to review thresholds and performance
Build adjuster-facing workflows (how detection results appear in the claims interface)
Connect detection documentation to your fraud referral process

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

Phase 3: Optimization (Months 5–6)

Objective: Build long-term capability and a documented business case.

Run batch analysis of recent historical claims to look for previously missed manipulation
Build management dashboards (analysis volumes, hit rates, modeled losses avoided)
Conduct a first business-case review (modeled losses avoided against program costs)
Optimize SIU workflows based on detection patterns
Plan for capability expansion (new media types, new generation methods)

Success criteria: A documented business case; optimized workflows; management visibility into program performance.

Phase 4: Scale (Ongoing)

Objective: Continuous improvement and expansion.

Keep detection current as generation methods evolve
Extend to additional media types (video and audio)
Integrate with the broader fraud analytics ecosystem
Share intelligence through industry bodies (NICB, the Coalition Against Insurance Fraud, or your market’s equivalent)
Adapt to regulatory requirements as they 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 surfaces details that are hard to see by eye, not as a check on their work.

Make it seamless. 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, share it internally (appropriately anonymized). Nothing builds adoption like demonstrated value.

For SIU Investigators

Frame it as intelligence. Detection provides forensic signals that are hard to obtain manually. Investigators start cases with specific findings, not just suspicion.

Invest in training. Investigators need to understand what detection results mean, how to read forensic reports and how findings may be presented in legal proceedings.

For Executives

Frame it as risk management. The deepfake fraud threat is growing. Detection is the organizational response. Frame the program as risk mitigation with a clear business case, not as a cost center.

Measure and report. Regular reporting on detection rates, confirmed cases and modeled losses avoided keeps executive sponsorship engaged.

Measuring Success

Leading Indicators (First 90 Days)

Claims analyzed per day (should reach 100% of target lines)
Average analysis latency (against your 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)
Modeled losses avoided (illustrative: confirmed cases × average claim value)
False positive rate (should fall as thresholds are tuned)
SIU conversion rate (proportion of alerts confirmed as fraud)
Net modeled benefit (modeled losses avoided minus program costs)
Loss ratio impact in fraud-sensitive lines

Strategic Indicators (12+ Months)

Deterrence effect (reduction in fraud attempt rates)
Regulatory positioning (proactive controls demonstrated)
Pricing advantage from lower fraud costs
Industry recognition for fraud prevention capability

Common Pitfalls

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

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

Ignoring false positives. A 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 used to review thresholds and performance, the program 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 the SIU must own the program.

deetech™ supports proofs of concept on your own representative claims media and integrates with your claims systems through its REST API and signed webhooks. Request a demo to evaluate it on your data.

Sources cited in this article: