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ArqFWA — Payment integrity and fraud intelligence
Payment integrity and fraud intelligence

ArqFWA

Fraud, waste, abuse, and claims-leakage intelligence for payer operations.

Overview

Built for healthcare payers and the teams around them.

ArqFWA is a payment-integrity intelligence layer for healthcare payers. It reads across claims, providers, policy, and member context to surface the cases most likely to be fraud, waste, abuse, or leakage — and explains the reasoning clearly enough for an investigator to act on and defend.

Built for: Healthcare payers, TPAs, PBMs, and program-integrity teams

30%+More high-value cases surfaced
2xFaster review triage
100%Decision evidence captured
The challenge

Where teams get stuck.

Payment-integrity teams are buried in claim volume, brittle rules, and cases that arrive too late to protect margin. The highest-risk patterns sit across claims, providers, policy, and member context — and no single system sees all of it.

The shift

What changes with ArqFWA.

ArqFWA prioritizes the cases worth human attention, explains why they matter, and keeps the evidence trail clean enough for downstream recovery and audit.

Built for production

ArqFWA prioritizes the cases worth human attention, explains why they matter, and keeps the evidence trail clean enough for downstream recovery and audit.

Capabilities

What ArqFWA does.

A reusable workflow spine, tuned to your data, systems, and controls — not a generic model wrapper.

Cross-signal anomaly detection

Correlates billing patterns, provider behavior, and member history into one risk picture instead of thousands of isolated rule hits.

Explainable case scoring

Every flagged case carries the evidence, peer comparisons, and policy references a reviewer needs — no black-box scores.

Provider risk profiling

Builds a longitudinal view of each provider so emerging risk is visible before it compounds into material leakage.

Reviewer workbench

Routes the highest-value cases to investigators with context, suggested next steps, and a clean, exportable audit trail.

Continuous calibration

Learns from investigation outcomes and recoveries so prioritization keeps sharpening against your real results.

How it rolls out

From fit check to first operating queue.

Accelerators move fastest when the first release is narrow, measurable, and connected to the people who own the work.

01

Load a sample claims and provider-history slice.

02

Calibrate risk signals against known leakage and investigation outcomes.

03

Deploy a reviewer workbench with explanations, evidence, and routing.

04

Expand from sampled review to active queue prioritization.

Use cases

Where it earns its place.

Pre-pay flagging

Hold high-risk claims for review before payment leaves the door, with rationale your edits team can stand behind.

Post-pay recovery

Surface paid-claim patterns worth pursuing for recovery, provider education, or audit.

Provider audits

Assemble defensible evidence packages for provider audits in minutes instead of weeks.

Integrations

Wired into the stack you already run.

Built from payer fraud, waste, abuse, and claims-review patterns already appearing across ArqAI healthcare work, with governance-first review loops and auditable recommendations.

Claims platforms (Facets, QNXT)Policy & edit enginesProvider directoriesData warehouse / lakehouseCase managementBI & reporting
ArqFWA in context
Fit signals

When ArqFWA is worth a closer look.

  • Rules generate too many low-value flags
  • SIU and claims-review teams need better prioritization
  • Provider risk context is scattered across systems
  • Leadership wants payment-integrity value without a black box

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