Every closed claim is a worked example with the answer attached.
A claim file records what the adjuster saw, what they decided, and what it cost. That is training data with the outcome already written down. We turn claims notes, photos, underwriting files and call recordings into datasets and evaluations, inside your tenant, with the documentation your AI governance program will be asked for.
What carriers and lenders already keep
A property carrier's claims system holds years of first-notice-of-loss calls, adjuster diary notes, photos, estimates, reserve changes, engineer and independent-adjuster reports, coverage letters and litigation referrals. Underwriting holds applications, loss runs, inspection reports and long email threads with brokers. Banks and credit unions have loan files, credit memos, complaint logs, KYC documents and call recordings.
What makes this data valuable is that most of it ends in a recorded outcome: paid, denied, reopened, litigated, subrogated. A model learning to summarize or triage claims can be checked against what actually happened.
What makes it hard is that the records are uneven. After a Gulf hurricane, claim volume can multiply for weeks, and the notes written under surge conditions are shorter, more abbreviated and less consistent than calm-season notes. A model trained only on the calm months misreads exactly the files that matter most. We sample by season and by event so the surge is in the training set and in the test.
Where insurance and banking AI projects start
The use cases we see most often, and the service that carries each.
Claims-file summaries and Q&A
Parse claim files (notes, estimates, photos, correspondence, engineer reports) into a retrieval index, so a desk adjuster can ask a question and get an answer with citations to the page.
Adjuster preference data
Senior adjusters compare paired summaries against a rubric: coverage-relevant facts, cause of loss, timeline, open items. Their judgments become the preference data that tunes the model.
Damage photo annotation
Experienced field adjusters label roof damage, water lines and debris in claim photos, with a rubric for the ambiguous ones. This is image annotation by people who have stood on the roof.
Triage and routing models
Severity, complexity, and special-investigation or litigation referral labels drawn from recorded outcomes, cleaned and balanced into a fine-tuning set with a held-out test.
Underwriting submission intake
Extract fields from applications, loss runs and inspection reports at volume, inside your cloud, with confidence scores and a review queue for the fields that matter to pricing.
Release gates and fairness testing
A golden set that includes surge-season files, a calibrated judge, and checks on outcomes across geography and other proxies your governance team specifies, wired into your deployment pipeline.
What you have, and what it becomes
| What you have | What it becomes | Where the work is |
|---|---|---|
| Adjuster diary notes on closed claims | Summary training pairs and a cause-of-loss classifier, labeled from outcomes and checked by adjusters | Clean: system-generated entries and templates are often a large share of all notes |
| Claim photos and estimates | Annotated damage images linked to estimate line items | Label: rubric for partial and pre-existing damage |
| First-notice-of-loss calls | Transcripts with structured loss details, and an evaluation set for intake assistants | Parse and Protect: transcription, then redaction of names, addresses and policy numbers |
| Underwriting submissions and loss runs | Extracted fields and a retrieval index over guidelines and prior decisions | Parse: broker formats vary by agency and by year |
| Complaint logs and customer correspondence | Labeled complaint categories and a test set for response-drafting assistants | Protect: account numbers and nonpublic personal information |
| Overrides of model recommendations | Preference data and drift signals for the next model version | Prove: monthly sampling of production decisions |
The rules your AI governance program answers to
NAIC members adopted the Model Bulletin on the Use of Artificial Intelligence Systems by Insurers on December 4, 2023. It expects a written AIS Program scaled to risk, governance and risk-management controls, oversight of AI used on the insurer's behalf, and documentation a department of insurance may request in an examination. As of October 2026, 29 jurisdictions had adopted it, including Mississippi on July 22, 2026. California, Colorado, New York and Texas have their own insurance-specific rules or guidance; New York's Circular Letter No. 7 expects unfair-discrimination testing before an AI system goes into production and keeps insurers responsible for vendor tools.
For financial institutions under the FTC Safeguards Rule, service providers must be overseen by contract, and a security event involving 500 or more consumers must be reported to the FTC within 30 days of discovery. Banks and credit unions answer to their own GLBA safeguards guidance and model-risk expectations.
What we deliver supports those programs: datasheets, lineage manifests, test results and reviewer records for every dataset. Whether they satisfy a particular rule is for your compliance team and counsel to decide. Benner House is not a law firm and does not provide legal advice.
Questions from carriers and lenders
Can you work with our claims system?
We work from exports or read-only replicas of whatever claims, policy or loan system you run, inside your tenant. The first week of most engagements is spent mapping its tables, diary-note conventions and system-generated entries.
How do you handle catastrophe seasons in the data?
We tag every claim by event and season, sample the surge deliberately rather than in proportion to calm months, and keep surge-season claims in the test set. A model that only works in June isn't ready for September.
Do your reviewers have claims and underwriting experience?
Yes. Reviewers come from our expert network of licensed adjusters, underwriters and examiners, matched to the line of business, and they work to a written rubric with hidden gold questions.
Will your documentation satisfy the NAIC bulletin?
It's built to support an AIS Program: what data trained the system, how it was tested, who reviewed it, and what changed between versions. Whether your program meets a particular state's expectations is a judgment for your compliance team and counsel.
Do you work with banks and credit unions?
Yes. Loan files, complaint logs, call recordings and KYC documents raise the same parsing, redaction and review work, with GLBA safeguards and your vendor-management process shaping how we get access.
Related services and reading
Bring us one messy data source and one question.
Tell us what the data is and what you want a model to do with it. A data engineer, not a salesperson, will reply within one business day with questions and a proposed first step.