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Insurance & Claims

Medical Claims Anomaly Detection API

Health claim leakage is rarely dramatic. It is a few extra units on a procedure code, repeated across thousands of claims, by providers who otherwise look entirely normal.

Health insurers
TPAs
Employer health schemes
Public health payers
Claims administrators

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What your customers would see

example interface

MockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.

The problem this solves

Most money lost in health claims is lost quietly. A provider who bills a slightly higher intensity code than their peers for the same presentation, consistently, across four years. A clinic that unbundles a procedure into components that each carry their own fee. The same service submitted twice under marginally different dates. None of it is obviously wrong on a single claim, and claim-by-claim adjudication is not an instrument that can detect it.

The comparison that works is peer-relative. A provider’s billing profile against other providers of the same specialty, treating a comparable case mix, in the same region. That requires enough volume to construct meaningful peer groups and enough care in constructing them that a legitimate sub-specialty is not flagged for behaving like a sub-specialty. Case mix adjustment is the part that separates a usable system from an alert generator.

There is a boundary here that we hold firmly. This identifies billing patterns that warrant review. It does not assess whether treatment was appropriate, necessary or well-chosen — those are clinical judgements that require clinical qualification and access to the full record, and a statistical outlier is not evidence of either fraud or poor care. A provider whose coding differs from their peers may be upcoding, or may be treating sicker patients, or may be coding correctly while their peers code lazily. The output is a question for a reviewer, not an answer.

Who buys this

Health insurers, TPAs, Employer health schemes, Public health payers, Claims administrators

Category: Insurance & Claims

What the API returns

What comes back

Claim-level anomaly scores with the specific billing patterns detected
Provider billing profiles compared against case-mix-adjusted peer groups
Duplicate and near-duplicate claim detection across dates, providers and members
Unbundling and code-fragmentation detection against standard bundling rules
Service frequency and intensity outliers at provider, member and procedure level
Member-level utilisation patterns consistent with card sharing or identity misuse
Prioritised review queue ranked by recoverable amount and evidence strength
Peer group composition disclosed per comparison, so a reviewer can judge its fairness

Endpoints

Method	Path	Returns
POST	/v1/claims/score	Score one claim or a batch for billing anomalies
GET	/v1/providers/{id}/profile	Case-mix-adjusted billing profile against peers
POST	/v1/claims/duplicates	Duplicate and near-duplicate detection across a claim set
GET	/v1/providers/{id}/patterns	Detected pattern detail with peer comparison evidence
GET	/v1/review/queue	Review queue ranked by exposure and evidence strength
POST	/v1/outcomes	Submit audit and recovery outcomes to recalibrate scoring

Request and response

illustrative

Request

{
  "claims": [
    {
      "claim_id": "HC-2026-7741208",
      "member_id_hash": "sha256:4f81\u2026",
      "provider_id": "prv_22104",
      "service_date": "2026-10-09",
      "procedures": [
        { "code": "99214", "units": 1, "amount": 182.00 },
        { "code": "36415", "units": 1, "amount": 24.00 }
      ],
      "diagnoses": ["E11.9", "I10"],
      "place_of_service": "office"
    }
  ],
  "peer_basis": "specialty_region_casemix",
  "include_evidence": true
}

Response

{
  "claim_id": "HC-2026-7741208",
  "anomaly_score": 68,
  "band": "elevated",
  "patterns": [
    { "code": "VISIT_INTENSITY_ABOVE_PEERS", "weight": 0.34,
      "detail": "Provider bills level-4 office visits on 71% of encounters; case-mix-adjusted peer median is 28%",
      "peer_group": { "specialty": "internal_medicine", "region": "south_central",
                      "providers": 184, "casemix_adjusted": true } },
    { "code": "POSSIBLE_UNBUNDLING", "weight": 0.19,
      "detail": "36415 billed separately alongside 99214 on 94% of encounters" }
  ],
  "duplicate_check": {
    "result": "near_duplicate",
    "matched_claim": "HC-2026-7739044",
    "difference": "service_date differs by 1 day, identical procedure and amount",
    "similarity": 0.96
  },
  "provider_context": {
    "claims_90d": 2140,
    "peer_percentile_billed_per_encounter": 0.97,
    "trend": "rising_since_2026-04"
  },
  "estimated_exposure": 41800.00,
  "recommended_action": "provider_billing_review",
  "clinical_judgement": "not_assessed",
  "requires_human_review": true
}

Where the data comes from

Your adjudicated claims history	Claims with procedure codes, diagnoses, amounts, providers and adjudication outcomes — the core dataset, which you already hold at scale
Standard code sets and bundling rules	Procedure, diagnosis and modifier code sets plus published bundling and edit rules, licensed where the code set requires it
Provider registries and credentialing data	Specialty, location and practice affiliation, needed to construct peer groups that mean anything
Fee schedules and contracted rates	Your own contracted rates by provider and procedure, so an amount outlier is measured against the agreed price rather than a national average
Audit and recovery outcomes	Which reviews found a problem and which did not — sparse, valuable, and usually poorly recorded
Published utilisation benchmarks	Where available and licensable, used as a weak external reference rather than as the primary comparison

On data licensing. Being allowed to read a dataset does not automatically mean you may redistribute it, resell it through an API, or train a model on it. Before any build starts we check the licence, terms and derived-data rules of every source, and we design around the ones that do not permit your intended use. Where a commercial licence is required, that cost is identified during discovery rather than discovered later.

