Insurance Fraud ROI Calculator: Quantifying the Value of Deepfake Detection
A step-by-step model for the return on deepfake detection in insurance, with sourced baselines and clearly labeled assumptions you can replace.
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 gives you a framework for estimating the return on media verification. It starts from published baselines, then makes every assumption explicit so you can replace it with your own numbers.
Illustrative — assumptions, not measured deetech outcomes. The worked example below is a model. Its fraud rates, attribution shares, detection rates and costs are assumptions chosen to show how the calculation works. None of them is a measured result from deetech or any other vendor.
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?
Illustrative planning ranges by line:
| Line of Business | Assumed Fraud Rate | Assumed 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 |
Illustrative planning ranges, not published statistics for any market. The Coalition Against Insurance Fraud estimates that fraud occurs in about 10% of US property-casualty losses; where you have your own loss and SIU data, use that instead.
Current Detection Rates
Most insurers detect only a fraction of total fraud. Published estimates of undetected fraud vary: the UK’s Association of British Insurers estimates that roughly as much fraud goes undetected as is detected, and Australia’s Insurance Council estimates about A$400 million a year undetected against A$560 million detected in motor and property. For this model, use your own figures:
- SIU referral rate: the share of claims your team flags for investigation
- SIU confirmation rate: the share of investigated claims confirmed as fraudulent
- Undetected fraud: your best estimate of fraud paid without challenge, relative to what you detect
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 often invisible to systems that score claims data alone. That is the gap media verification is meant to close.
The ROI Framework
Step 1: Estimate Your Fraud Exposure
Annual claims volume × fraud rate × average fraudulent claim value = Total annual fraud exposure
Illustrative example for a hypothetical 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% (assumed; 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.
No published study measures this share for insurance. The trend data points one way:
- Sumsub’s 2024 Identity Fraud Report found identity fraud rates more than doubled, from 1.10% to 2.50%, between 2021 and 2024, with deepfakes one of the main trends
- Pindrop’s 2025 Voice Intelligence and Security Report recorded a 475% rise in synthetic-voice attacks on insurers in 2024
Assumption for this model: 10–25% of currently undetected fraud involves AI-generated or manipulated evidence. Adjust it to your digital claims mix.
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.
Assumption for this model: 40–60% of deepfake-attributable fraud is flagged and then confirmed by your reviewers. Measure this in a proof of concept on your own media rather than taking any vendor’s figure on trust.
Using the mid-range (50%):
- Deepfake fraud exposure: US$26.25 million
- Detection rate: 50%
- Modeled losses avoided: US$13.125 million
Step 5: Calculate Net ROI
Modeled losses avoided − (platform cost + added investigation cost + integration cost) = Modeled net annual benefit
Assumed 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 assumptions:
- Modeled losses avoided: US$13.125 million
- Total costs: US$600,000 (first year), US$450,000 (subsequent years)
- Modeled net benefit, year 1: US$12.525 million
- Modeled return: about 21x first-year cost
With lower assumptions for fraud rate, deepfake attribution and detection rate, the modeled return shrinks but, in this example, stays positive. The sensitivity tables show how far.
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 | Modeled losses avoided (50% detection) | Modeled net benefit |
|---|---|---|
| 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 a 5% attribution, the modeled net benefit exceeds US$3.7 million, about six times the total first-year cost.
Variable 2: Detection Effectiveness
| Detection rate on deepfake fraud | Modeled losses avoided (15% attribution) | Modeled net benefit |
|---|---|---|
| 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%) | Modeled losses avoided (15% × 50%) | Modeled net benefit |
|---|---|---|---|
| 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 modeled benefit scales with claims volume. In practice, platform and review costs also scale with volume; this table holds them constant for simplicity.
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. An older FBI estimate put the cost of non-health insurance fraud at US$400–700 a year per US family in higher premiums. Insurers who reduce their fraud burden can offer better rates.
Regulatory Positioning
Demonstrating strong fraud controls strengthens regulatory relationships. In the US, 43 states and the District of Columbia require insurers to report suspected fraud, and verifying claims media shows regulators a proactive approach to an emerging risk.
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 fast, while most insurers still do little systematic verification of claims media. 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
Under the base-case assumptions above, and with costs held constant, the model stays positive down to roughly 5,000 claims a year. Test whether those assumptions hold for your book, ideally with a proof of concept on your own media, before relying on the result.
Related Reading
deetech™ can help insurers and claims teams build this model with their own numbers. A proof of concept on representative, anonymized claims media measures detection on your data before you commit. Request a demo.
Sources cited in this article: