The $17.7M Cost of a Single Hiring Filter
At one Fortune 500 insurance carrier, a single screening rule, "must have insurance experience," would have rejected 2,863 agents who went on to produce. Those agents generated $17.7M in annual premium credit. The filter did not select better hires. It removed millions of dollars in production. This number comes from connecting applicant tracking data to actual outcomes across 10,765 hires, a view no single system could produce on its own.
Source: "Decision Traces," Saad Bin Shafiq, NODES, 2026. N=10,765 agents hired 2022 to 2025 at a Fortune 500 insurance carrier, analyzed inside the carrier's VPC. Read it on arXiv.
Estimate a filter's cost on your own numbers: Hiring ROI Calculator.
How one filter loses $17.7M
The "insurance experience required" rule looks reasonable. The data says otherwise. Agents with insurance experience produced at 28.0%, while agents without it produced at 33.7% (odds ratio 0.763, anti-predictive within multiple hiring years). Of the agents who lacked the flag, 2,863 went on to produce. The sum of their observed annual premium credit was $17.7M. Every one of them would have been screened out at the first gate by a standard ATS configuration.
The candidates the filter would have cut
Two populations tell the whole story:
- No keywords, high score: 677 agents had no traditional ATS keywords but scored 75 or above on behavioral assessment. They produced at 33.7%, above the overall rate.
- Every keyword present: agents who matched all the standard filters produced at only 26.3%, below the no-keyword high-scored group.
The signal the carrier trusted was inversely related to the outcome it wanted.
Why this stays hidden
The ATS records who you screened out. The HRIS records who produced. Nobody connects the two, so the cost of a filter never appears on a report. When this analysis first ran, the carrier's talent acquisition team was surprised. Several managers had personally required insurance experience for over a decade. Their highest-performing agents were disproportionately the ones without it. The data had been sitting in two systems the whole time, never joined.
What each system can see
| System | What it knows | What it cannot see |
|---|---|---|
| ATS | who you screened and on what | whether they produced |
| HRIS | who produced and how much | what you screened them on |
| Decision trace | both, connected | nothing, that is the point |
Find your own number
The $17.7M is specific to this carrier and this role. The method to find your equivalent is general. Connect your ATS screening rules to HRIS outcomes through a decision trace, then total the production of the people each rule would reject. Most enterprises already hold both datasets. They have simply never put them next to each other.
Frequently asked questions
How much can one bad hiring filter cost? In this study, requiring insurance experience would have rejected 2,863 agents who produced, representing $17.7M in annual premium credit.
How was the $17.7M calculated? It is the sum of observed annual premium credit across the 2,863 producing agents who lacked the insurance-experience flag and would have been excluded by keyword-based filtering.
Do candidates with no keywords perform worse? Not here. Agents with no traditional keywords who scored 75 or above produced at 33.7%, above the overall rate, while agents with every keyword present produced at only 26.3%.
How would we find our own version of this number? Connect your ATS screening rules to HRIS outcomes through a decision trace, then add up the production of the people each rule would screen out.
Related reading
- What 10,765 hires revealed about resume keywords
- Why ATS keywords fail to predict performance
- Decision traces, explained
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