Senior Auto Claims Adjuster - Freelance AI Trainer at Mindrift

Oklahoma City, Oklahoma, United States -

Full Time

Start Date

Immediate

Expiry Date

18 Sep, 26

Salary

60.0

Posted On

20 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Auto Claims Adjusting, AI Evaluation, Coverage Analysis, Fraud Detection, Subrogation, Policy Citation, Payout Calculation, Regulatory Compliance, FNOL Scenario Design, Excel

Industry

technology;Information and Internet

Description

Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. What this opportunity involves While each project involves unique tasks, contributors may: Evaluate AI-generated auto insurance claims decisions for accuracy, coverage correctness, and regulatory compliance; Design realistic FNOL (First Notice of Loss) scenarios with deliberate contradictions, decoy files, and outdated documents to test agent robustness; Create test cases for coverage-scope decisions (collision vs. comprehensive) where the correct answer requires domain knowledge, not keyword matching; Write and grade fraud-flagging scenarios using structured reason codes (late reporting, recently purchased policy, inconsistent damage) for SIU referral; Build subrogation test cases applying state-specific negligence rules (comparative vs. contributory) and assess likelihood of recovery; Develop supervisor-escalation scenarios that test whether the agent correctly recognizes authority-limit thresholds ($25,000) and stops short of auto-approving; Draft and evaluate reservation-of-rights letter scenarios, verifying language stays within the bad-faith line; Validate coverage-limits math when multiple endorsements (OEM, rideshare, extended rental) stack on a single claim; Document test cases clearly with correct answers, policy citations, and payout calculations. What we look for This opportunity is a good fit for mortgage underwriters and loan origination professionals open to part-time, non-permanent projects. Ideally, contributors will have: Degree in Finance, Business, Insurance, or related field — or equivalent professional experience; no specific degree is required if AIC, CPCU, or comparable credentials are present, or if the candidate has 4+ years of hands-on claims adjusting experience; 3+ years of hands-on auto claims adjusting, examining, or supervisory experience at a U.S. carrier, independent adjusting firm, or SIU team; Ability to make coverage decisions (collision vs. comprehensive, endorsement stacking, exclusion vs. coverage grant) without looking them up; Familiarity with U.S. state-specific rules — comparative vs. contributory negligence states, state adjuster licensing requirements; Experience reading full auto policy documents with citation discipline (able to reference a specific section, e.g. "Section IV.B.2"); Comfort computing payout math involving deductibles, sub-limits, and layered endorsements in Excel or equivalent; Awareness of the bad-faith line and adjuster authority-limit culture; Associate in Claims (AIC), CPCU, CIFI, or SCLA credential is a strong positive signal — but not required if experience is solid; Strong written English (C1+). How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Compensation On this project, contributors can earn up to $60 per hour equivalent, depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

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Responsibilities

Evaluate AI-generated auto insurance claims decisions for accuracy and regulatory compliance. Design complex test cases and scenarios to improve the robustness of AI agents in claims processing.