Every SIU leader in the US P&C industry uses the same benchmark: an investigator handles 200 active cases and closes about 10 investigations per month. The number is worn smooth by repetition. It is the ceiling around which SIU headcount plans, loss ratio projections, and fraud-recovery budgets are built.
The benchmark is obsolete. It was built around a manual workflow - 14+ days of evidence gathering, 4-8 hours of report writing - that autonomous AI investigation has fundamentally changed. The new ceiling is 800+ investigations per investigator per month, 80x the legacy standard.
This piece lays out the core SIU performance metrics, the benchmarks that defined the last five years, and the 2026 benchmarks that account for AI-augmented workflows. The intent is practical: SIU leaders rebuilding their capacity plans need new numbers, and the industry needs a shared baseline.
Why benchmark SIU performance
SIU performance benchmarking serves three purposes: board-level accountability (how is fraud cost changing?), internal capacity planning (can we investigate everything we flag?), and vendor evaluation (is this new tool actually moving the numbers?). Without shared benchmarks, every conversation about SIU effectiveness defaults to anecdote.
For most of the last decade, the shared benchmarks came from Coalition Against Insurance Fraud surveys and NAIC annual reports. The numbers described a system that was investigating ~25% of what it flagged at a cost of approximately $2,500 per investigation. Those numbers held steady because the workflow held steady. They are now moving.
The core metrics
Seven metrics define SIU performance. Track them quarterly at minimum:
- Flagged claim volume - total claims referred to SIU per period. Driven by detection quality and claims volume.
- Investigation coverage rate - percentage of flagged claims that receive full investigation. The headline throughput metric.
- Cases per investigator per month - investigations completed, a per-FTE productivity measure.
- Time-to-close - average days from referral to final report. Measures workflow efficiency.
- Confirmed fraud rate - percentage of investigated claims where fraud is substantiated.
- Recovery rate - dollars recovered or denied relative to investigated claim value.
- Cost per investigation - fully loaded per-case cost (investigator time + vendor fees + technology).
These seven metrics interact. Investigation coverage depends on cases per investigator. Confirmed fraud rate depends on referral quality and investigation depth. Cost per investigation depends on workflow efficiency. Moving any one meaningfully requires addressing the others - which is why benchmark changes tend to arrive together rather than incrementally.
Current benchmarks (2020-2025)
These numbers are stable across the industry. Variance exists - top-20 carriers are marginally more efficient than mid-size regionals; line of business matters - but the central tendency holds. They describe what manual investigation capacity looks like.
2026 benchmarks with AI investigation
Throughput and cost changes are the headline. Confirmed fraud rate and recovery rate changes are more modest, because the underlying fraud density in the flagged population is roughly constant. What changes is how much of the flagged population actually gets investigated. Cases that were previously closed without investigation are now being investigated, which widens the base.
Cases per investigator per month - legacy vs AI-augmented
Benchmarks by carrier size
Throughput expectations scale with carrier size, but ratios are stable. Smaller carriers face a steeper economic hurdle to add investigation capacity, which is precisely why AI investigation has a disproportionately large impact on mid-size and specialty lines:
Closing the 75% gap
The most important consequence of the new benchmarks is the collapse of the coverage gap. For a decade, the SIU industry has operated under the assumption that ~75% of flagged claims will not receive full investigation because there is no capacity to investigate them. That assumption is no longer accurate.
When cases-per-investigator shifts from 10 to 800+, the constraint moves from investigator capacity to detection quality. If your detection platform flags 10,000 claims per year and you have 5 investigators who can each handle 800+ cases per month, you can investigate everything that is flagged. The remaining question is whether the flagging is right.
The new bottleneck is detection precision
Investigating 100% of flagged claims matters only if the flags are meaningful. Rules-based detection runs at 60-85% false positive rates. With autonomous investigation, the low cost of investigating a false positive (a few minutes of human review) makes the false positive rate manageable - but the throughput wave will also put pressure on detection platforms to improve signal quality.
For the downstream economic impact of closing the coverage gap, see insurance claims leakage: how uninvestigated claims drain profitability. For the architectural comparison of detection vs investigation, see legacy rules-based systems vs. autonomous AI.
Key takeaways
- The 2020-2025 SIU benchmark of 10 investigations per investigator per month was a manual-workflow ceiling, not a fundamental limit.
- 2026 benchmark with autonomous AI investigation: 800+ investigations per investigator per month (80x), 100% flagged-claim coverage (4x), 2-4 hour time-to-close (95% faster), ~$150 per investigation (94% lower cost).
- Track seven core metrics quarterly: flagged volume, investigation coverage, cases per investigator, time-to-close, confirmed fraud rate, recovery rate, cost per investigation.
- Investigation coverage gap closes from 25% to 100% at current SIU headcount. The new bottleneck becomes detection quality, not investigation capacity.
- Benchmarks scale with carrier size but ratios are stable. Mid-size and specialty carriers see disproportionate benefit because they face the steepest economic hurdle to manual SIU expansion.