Insurance Fraud ROI Calculator: Quantifying the Value of Deepfake Detection
Calculate the ROI of implementing AI deepfake detection for insurance. Industry averages for fraud rates, claim sizes, and detection improvement rates.
Every fraud prevention investment comes down to one question: does it save more than it costs?
For deepfake detection, the answer depends on your claims volume, fraud exposure, and current detection capability. This article provides a framework — using real industry data — for calculating the expected return on an AI-powered media detection investment.
The Baseline Numbers
Industry Fraud Rates
The Coalition Against Insurance Fraud provides the most widely cited baseline:
- US$308.6 billion — estimated annual cost of insurance fraud to American consumers
- ~10% — estimated proportion of property-casualty losses attributable to fraud
For individual insurers, the relevant number is your exposure: what percentage of your claims are fraudulent, and how much are you currently losing?
Industry studies suggest fraud rates by line:
| Line of Business | Estimated Fraud Rate | Typical Claim Value |
|---|---|---|
| Auto (bodily injury) | 15-20% contain some fraud element | US$15,000-50,000 |
| Auto (property damage) | 10-15% | US$5,000-25,000 |
| Property (homeowners) | 10% | US$10,000-75,000 |
| Workers’ compensation | 10-15% | US$20,000-60,000 |
| Health | 3-10% of total spend | Variable |
Sources: Coalition Against Insurance Fraud, Insurance Research Council estimates. Ranges reflect industry variation.
Current Detection Rates
Most insurers detect and prevent only a fraction of total fraud. Industry estimates suggest:
- SIU referral rate: 3-5% of claims are flagged for investigation
- SIU confirmation rate: 30-50% of investigated claims are confirmed as fraudulent
- Undetected fraud: Estimated at 2-5x the volume of detected fraud
This means for every fraudulent claim you catch, two to five more are paid without challenge.
The Deepfake-Specific Gap
Current fraud detection tools — rules-based flagging, predictive scoring, network analysis — were designed for pre-AI fraud. They analyze claims data patterns, not evidence authenticity.
When fraud involves AI-generated or manipulated evidence:
- Photos of fabricated damage pass visual inspection
- Forged documents match expected formatting
- Synthetic voices pass basic authentication
- Claims data patterns appear normal
These claims are invisible to existing detection systems. This is the gap that deepfake detection closes.
The ROI Framework
Step 1: Estimate Your Fraud Exposure
Annual claims volume × fraud rate × average fraudulent claim value = Total annual fraud exposure
Example for a mid-size P&C insurer:
- 100,000 claims per year
- 10% fraud rate = 10,000 fraudulent claims
- Average fraudulent claim value: US$25,000
- Total fraud exposure: US$250 million
Step 2: Estimate Currently Undetected Fraud
Total fraud exposure × (1 - current detection rate) = Undetected fraud
- Total fraud exposure: US$250 million
- Current detection rate: 30% (through existing SIU and analytics)
- Undetected fraud: US$175 million
Step 3: Estimate Deepfake-Attributable Fraud
What proportion of undetected fraud involves AI-generated or manipulated evidence? This is harder to estimate precisely because — by definition — it’s currently going undetected.
Conservative estimates based on the trajectory of AI-enabled fraud:
- Sumsub’s 2024 Identity Fraud Report showed identity fraud rates doubling from 1.10% to 2.50% between 2021 and 2024, driven by AI
- Pindrop’s 2025 Voice Intelligence and Security Report documented 2.6 million fraud events and US$12.5 billion in contact center fraud losses in 2024
A reasonable estimate: 10-25% of currently undetected fraud involves AI-generated or manipulated evidence, and this percentage is growing.
Using the mid-range (15%):
- Undetected fraud: US$175 million
- Deepfake-attributable: 15%
- Deepfake fraud exposure: US$26.25 million
Step 4: Estimate Detection Improvement
Deepfake detection tools won’t catch 100% of AI-enabled fraud. Realistic detection rates depend on the quality of the tool, the sophistication of the fraud, and integration effectiveness.
Conservative estimate: 40-60% of deepfake-attributable fraud detected in the first year, improving as models are tuned to your claims data.
Using the mid-range (50%):
- Deepfake fraud exposure: US$26.25 million
- Detection rate: 50%
- Additional fraud prevented: US$13.125 million
Step 5: Calculate Net ROI
Fraud prevented - (platform cost + investigation cost increase + integration cost) = Net annual benefit
Typical costs:
- Detection platform: US$200,000-500,000 per year (varies by volume and vendor)
- Additional investigation costs: US$50,000-150,000 (more flagged claims = more investigations, though many are automated)
- Integration and training: US$50,000-100,000 (primarily first year)
Using mid-range estimates:
- Fraud prevented: US$13.125 million
- Total costs: US$600,000 (first year), US$450,000 (subsequent years)
- Net benefit year 1: US$12.525 million
- ROI: ~2,000%
Even with much more conservative assumptions — lower fraud rates, lower deepfake attribution, lower detection rates — the ROI remains strongly positive.
