Insurance AI data, labeled by people who know claims.
Insurance models read policies, assess damage and explain decisions to customers. Each step needs data labeled by people who understand coverage, and handled with care for the people in it.
Insurance, with proof.
Insurers and insurtech teams apply models to policy comparison, claims intake, document extraction, damage assessment and customer correspondence. The failure modes are specific. A model that misreads an exclusion, overlooks a sublimit or misjudges damage severity creates leakage, disputes and unfair outcomes. HUMXN commissions credential-checked claims specialists, underwriters, adjusters and legal experts to label documents, annotate images and grade model answers against your rubric. You bring the line of business and the task. We scope the dataset and deliver a pilot batch first.
Damage imagery is a good example of where provenance matters. A training set for vehicle or property assessment needs real photographs, honestly labeled, and it needs to exclude manipulated or AI-generated images that would teach the wrong signal. Every image is fingerprinted at intake with crop-tolerant perceptual hashes and checked against everything registered. Generator signatures in EXIF and XMP, IPTC digital-source-type fields and C2PA records are detected. Each work records whether it is human-created, AI-assisted or AI-generated.
Claims files are full of personal data. Documents and tables are scanned for personal information and masked before delivery, and location and personal metadata never reach buyers. Photographs showing identifiable people are offered for training only with consent on file, and property releases are bound to each item where relevant. Every delivery carries signed provenance and a receipt by ID and hash, designed to support the fairness and governance reviews your teams carry out on automated decisions.
Built to your specification.
Every engagement starts from a written spec and a pilot batch. These are the most common requests we source for insurance.
Policy wording annotation
Coverage grants, exclusions, conditions, limits and sublimits labeled across policy forms by underwriting and legal specialists.
Claims document extraction
Structured labels from claim forms, estimates and correspondence, with personal data scanned and masked before delivery.
Damage imagery with labels
Vehicle and property damage photographs annotated for parts, damage type and severity, with releases and human-origin records.
Graded coverage answers
Expert grading of model answers about what a policy covers and why, checked against the wording and with written rationales.
Customer correspondence demonstrations
Specialist-written explanations of decisions for fictional claims, clear and accurate, for assistants that talk to policyholders.
Fraud-pattern review sets
Expert-reviewed examples for evaluating detection models, with labels marked by source and contested calls sent to a second reviewer.
01Misread coverage
Exclusions and sublimits change outcomes. Specialist annotation teaches models to read wording the way an underwriter does.
02Manipulated imagery
Edited or generated damage photos poison assessment models. Fingerprinting and generator-signature checks screen them at intake.
03Policyholder privacy
Claims documents are scanned for personal data and masked, and consent is required for imagery showing identifiable people.
04Explainable decisions
Fairness reviews ask what a model learned from. Signed provenance and receipts give you a traceable answer.
Insurance: asked often.
Can you source damage photographs for training?
Yes, from consenting creators in our catalogue and through commissioned work, against a specification of vehicle types, property types and damage categories. Each photograph is fingerprinted, checked for duplicates and screened for AI-generation signals at intake. Images showing identifiable people require consent on file, and location metadata never reaches buyers.
How do you protect policyholder data?
Documents and tables are scanned for personal data and masked before delivery, and personal metadata is stripped. Many insurance datasets use realistic fictional claims written by specialists, which avoids real policyholder data entirely. Your privacy team should still assess the dataset against its obligations; we provide the records it needs.
Who labels insurance documents?
Credential-checked specialists matched to the task: underwriters and insurance attorneys for policy wording, adjusters for claims, appraisers and surveyors for damage imagery. Labels record whether they came from a creator, an AI suggestion or expert review, and contested items go to a second expert.
Will this help with fairness and regulatory reviews?
Signed provenance, a hash-chained audit trail and per-item receipts give you a clear record of what a model was trained and tested on. That is designed to support your governance and fairness work. It is not a guarantee of compliance with any regulation, and your compliance team should make that assessment.
How is insurance data priced and delivered?
Pricing follows task complexity, specialist type, time per item, review depth and licence; volume and standing orders reduce the unit price. Delivery is in JSON Lines, CSV or Parquet for labels and tables, COCO captions for images, and WebDataset for larger image sets, with a signed receipt.
Tell us what your model needs to learn.
Send a brief in five minutes. A data lead replies within one business day with questions and a first sourcing plan.