Industries · Insurance
Banao builds and deploys AI across the insurance workflow — first-notice-of-loss triage, claims document extraction, fraud signals, and underwriting decision support — for general, health, and life insurers and the TPAs that serve them.
Every system below runs against live policy and claims data, wired into your policy admin and claims systems. We ship deployed software, not a proof of concept that dies in the sandbox.
A multi-line general insurer— first-notice-of-loss triage and claims document extraction running on live intake.
Each of these maps to a number on your loss ratio or expense ratio — claims leakage, fraud, cycle time, or manual data entry. We start where the cost is measurable.
Models that read FNOL forms, medical bills, police reports, and repair estimates out of PDFs and images into your claims system — structured, validated, and ready for an adjuster.
Pattern and network models that score claims for fraud at intake, surfacing the suspicious few percent for investigation instead of forcing manual review of every file.
Risk models over application, third-party, and historical data that give underwriters a defensible score and the reasons behind it — straight-through for clean risks, escalation for the rest.
Computer vision on motor and property photos that estimates damage and flags likely total losses, so first estimates don't wait on a field surveyor's calendar.
Extraction and comparison across policy wordings, endorsements, and submissions so service teams stop reading 40-page documents to answer one question.
Models over policy, payment, and service history that flag at-risk renewals early enough for retention teams to act, not after the lapse.
These deployments are live; the named insurers are under NDA, so receipts here are described by line of business. Metrics shown dotted (··) are being finalised in our case-study metrics pack — we will not publish a number before it is verified.
A multi-line general insurer
The insurer's claims team keyed every FNOL by hand from email, PDF, and call notes before an adjuster could even look. Banao deployed extraction and triage at intake — documents read, claims classified by severity, and clean records pushed into the claims system, with anything ambiguous routed to a human.
A health insurance TPA
Pre-authorisation and reimbursement packets arrived as scanned bundles that staff sorted and typed page by page. Banao built extraction tuned to hospital bill and discharge formats, cutting the manual sort and keeping a reviewer on the exceptions rather than the whole stack.
Banao operates a ~300-person engineering company on its own AI products before any client sees them. InterviewGod screens our own hires. Vikaas runs our own demand generation.
An insurer's stakes are higher than ours, but the discipline is the same: a model we depend on daily reaches you already hardened against the edge cases a demo never shows. We sell what we are willing to run our own payroll and pipeline on.
Most AI vendors will sell you a model regardless. We would rather tell you when not to build — it is why claims and underwriting heads take our second call.
You have been pitched insurance AI by every core-system vendor already. We start by proving the cost of the problem, not by quoting a build.
On-site or remote. You walk out with a prioritised list of AI opportunities across claims, underwriting, and servicing, baseline loss- and expense-ratio maths, and a go/no-go per opportunity — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
Data engineering first, then the model. We build the extraction and cleaning pipeline as a deliverable and integrate with your policy admin, claims, and core systems — legacy platforms included.
Deployment with adjuster and underwriter override, an audit trail for the regulator, and change management for your operations team. The model keeps improving as each month's claims close.
Our policy admin and claims systems are decades old. Does that rule us out?
No — integration with legacy core systems is routine for us. Banao connects to mainframe and on-prem claims platforms via file drops, APIs, or screen-level integration. The model cares about the data, not the age of the platform, and we run an integration audit in week one.
We can't send claims or policy data to a third party. How does that work?
We deploy inside your environment — your cloud tenant or on-prem — so policy and claims data never leaves your control. Models can be trained and run where the data already lives, with access logging for audit.
Regulators expect us to explain every decision. Can AI do that?
For underwriting and claims we use models that produce a reason code, not just a score, and we keep the human as the decision-maker with a full audit trail. The point is to speed up the obvious cases and route the rest to a person, not to hand decisions to a black box.
How do we prove ROI before committing budget?
That is what the AI Discovery Sprint produces — fixed price, two weeks, you keep the loss- and expense-ratio model whether or not you continue. Worst case you have a free assessment; best case you have your board business case.
How fast can a system reach live claims?
A typical path is a 2-week Sprint, a 6–8 week build, and a 4-week production rollout against live intake. Banao's ~300-engineer bench means delivery starts in weeks, not the months a local hire would take.
Bring your worst source of claims leakage, fraud, or underwriting delay. In 45 minutes we'll map the AI opportunity and the loss-ratio maths behind it.
Book a 45-min scoping call
