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Industries · Insurance · Policy Document Processing

Your underwriters are reading documents. They should be underwriting.

Policy wordings, endorsements, riders, and submissions arrive as dense PDFs that nobody has time to read end to end. Banao deploys extraction and comparison AI that pulls the coverage terms, exclusions, and conditions your team actually needs — structured, searchable, and ready for a decision.

The same extraction that runs against new submissions at intake can diff policy wordings on renewal and flag coverage gaps in seconds. Service teams stop spending the first eight minutes of every call finding the right endorsement clause.

A commercial lines insurer— policy wording extraction running on submission intake and renewal comparison.

What we deploy for policy document processing

Each capability maps to a specific cost in your operation — adjuster time, underwriter time, or service call length. We start where the time spent is easiest to measure.

Wording extraction and structuring

Models that read policy PDFs and pull coverage limits, exclusions, conditions, and definitions into a clean structured record — so your systems hold the policy data, not just the document.

Submission document processing

Reads new-business submissions and spreads the key risk data — property details, limits, prior claims, industry codes — directly into your underwriting system, without a human rekeying a spreadsheet.

Policy comparison on renewal

Diff-maps expiring and incoming wordings to surface coverage changes, added exclusions, and limit shifts before bind. Underwriters see what actually changed, not a 40-page document they have read before.

Endorsement and rider parsing

Reads mid-term endorsements and keeps a running structured view of what a policy currently covers — so service teams and adjusters do not need to reconstruct coverage history from a folder of PDFs.

Service-team query answering

Routes policyholder and broker questions to the correct clause, sublimit, or condition without a human reading through the whole wording. Typical service call first-response time drops when staff stop searching documents manually.

Coverage gap and duplication flagging

Where a client holds multiple policies in a programme, models map the tower for gaps and overlap — so account managers can have an informed renewal conversation instead of discovering a gap at claim.

Deployed on live policy operations

These deployments are live; named carriers are under NDA. Metrics shown dotted (··) are being finalised in our case-study metrics pack — we publish numbers only after verification with the client.

A mid-market commercial lines insurer

Policy wording extraction and renewal comparison on live submissions

  • ··%of submission documents auto-extracted
  • ··mincut from per-submission review time
  • ··%coverage changes surfaced automatically on renewal

Renewal underwriters reviewed expiring and incoming wordings side by side, manually noting coverage changes in a Word document before each bind decision. Banao deployed extraction and comparison against the submission pack — expiring wording, new wording, and any mid-term endorsements — and delivered a structured diff that showed only what changed, with clause-level references. The team now reviews the diff, not the documents.

We run our own operation on the AI we sell

Banao operates a ~300-person engineering company on its own AI products before any client sees them. InterviewGod screens our own engineering hires. Vikaas runs our own demand-generation pipeline.

Document extraction and NLP are not new for us — we have built extraction pipelines against financial, government, and operational documents at scale. When we deploy policy document processing for an insurer, the edge cases that surface in the first week of production are ones we have already seen and handled.

  • InterviewGodScreens Banao's own engineering candidates every week.
  • VikaasRuns Banao's own demand-gen pipeline end to end.

When policy document AI does not earn its keep

Document extraction works well when the problem is volume, not judgement. We will tell you when not to build before we quote a solution.

  • Highly non-standard wordings: if your policies are bespoke manuscript forms that change structure with every placement, model accuracy needs more tuning time — the Sprint will surface this before a build is scoped.
  • Very low document volume: below a few dozen policies a month, a trained analyst reviews documents faster than a pipeline pays for itself. We will say so.
  • Full automation with no human override: document AI works best as a first-pass that puts the right information in front of an underwriter or service agent. Removing the human entirely in a regulated environment is a compliance question your legal team must answer, not a technology one.

How we start — fixed-price, low risk

You have seen demos that make document processing look easy. We start by showing you what the models can and cannot read on your actual policy stock.

  1. AI Discovery Sprint2 weeks · fixed price

    On-site or remote. Bring a sample of your messiest document types — scanned PDFs, bespoke wordings, multi-section endorsements. You walk out with extraction accuracy benchmarks on your own documents, a prioritised list of workflows worth automating, and a go/no-go recommendation — yours to keep either way. If you proceed, the Sprint cost is credited against the build.

  2. Build

    Data engineering first, then the extraction and comparison models. We build integration with your policy admin or document management system and deliver the extraction pipeline as a tested, documented deliverable — not a notebook.

  3. Production and continuous improvement

    Deployment with a human-review queue for low-confidence extractions, an audit log for the regulator, and model retraining as new wording formats arrive. Accuracy improves as the correction queue feeds back into training.

Frequently asked questions

Non-standard wordings need tuning — the Discovery Sprint benchmarks accuracy on your actual document types before we scope a build. Frequently changing wordings are manageable if changes are structural (new clauses, limit tables); models can be retrained. If the wording structure changes with every placement, we will tell you in the Sprint rather than build something that rots in six months.

Yes, but it is handled. Native digital PDFs give higher extraction accuracy. Scanned documents need OCR as a first step — we have built OCR pipelines against insurance documents at multiple quality levels and can benchmark your scan quality in week one. Most operations have a mix, and the models handle both.

The extraction pipeline reads the base wording and each endorsement in date order, maintaining a current-state view of what the policy covers today — not just what the original document said. Superseded clauses are retained in history for claims reference.

Two things the build cannot start without: extraction accuracy benchmarks on your actual document types (not a vendor demo set), and a time-per-document baseline for the workflows you want to automate. These determine whether the build ROI is real before any money is committed to it.

We integrate with your policy admin via API, file-based exchange, or direct database write depending on what the platform supports. Banao has integrated with legacy on-prem policy admin platforms and modern cloud systems — the extraction pipeline produces structured output in whatever format the downstream system expects.

Find out how much of your document handling can be removed

Bring a sample of your most time-consuming policy type. In 45 minutes we will show you what extraction can do on your actual documents and what it cannot.

Book a 45-min scoping call