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Insurance · Underwriting automation

Your underwriters spend the week reading submissions, not deciding risks

Banao builds AI underwriting decision support that reads the submission, pulls third-party risk data, scores the risk against your appetite, and routes it — straight-through for clean risks within your defined limits, escalation with structured reason codes for everything else.

The underwriter sees a prepared risk file, not a stack of raw broker documents. The model runs on your data, in your environment, integrated with your policy admin and rating systems.

A commercial lines insurer— risk scoring and submission triage deployed against live new-business intake.

What a Banao underwriting deployment includes

Underwriting automation is not a single model. It is ingestion, enrichment, scoring, routing, and the workbench that surfaces all of it to the underwriter — we build the whole chain.

Submission ingestion and extraction

Models that read ACORD forms, broker submissions, application PDFs, and supplementals into structured fields — correctly classified, cross-validated, and ready for scoring without manual keying.

Third-party data enrichment

Automated pulls from credit bureaus, property data, fleet registries, and claims history. The underwriter gets a single enriched file, not seven browser tabs open in parallel.

Risk scoring with reason codes

A score against your appetite and guidelines, with the top factors behind it. Defensible to the regulator and to the broker, not a black-box number that no-one can explain.

Straight-through processing

Clean risks within your defined authority limits bind automatically. Complex or out-of-appetite risks route to the right desk with a summary, so senior underwriters spend time on genuinely difficult decisions.

Appetite and referral rules engine

Your underwriting guidelines encoded as rules that work alongside the model. When your appetite changes, you update the rules — the model does not need retraining for every guideline shift.

Underwriting workbench

A structured view for escalated risks: enriched data, score, reason codes, comparable historical risks, and override controls. Designed for underwriters, not data scientists.

Where this is running

Named insurers are under NDA. Receipts are described by line of business. Metrics shown dotted (··) are being verified in our case-study metrics pack — we publish only once a number has been confirmed.

A commercial lines insurer

Submission triage and risk scoring on live new-business intake

  • ··%submissions auto-routed at intake
  • ··daysoff average underwriting cycle time
  • ··%manual data keying removed

The underwriting team spent the first day of every submission reading and keying broker documents before a decision could start. Banao deployed extraction and scoring at intake — submissions classified, third-party data pulled, risks scored against appetite, and clean risks routed to bind without touching an underwriter's queue.

A health and life insurer

Automated underwriting for individual and group new business

  • ··%applications straight-through processed
  • ··×faster time to quote

Paramedical and financial underwriting rules were encoded alongside a risk-scoring model for new individual and group applications. Standard applications bind without an underwriter; non-standard applications arrive with a structured summary that cuts the manual review from hours to minutes.

We run our own company on the AI we sell

Banao operates a ~300-person engineering company on its own AI products before any client sees them. InterviewGod scores and routes every engineering hire we make. Vikaas runs our own sales pipeline.

Scoring and routing is what we ask these systems to do for our own operation daily — so the underwriting automation we build for insurers is tested against the discipline of running it on real decisions with real consequences. We know what a production scoring model has to survive.

  • InterviewGodScores and routes Banao's own engineering hires every week.
  • VikaasRuns Banao's own demand-gen and sales pipeline end to end.

When underwriting automation is the wrong fit

Not every underwriting operation is ready for AI. We would rather identify that in week one than after a build:

  • Novel or first-of-a-kind risks: a model learns from closed-book patterns. If your book is genuinely new territory with no historical comparables, a rules-only approach is more honest than a model with thin signal.
  • Appetite that changes monthly: if your underwriting guidelines shift every quarter, a static scoring model becomes a liability before it pays back. That needs a rules-first design with AI in a supporting role.
  • No usable loss history: risk scoring requires labelled outcomes. If prior submissions were never logged with decisions and results, week one is building that record — not scoring against it.

How we start — fixed-price, no surprises

Most insurers have already sat through three vendor demos. We start by costing the problem, not by selling the solution.

  1. AI Discovery Sprint2 weeks · fixed price

    On-site or remote. We audit a sample of your real submissions, map the underwriting workflow, and hand back a prioritised list of automation opportunities, straight-through processing rate estimates, and cycle-time maths — yours to keep. If you proceed, the Sprint cost is credited against the build.

  2. Build

    Data engineering first: submission ingestion, third-party integration, and the scoring pipeline as a delivered artefact. The model trains on your closed-book data and is integrated with your policy admin, rating, and workflow systems.

  3. Production & continuous improvement

    Live deployment with underwriter override and a full audit trail. Override decisions feed back to improve the model each month as more risks close. Change management for your underwriting team is part of the deliverable.

Frequently asked questions

Yes, though the approach differs. Personal lines suit high-volume scoring models. Complex commercial and specialty risks suit a hybrid — rules encoding your appetite plus AI-driven data enrichment and triage, with underwriters retained for genuinely complex decisions. The Discovery Sprint establishes which model fits your lines of business.

Every decision the model makes produces a structured reason code that ties back to specific risk factors — not a black-box score. The underwriter remains the decision-maker with an audit trail for the regulator. Auto-bound risks are within limits you define and can be reviewed at any time.

Legacy system integration is routine for us. Banao connects to on-prem and mainframe policy admin platforms via file exchange, APIs, or screen-level integration where necessary. The model cares about data quality, not the age of the platform — and we run an integration audit as part of the Sprint.

Appetite and referral rules are encoded separately from the scoring model, so guideline changes — revised authority limits, new classes excluded or added — are applied as rule updates, not model retraining. The scoring model retrains on a cycle as your closed-book data grows.

A standard path is a 2-week Sprint, a 6–10 week build depending on systems complexity, and a staged rollout starting with shadow-scoring against live submissions before the model routes anything automatically. Banao's engineering bench means the build starts within weeks of sign-off, not months.

Show us your worst submission backlog

Bring your most time-consuming submission type and your current cycle time. In 45 minutes we will map which part of the underwriting workflow AI can cut first — and what the straight-through rate could be.

Book a 45-min scoping call