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Healthcare · Claims processing automation

Your claims team is reworking denials that a clean first submission would have avoided

Manual claims processing — coding errors, missing modifiers, eligibility mismatches caught at submission — generates denial rates that claw back a meaningful share of earned revenue and pile rework onto billing staff who are already stretched.

Banao builds claims processing AI that reads clinical documentation, assigns codes, flags payer-rule violations before a claim leaves the building, and routes prior-authorisation requests without a coordinator waiting on a hold queue. The result is a first-pass acceptance rate that moves, not a dashboard that looks good.

What a Banao claims automation deployment covers

Each capability connects directly to a revenue-cycle KPI a CFO or revenue cycle director tracks every month. We start with the one that is bleeding the most today.

Automated medical coding from clinical text

The AI reads clinical notes, operative reports, and discharge summaries and assigns ICD-10, CPT, and HCPCS codes — including modifiers — at a specificity that coders reviewing hundreds of encounters a day cannot consistently maintain.

Pre-submission claim scrubbing

Before a claim leaves your system, the AI checks it against payer-specific rules, bundling edits, and eligibility data. Errors surface inside your billing workflow — not as a denial letter two weeks later.

Prior-authorisation triage and submission

The AI identifies which encounters require PA, pulls the clinical criteria, assembles supporting documentation, and submits to the payer portal. Phone-queue time drops out of the coordinator's day.

Denial pattern analysis and appeal drafting

The system maps denial codes to root causes — wrong payer rule, missing documentation, eligibility lapse — and drafts the appeal letter with the correct clinical evidence attached. Coordinators review and file; they do not write from scratch.

Eligibility and benefit verification at scheduling

Eligibility checks run automatically at the point of scheduling, not billing. Coverage gaps, deductible status, and plan limits surface before the patient arrives — so the conversation happens at the front desk, not in collections.

Revenue-cycle operations dashboard

First-pass acceptance rate, denial rate by payer and code, days in AR, and prior-auth turnaround — the numbers a revenue cycle director and CFO need weekly, extracted automatically rather than pulled from a billing-system report run on Fridays.

Live work in revenue cycle

Metrics shown dotted (··) are being finalised in our case-study pack — we publish only once a number is verified against audited billing data.

A GCC hospital group

Claims routing and prior-auth AI deployed across outpatient and inpatient billing

  • ··%first-pass acceptance rate
  • ·· daysprior-auth turnaround
  • ··%denial rework volume reduced

A multi-site hospital group across the GCC was processing prior-auth requests manually, with coordinators spending hours on payer phone queues each day. Banao built an AI layer over the group's billing system that identifies PA-required encounters, assembles clinical documentation, and submits to the payer portal automatically. The client is named on request under NDA.

We run on our own AI before you run on ours

Banao operates a ~300-person engineering company on the same AI systems it builds for clients. InterviewGod screens our own engineering hires every week. Vikaas runs our own demand-generation pipeline end to end.

In revenue-cycle work, that means the engineers building your claims AI have spent years inside a company that stakes its own operations on what it ships. We know what a production system looks like under real audit scrutiny — because ours has been.

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

When claims automation AI is not the right first move

Most revenue-cycle problems are diagnosable before any model is built. We will give you the straight read:

  • Upstream documentation is the real problem: if denials trace back to physician documentation habits rather than coding error or payer-rule gaps, an AI coding layer treats the symptom, not the cause. A documentation improvement programme is a cheaper first step.
  • Billing system integration is blocked: some legacy billing platforms do not expose a write path. If your system falls into that category, the Discovery Sprint is an integration audit, not a model build — and the right answer may be fixing the data feed first.
  • Low volume: a practice billing fewer than two hundred claims a month rarely earns back a custom AI build on denial prevention alone. A well-configured clearinghouse and a human coder is cheaper — we will say so.
  • Payer rules shift faster than the model can track: in markets where payer rules change every quarter, a static model erodes quickly. We build a continuous rule-refresh cycle in from the start — but if you are not prepared to maintain that, the value will decay.

How we start — audit your denial data before we write a line of code

Revenue-cycle AI has to prove its number before it earns the build. We audit your real denial data and billing patterns first.

  1. AI Discovery Sprint2 weeks · fixed price

    We pull your last six months of denial data, map root causes by payer and code, quantify the recoverable revenue, and assess the integration path into your billing system. You walk out with a prioritised list of AI opportunities and an ROI model — yours to keep whether or not you continue. The Sprint cost is credited against any build.

  2. Build

    Integration into your billing system and practice-management platform first, then the model. We wire into your EHR, clearinghouse, and payer portals — audit logging and role-based access are built in from the first sprint, not added at go-live.

  3. Production & continuous improvement

    Live deployment with a revenue-cycle dashboard, billing team training, and a payer-rule update cycle. The model retrains as payer logic changes, so first-pass acceptance rates hold rather than erode over time.

Frequently asked questions

Banao has integrated with Epic, Oracle Health (Cerner), Athenahealth, and several GCC-region HMIS platforms. Where a direct API path exists, we use it; where it does not, we work through HL7 feeds, clearinghouse APIs, or a database read path. The Discovery Sprint establishes the exact integration surface before any build is scoped.

We build a payer-rule update cycle into the system from day one — the model does not sit static after go-live. Rule changes are ingested, validated against your current claim mix, and deployed on a scheduled cycle. The dashboard tracks first-pass acceptance rate as the leading indicator that a rule has shifted.

The AI handles volume and consistency; coders handle judgment calls, edge cases, and accounts that genuinely need clinical context. Most teams that deploy this move coders from routine coding into denial management and complex case review — where their knowledge actually earns its keep.

Data residency stays in your jurisdiction. Inference runs on-premises or in your cloud tenancy. Encryption at rest and in transit, audit logging, and role-based access are defaults from the first sprint — HIPAA, DISHA, and UAE PDPL controls are not add-ons.

In high-volume settings, a pre-submission scrubbing layer typically shows movement in first-pass acceptance within the first billing cycle after go-live. The Discovery Sprint produces a denial-rate baseline and an ROI model so you have a number to measure against from day one — not a promise to verify six months later.

Find out how much revenue your denial rate is leaving on the table

Bring your last six months of denial data and your top three payer headaches. In 45 minutes we will tell you what the AI opportunity is, what the integration path looks like, and what the recoverable number is.

Book a 45-min scoping call