STANDALONE AI
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TPA Control Layer

Governance between insurer and TPA — decision quality, data quality, and full auditability.

A governance layer between the insurer and TPA organizations. It restores transparency and control: what decisions are being made, how complete and consistent the data is, how fast cases move, and where abnormal patterns appear — without replacing your core. It integrates on top of your existing landscape.

Claims supervisor · 11:40
Anything unusual in this week’s TPA submissions?
TPA Control Layer
3 flags: one case settled 40% above historical pattern for this clinic, two cases missing required invoices. Information requests sent automatically; the settlement outlier is escalated to your review queue.
Why it matters

Once TPAs are part of the operating model, insurers typically lose standardization and traceability. Data arrives with inconsistent quality, conversations fragment across email, calls, and messengers — and anomalies or leakage are detected too late, after payment decisions. The Control Layer turns TPA management into a controlled, auditable process.

Key capabilities

What TPA Control Layer covers

Single control point for incoming TPA casesCompleteness & logic validationComparison against historical patternsAutomatic document & info requestsEscalation to internal teamsCase-level chats & team workspacesFilter by department / line of business / TPASLA & KPI reportingManagement analyticsFull audit trail
Platform highlights

Why teams choose TPA Control Layer

Decision quality governance

AI guidance on missing items, anomalies, and next best action — grounded in your configured rules and historical patterns.

Earlier anomaly detection

Abnormal patterns and potential leakage surface before payment decisions and case closure — not after.

Self-hosted, your keys

Deployed on your infrastructure; LLM capabilities use your own provider keys per your security and compliance policy.

Pilot-first

Start with one process and one or two TPAs, measure impact, then scale across lines of business.

How it works

End-to-end flow

01

Integrate

Connect to your data sources — claims, policy, provider networks, payments, and other repositories.

02

Build context

The layer assembles full context: coverage, limits, historical behavior, prior decisions.

03

Validate & detect

Rule-based validation plus AI analysis detect abnormal patterns and missing data.

04

Communicate

Targeted questions, document requests, and escalation paths are triggered automatically.

05

Measure

Everything is logged and converted into SLA / KPI and audit-ready reporting.

Guide: TPA oversight & claims leakage →

Where it works

Use cases that make the difference

Claims via TPA networks

Validate completeness and consistency of every TPA submission against policy context.

Multi-TPA operations

Compare performance across TPAs: speed, quality, and consistency of outcomes — not just SLA timers.

Audit & compliance

A consistent trail of what happened and why — data, actions, and communications linked to each case.

Best fit
Insurers working with TPAsClaims operationsHealth & motor portfoliosAudit & compliance teams
Outcome

Lower leakage and fewer decision errors, reduced manual workload, faster cycle times, and real visibility into TPA performance — with a complete audit trail.

Common questions

FAQ

Will our TPAs have to change their systems?

No — the layer ingests the case flows and updates TPAs already send, validates completeness and logic, and requests missing information automatically. TPAs keep working as they do; you regain visibility and control.

Where does it run and where does our data go?

Self-hosted on your infrastructure — data never leaves your perimeter. LLM capabilities use your own provider keys, so you choose the model according to your security and compliance policy.

How does it detect leakage?

Every submission is compared against your historical patterns and policy context — rule-based validation plus AI anomaly detection. Outliers (unusual settlement amounts, missing documents, abnormal patterns per provider) surface before payment, not after closure.

How do we start without a big project?

A focused pilot: one process, one or two TPAs, agreed metrics. Integration to your data sources is scoped as a separate small project; scale follows measured impact.

Goes well with

Related products

See all in Insurance

Try it on your data

Two weeks. Your environment. A working proof of concept under NDA — see the impact before any commitment.