Sensitivity Analysis
The ROI calculation is most sensitive to three variables. Here’s how the numbers shift:
Variable 1: Deepfake Attribution Rate
| % of undetected fraud that’s deepfake-enabled | Annual fraud prevented (50% detection) | Net ROI |
|---|---|---|
| 5% (very conservative) | US$4.375M | US$3.775M |
| 10% | US$8.75M | US$8.15M |
| 15% (base case) | US$13.125M | US$12.525M |
| 25% (aggressive) | US$21.875M | US$21.275M |
Even at the most conservative 5% estimate, the net benefit exceeds US$3.7 million — roughly 6x the platform cost.
Variable 2: Detection Effectiveness
| Detection rate on deepfake fraud | Annual fraud prevented (15% attribution) | Net ROI |
|---|---|---|
| 30% (low) | US$7.875M | US$7.275M |
| 50% (base case) | US$13.125M | US$12.525M |
| 70% (optimized) | US$18.375M | US$17.775M |
Variable 3: Insurer Size
| Annual claims volume | Total fraud exposure (10%) | Deepfake fraud prevented (15% × 50%) | Net ROI |
|---|---|---|---|
| 25,000 (small) | US$62.5M | US$3.28M | US$2.68M |
| 100,000 (mid) | US$250M | US$13.125M | US$12.525M |
| 500,000 (large) | US$1.25B | US$65.625M | US$65.025M |
The ROI scales with claims volume. For large insurers, the numbers are enormous.
Beyond Direct Fraud Prevention
The ROI calculation above captures only the direct benefit of preventing fraudulent payouts. Additional value includes:
Reduced Investigation Costs
Automated detection triages claims more efficiently than manual review. SIU investigators spend time on confirmed-suspicious claims rather than screening the entire claims pipeline. This reduces the cost per confirmed fraud case even as detection rates increase.
Faster Legitimate Claims Processing
When detection runs automatically at intake, clean claims are cleared quickly — no investigation hold, no adjuster suspicion, no delays. This improves customer satisfaction scores and retention.
Lower Loss Ratios
Improved fraud detection directly reduces loss ratios. A 1-2 percentage point improvement in loss ratio has significant implications for underwriting profitability and competitive positioning.
Premium Competitiveness
Lower fraud losses enable more competitive pricing. The Coalition Against Insurance Fraud estimates that fraud adds US$400-700 per year to the average American household’s insurance premiums. Insurers who reduce their fraud burden can offer better rates.
Regulatory Positioning
Demonstrating advanced fraud detection capabilities strengthens regulatory relationships. The Coalition Against Insurance Fraud notes that 43 states and DC require insurers to report suspected fraud. Having AI detection in place demonstrates proactive compliance.
Deterrence
The existence of AI detection capability, once known, deters fraud attempts. Fraudsters who know their submitted evidence will be analyzed for manipulation will direct their efforts elsewhere — ideally to insurers without detection capability, not yours.
Building the Business Case
For the CFO
Frame the investment as loss ratio improvement. Calculate the expected reduction in fraudulent payouts as a percentage of total claims spend. Even a fraction of a percentage point of loss ratio improvement justifies the platform cost for most insurers.
For the CTO/CIO
Frame the investment as closing a technology gap. Current fraud analytics were built for pre-AI fraud. AI-generated evidence bypasses existing detection entirely. Deepfake detection closes this gap without requiring replacement of existing systems — it layers on top via API integration.
For the Chief Claims Officer
Frame the investment as adjuster effectiveness. Automated detection means adjusters receive forensic intelligence with every claim — not just the ones that “feel wrong.” This catches fraud that even experienced adjusters would miss while freeing their time for complex assessment work.
For the Board
Frame the investment as risk management. The deepfake fraud threat is growing exponentially while detection capability at most insurers remains zero. The question is not whether to invest, but when — and the cost of delay is measured in undetected fraudulent payouts.
Running Your Own Numbers
To calculate your specific ROI:
- Get your annual claims volume by line of business
- Estimate your fraud rate — use 10% if you don’t have a more specific number
- Estimate your current detection rate — what percentage of fraudulent claims does your SIU identify?
- Apply the deepfake attribution factor — start with 10-15% and adjust based on your digital claims mix (higher digital submission = higher deepfake exposure)
- Apply a detection rate — 40-60% is realistic for first-year deployment
- Subtract platform and integration costs — request pricing from vendors for your specific volume
If the resulting number is positive — and for virtually any insurer processing more than 10,000 claims annually, it will be — the business case is clear.
Related Reading
deetech can help you quantify the ROI of deepfake detection for your specific portfolio. We provide proof-of-concept deployments on your anonymised claims data so you can measure detection rates before committing. Request a demo.
Sources cited in this article: