$75/hr
The role in one line
An insurance professional who audits AI system outputs to verify they handle real insurance work correctly.
Written by Training Turk from the public listing; it may be incomplete or out of date. Read the full posting on Mercor.
What you would do
- Develop realistic scenarios based on your actual daily insurance work
- Review AI-generated insurance decisions for technical and regulatory accuracy
- Judge whether AI output meets industry standards and legal requirements
- Provide detailed written explanations of where AI succeeds or fails
- Flag plausible-but-incorrect outputs that a non-expert might accept
Who they are looking for
- 3+ years in underwriting, claims, actuarial, or related insurance practice
- CPCU, ARM, AINS, CLU, AIC, ACAS, FCAS, ASA, or FSA credential preferred
- Comfortable with policy administration and claims software platforms
- Strong written communication and attention to detail
- Ability to spot domain-specific errors that outsiders would miss
Skills this role asks for
What the interview is likely to probe
1.Coverage analysis and contract interpretation
Evaluators check whether you can identify subtle coverage gaps or policy language misreadings that AI systems produce, which would expose insurers to liability.
Expect something like: “You review an AI claim decision that cites a specific exclusion. The text is clear, but you notice the exclusion was amended by endorsement. How would you evaluate whether AI used the current language?”
2.Actuarial basis and assumption validation
Interviewers verify you can spot when AI applied the right formula but used wrong assumptions or input figures, a common failure mode in forecasting work.
Expect something like: “An AI-generated calculation uses the correct formula but applies previous quarter's data instead of the twelve-month rolling average. Would you assess this as a systematic error?”
3.Regulatory compliance recognition
Evaluators test whether you catch violations of state licensing, fair lending, or disclosure standards that AI systems might violate without knowing the rules.
Expect something like: “You review an AI underwriting decision that correctly evaluates risk factors but fails to document the consideration of a state-mandated protected class. How would you flag this gap?”
4.Task realism and grading accuracy
Interviewers want to know whether test scenarios match your actual work and whether grading criteria would identify genuinely competent insurance professionals.
Expect something like: “A task asks AI to reconcile a reserve estimate against a claims file, but the scenario provides incomplete claim detail. Would you mark this as realistic?”
5.Error diagnosis and feedback specificity
Evaluators assess your ability to pinpoint why AI failed, not just that it failed, so improvement can be targeted and not just general retraining.
Expect something like: “An AI output is marked wrong for incorrect reserve amount, but the error stems from misinterpreting premium finance terms. How would you distinguish calculation error from conceptual misunderstanding?”
Exercise you may get
Review a complete AI underwriting or claims decision against the policy document and regulations it must follow, then identify which parts are correct, which violate rules, and what corrections the AI should make.
How to prepare
- Review your domain's regulatory requirements and common compliance traps
- Gather examples of recent underwriting, claims, or actuarial decisions you handled to use as reference scenarios
- Review what errors AI systems commonly make in insurance scenarios from public research
- Practice writing concise, specific feedback that targets root causes rather than just labeling outputs wrong
Facts
- Pay
- $75/hr
- Commitment
- hourly
- Work arrangement
- remote · Remote
- Eligible locations
- USA
- Posted
- 9/16/2026