The intelligence layer

The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.

Peer group construction with case mix adjustment as the central design problem, since an unadjusted comparison flags every provider with a sicker-than-average panel
Hierarchical modelling of provider billing behaviour so a provider with ninety claims is not compared on the same footing as one with nine thousand
Unsupervised outlier detection as the primary method, because confirmed billing fraud labels are extremely sparse and heavily biased toward what was previously audited
Rule-based edit checks for unbundling, mutually exclusive codes and frequency limits, evaluated separately from statistical scoring and reported separately
Near-duplicate detection across date, provider and member dimensions with tolerance, since exact duplicates are largely caught by existing edits already
Temporal change detection on provider profiles, which often catches a behaviour shift earlier than a level-based outlier test
Explicit peer group disclosure on every comparison, so a reviewer can see and challenge who the provider was compared against
Calibration against audit outcomes with false positive tracking, because provider relationships are damaged by unfounded review requests

What it changes for the business

Recovered leakage

Peer-relative detection surfaces consistent small overbilling that claim-level edits are structurally unable to see.

Prioritised audit effort

A queue ranked by exposure and evidence strength replaces audit selection by sampling or by complaint.

Earlier detection

Trend detection on provider profiles catches a shift in behaviour months before a level-based outlier test would.

Defensible provider conversations

Case-mix-adjusted peer evidence with the comparison group disclosed is a discussion a provider can engage with.

How it fits into your product

How it embeds

Batch scoring over adjudicated claims, typically nightly, with provider profiles refreshed on a slower cycle
Pre-payment scoring where your adjudication flow can accommodate it, though most value is achievable post-payment
Review queue surfaced in your existing audit tooling with the full peer comparison evidence attached
Outcome feedback from audits and recoveries, which is the main driver of improving precision over time
Provider-facing evidence packs generated from the same comparison data, for use in billing review conversations
Deployment inside your infrastructure, which health claim data requires in essentially every jurisdiction

Included in every build

Authentication, scoped API keys and per-customer rate limiting
Input validation with predictable, documented error responses
Versioned endpoints so your integration never breaks without notice
Request and response logging for audit and dispute resolution
Latency, error-rate and usage monitoring with alerting
OpenAPI specification, sandbox environment and client examples
Backups, failover design and a documented recovery procedure
An agreed support arrangement once the service is live

How we would build it

about 20 weeks

Discovery and feasibility

Weeks 1–3

This one depends heavily on data access, so we start by proving the sources exist, can be licensed for your use, and actually carry the signal the model needs. If they do not, you find out here.

Architecture and baseline model

Weeks 4–6

A written architecture and a baseline model measured against a holdout period, so you know what accuracy you are buying before the full build starts.

Production build

Weeks 7–16

Data pipelines, model training and serving, historical archive, validation harness, API, security, monitoring and documentation.

Validation and integration

Weeks 17–20

Backtesting against held-out history, accuracy reporting your team can audit, integration support and an agreed retraining cadence.

Commercial models

There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.

Fixed-scope prototype

A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.

Fixed-price build

An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.

Build then retainer

We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.

White-label product

The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.

Questions buyers ask

Does this judge whether treatment was appropriate?

No, deliberately. It analyses billing patterns, not clinical decisions. Whether a particular level of service was warranted, whether a procedure was necessary, whether care met a standard — all of those require clinical qualification and the full medical record, and a statistical outlier is not evidence either way. Every response carries an explicit field stating that clinical judgement was not assessed, because the distinction gets lost quickly once a score reaches an operational team.

Will this flag legitimate providers?

Yes, some of the time, and pretending otherwise would be dishonest. A provider treating a genuinely sicker panel, a sub-specialist within a broad specialty code, or a provider coding correctly while their peers under-code will all look like outliers. Case mix adjustment reduces it; it does not eliminate it. That is why output is a prioritised review queue with disclosed peer groups rather than a finding, and why we track false positives against audit outcomes.

How much claim volume do you need?

Enough to build peer groups, which is the real constraint. Peer comparison needs a reasonable number of comparable providers within a specialty and region — roughly a hundred for stable comparison, and small specialties or thin regions may never reach it. Duplicate detection and rule-based edits work at any volume. For a small scheme we would be clear about which parts of this are viable and which are not.

Can this run before payment?

Technically yes, with a caveat about where the value is. Pre-payment scoring prevents outflow but has to meet adjudication latency and tends to be limited to duplicates and rule-based checks. Peer-relative patterns need accumulated volume and are inherently retrospective. Most programmes get the majority of their recovery post-payment and use pre-payment for the subset of checks that work there.

What about member privacy?

Health claim data is among the most sensitive categories in every jurisdiction we would build in. Member identifiers are hashed or tokenised, member-level analysis is limited to what the detection genuinely requires, retention is bounded, and deployment is inside your environment. Where member-level utilisation patterns are analysed — for card sharing, for instance — that has its own lawful basis and access controls rather than sitting in the same queue as provider analysis.

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