Claims Processing Agent - Freelance Ai Trainer

Mindrift; Philippines; On-site; PHP 1,056,000 - 1,265,000; Full time

Job summary

Mindrift is seeking a Claims Processing Agent - Freelance AI Trainer in the Philippines. The role involves evaluating AI-generated auto insurance claims decisions for accuracy, coverage, and regulatory compliance, and designing FNOL scenarios to test agent robustness.

Contributors will also build fraud-flagging and subrogation test cases, and document test cases with correct answers, policy citations, and payout calculations.

Qualifications

• Degree in Insurance, Risk Management, Business Administration, Finance, Law, or related field.
• 3+ years of insurance, claims, legal, or financial services experience.
• Strong written English (C1+).

Responsibilities

• Evaluate AI-generated auto insurance claims decisions for accuracy, coverage correctness, and regulatory compliance.
• Design FNOL scenarios with deliberate contradictions, decoy files, and outdated documents to test agent robustness.
• Write and grade fraud-flagging scenarios using structured reason codes for SIU referrals.
• Build subrogation test cases applying state-specific negligence rules and assess likelihood of recovery.
• Document test cases clearly with correct answers, policy citations, and payout calculations.

Skills

Strong written English

Education

Insurance degree

Professional credentials (AIC/CPCU)

Job description

Claims Processing Agent - Freelance AI Trainer

Salary: ₱104,000 monthly

Company: Mindrift

Job Type: Full Time

Location: Philippines

Job Description - Claims Processing Agent - Freelance AI Trainer

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• Design FNOL scenarios with deliberate contradictions, decoy files, and outdated documents to test agent robustness;
• 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;
• Document test cases clearly with correct answers, policy citations, and payout calculations.

What we look for

This opportunity is a good fit for professionals with a background in insurance claims, legal services, or broader financial services who are open to part-time, non-permanent projects. Ideally, contributors will have:

• Degree in Insurance, Risk Management, Business Administration, Finance, Law, or any related field;
• 3+ years of insurance, claims, legal, or financial services experience;
• Current or recent experience in claims & adjusting or adjacent roles;
• Familiarity with auto insurance coverage decisions, state-specific negligence rules, and adjuster authority-limit culture;
• AIC, CPCU, CIFI, or SCLA credential is a strong positive signal, though not required if hands-on 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$50 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.