AI Hiring Intelligence for Insurance Carriers
Insurance carriers hire agents at high volume and lose most of them early. By LIMRA's research, only about 15% of full-time financial professionals were still with their hiring company after four years, with most departures in years one and two. NODES helps carriers predict which agents will actually produce, using the carrier's own ATS, HRIS, and assessment data rather than resume keywords. The approach is backed by a published study of 10,765 insurance agent hires.
Source: "Decision Traces," Saad Bin Shafiq, NODES, 2026, N=10,765 agents at a Fortune 500 insurance carrier. Retention figures from LIMRA. Read the study on arXiv.
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The insurance hiring problem
Carriers recruit agents constantly, and most never reach steady production. LIMRA's research puts four-year retention near 15%, with the heaviest losses in years one and two. The same research points to selection quality and a fast start as the largest drivers of early retention, which is exactly where most carriers have the least objective data.
What the research found in insurance specifically
This study was conducted in insurance, so the findings map directly to the carrier hiring problem:
- Resume keywords did not predict production. Of 3,597 tested keywords, none survived correction, and prior-experience signals were anti-predictive.
- Requiring insurance experience as a filter would have rejected 2,863 agents who produced, representing $17.7M in annual premium credit. Details.
- Personality assessment was the strongest single predictor, and fusion reached an AUC of 0.735 on the evaluable sample.
- Speed to production was worth about $54 per agent per day, and high-scored agents captured roughly 2.8 times more value from a fast ramp. Details.
How NODES works for carriers
NODES connects your ATS, HRIS, and assessment data, builds a decision trace for each agent, and learns which signals predicted production in your own book of business. It then scores candidates against those signals, with an explanation for each, and runs entirely inside your VPC. The deployed score works mainly as a moderator of who converts a fast ramp into production, and the model ingests no demographic data.
Built for regulated insurance
SOC 2 Type II, single-tenant VPC, zero data egress, no third-party model calls, audit trails, and no demographic data in the model. NODES integrates with the ATS and HRIS systems carriers already run. See VPC deployment.
Frequently asked questions
Why is insurance agent turnover so high? Selection and early activity are the biggest drivers. LIMRA research finds most terminations happen in years one and two, and only about 15% of agents remain after four years.
Does requiring insurance experience improve hiring? In a study of 10,765 agents, no. Insurance experience was anti-predictive, and requiring it would have rejected 2,863 agents who produced $17.7M.
What does NODES predict for carriers? Which candidates are most likely to produce, and who will convert a fast ramp into production, based on the carrier's own outcomes.
Is candidate data safe? Yes. NODES runs inside the carrier's own VPC with SOC 2 Type II controls and no data egress.
Related reading
- The $17.7M cost of a single hiring filter
- The speed-to-production constant
- VPC-deployed AI hiring with zero data egress
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