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Insurance · Claims automation

Every straight-through claim still touches an adjuster because intake is still manual

Banao builds claims automation that takes a claim from first notice to an adjuster assignment decision without a human touching the file — FNOL intake, document extraction, severity classification, and reserve estimation, running in sequence.

The system connects to your policy admin and claims management platform and handles every document type that arrives at intake: mobile photo, PDF, email, scanned form. Straight-through claims close without adjuster intervention; complex ones reach the right adjuster with the file already built.

What a Banao claims automation deployment includes

Claims automation is not one model. It is intake, extraction, classification, routing, and the adjuster workflow around them — we own the full chain.

FNOL intake from every channel

Claims arriving by email, mobile photo, PDF upload, and inbound call transcript are read, parsed, and structured into your claims system automatically — no manual keying between intake and the first adjuster action.

Document extraction on real claims documents

Medical bills, police reports, repair estimates, surveyor photos, and discharge summaries read field-by-field into structured records — handling the mixed quality and format variation that real claims documents arrive in.

Severity classification and routing

Each claim is classified by type, severity, and estimated complexity at intake, then routed to the right handler — straight-through for clear, low-value cases; specialist adjuster queues for complex ones.

Reserve estimation support

Initial reserve estimates drawn from the intake data, historical payment patterns on similar claims, and policy limits — giving the claims manager a figure to book rather than a blank to fill manually after the adjuster's first call.

Adjuster handoff pack

Every complex claim reaches its adjuster with a pre-built file: structured intake data, extracted document fields, coverage check against the policy, and a reserve recommendation — so the adjuster applies judgment, not data entry.

Audit trail and regulatory log

Every intake event, extraction result, classification decision, and routing call is logged with a timestamp and model version — so the compliance team produces a regulator-ready trail in minutes, not a document reconstruction.

Where this is already running

Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified. Insurers in this vertical are described without naming where contracts require.

A regional motor insurer

Intake assembly automated across motor claims

  • ··%claims auto-classified at intake
  • ··hrsoff average first-response time
  • ··%manual data entry removed

Motor claims arrived as a mix of mobile-app photos, WhatsApp images, and emailed repair estimates from approved workshops. Each file was assembled by a claims handler before an adjuster could assess it. Banao automated the intake assembly — photos extracted and tagged, repair estimates read and line-itemed, coverage checked against the policy — so the adjuster receives a structured file, not an inbox.

A health insurer

Pre-authorisation extraction removed the manual sort

  • ··%pre-auth documents auto-processed
  • ··×processing throughput

Pre-authorisation packs arrived as multi-page scanned bundles — hospital letters, pathology reports, pharmacy invoices — and a clerk read each page to extract the required fields. Banao trained extraction models on the document types this insurer receives most, then automated extraction and validation so clerks only review the pages the model flagged as ambiguous.

We run document extraction and routing on our own operation first

Banao manages a ~300-person engineering operation. Every inbound contract, invoice, and candidate document that passes through our own intake is processed by the same kind of extraction and routing AI we deploy for insurers — not a separate demo environment.

InterviewGod handles candidate documentation and structured assessment routing for our own engineering hires every week. Vikaas manages our outbound pipeline. The systems we run our own business on are the systems we hold your claims automation to — same standard, same accountability.

  • InterviewGodRoutes candidate documentation and assessment intake for Banao's own engineering hires every week.
  • VikaasManages Banao's own outbound demand-gen pipeline end to end.

When claims automation doesn't pay its build cost

Not every claims operation is ready for automation — and we will say so before you commit budget:

  • Thin claims volume: below roughly 200–300 claims a month, hiring or training another adjuster costs less than an automation pipeline and its ongoing maintenance. We will tell you where your volume sits.
  • High claim complexity: automation earns its keep on the high-volume, lower-complexity end of the mix — motor property damage, standard health reimbursements, straightforward liability. Lines where every claim needs specialist judgment are a smaller opportunity.
  • Digitised records are sparse: extraction and classification models need labelled training data from real claims. If your claims history is mostly paper-based or in unstructured notes, the first project is records preparation — not modelling.
  • No integration path into your claims system: if your claims platform cannot accept API calls or file-based input, integration is the first project. We scope this in the Discovery Sprint before any model work starts.

How we start — prove extraction quality before you build

We do not quote a claims automation build off a brief. We read a sample of your real claims documents first.

  1. AI Discovery Sprint2 weeks · fixed price

    We extract a sample of your real claims documents, measure field-level accuracy on your hardest document types, and hand back a feasibility report with an automation-rate estimate and ROI model — yours to keep either way. If you proceed, the Sprint fee is credited against the build.

  2. Build

    Document extraction and classification models trained on your claims types, integration with your policy admin and claims management system, routing rules tuned to your adjuster structure, and an exception-handling queue your claims team will actually use.

  3. Production & continuous learning

    Deployed pipeline with adjuster override, an audit trail for the regulator, and a claims-team feedback loop. Adjuster corrections and outcome data feed back into the model so accuracy improves as each month's claims close.

Frequently asked questions

High-volume, lower-complexity claims where intake documents are relatively consistent — motor property damage, standard health reimbursements, and straightforward liability. The Discovery Sprint maps your claims mix and identifies the automation opportunity within it before any build commitment.

Yes — integration with legacy and modern claims platforms is routine. Banao connects via API, file drop, or database adapter depending on what the platform supports. The model cares about the data, not the age of the system, and we run an integration audit in week one.

Claims the model cannot classify with confidence are flagged and routed to the right adjuster queue with the raw intake data attached — the same outcome manual triage would produce, without the queue delay. The adjuster decision is logged and feeds back into the model.

Every intake event, extraction result, classification call, and routing decision is logged with a timestamp and model version. The audit trail is exportable in the format your compliance team submits to the regulator — built from day one, not retrofitted before an exam.

A typical path is a 2-week Sprint, a 6–8 week build, and a 3–4 week production rollout against live intake. Banao's ~300-engineer bench means the build starts in weeks, not the months a local hire would take.

Bring your highest-volume claim type and your hardest document

In 45 minutes we will tell you whether your intake document quality supports automation — and what the adjuster-hours saving would look like before you commit to a build.

Book a 45-min scoping call