Surfaces the cases most likely to be fraud, waste, or abuse before payment leaves the door.
ArqFWA is a payment-integrity intelligence layer for healthcare payers that reads across claims, providers, policy, and member context to surface the cases most likely to be fraud, waste, abuse, or claims leakage — and explains the reasoning clearly enough for an investigator to act on and defend. Unlike rule-based systems that generate thousands of low-value flags, it correlates signals across multiple dimensions to prioritize only the cases worth human attention.
Built for: Healthcare payers, TPAs, PBMs, and program-integrity teams
Typically owned by: VP of Payment Integrity, Director of SIU, and Chief Medical Officer at health plans; VP of Operations and VP of Compliance at TPAs; Program Integrity leads at government and managed care organizations.
Payment-integrity teams are buried in claim volume. Rule engines generate enormous alert volumes, but most are low-value hits that consume investigator time without recovering meaningful dollars. The highest-risk patterns sit across claims history, provider behavior, policy language, and member context simultaneously — no single system sees all of it, and cases often escalate after payment has already left the door.
ArqFWA converts the payment-integrity function from a high-volume, low-yield review queue into a targeted, evidence-backed investigation workflow. Investigators spend their time on cases that matter. Each case arrives with the context needed to act on it and defend the decision downstream. Leaders have a clear view of program performance without a black box.
A reusable workflow spine, tuned to your data, systems, and controls — not a generic model wrapper.
Correlates billing patterns, provider behavior, and member history into one risk picture instead of thousands of isolated rule hits — surfacing coordinated schemes that single-dimension rules cannot detect.
Every flagged case carries the evidence, peer comparisons, and policy references a reviewer needs. Every flag includes the reasoning chain an investigator can follow and defend in an appeal, audit, or recovery proceeding.
Builds a longitudinal view of each provider — behavioral drift, peer-comparison anomalies, and network-level coordinated billing — so emerging risk is visible before it compounds into material leakage.
Routes the highest-value cases to investigators with context assembled, suggested next steps pre-populated, and a clean, exportable audit trail attached.
Learns from investigation outcomes and recoveries so prioritization keeps sharpening — reducing false positives over time and surfacing more material cases per investigator hour.
Operates across pre-payment flagging and post-payment recovery: preventing payments that match risk patterns and identifying paid patterns worth pursuing retrospectively.
Every agent action carries the trigger, the reasoning, the inputs, and the outcome in an encrypted, persistent audit trail. No black boxes.
A coordination agent manages the flow between signal ingestion, pattern correlation, scoring, and routing. A claim analysis agent reads across the claim history for the member and provider simultaneously, while a peer comparison agent benchmarks provider behavior against peer groups adjusted for specialty and geography.
A case assembly agent builds the evidence package and reviewer brief — pulling relevant claims, provider history, policy language, and comparable peer data into a structured case record. All agents write to a shared audit log that serves as the governance trail for every decision made in the system.
Accelerators move fastest when the first release is narrow, measurable, and connected to the people who own the work.
Load a sample claims and provider-history slice; calibrate signals against known leakage patterns and past investigation outcomes.
Deploy the reviewer workbench with explanations, evidence packages, and routing alongside the existing process.
Expand from sampled review to active queue prioritization; measure recovery and false-positive rates against baseline.
Integrate the recovery workflow, calibrate continuously from outcomes, and expand to additional claim types and program lines.
Hold high-risk claims for review before payment leaves the door, with rationale your edits team can stand behind.
Surface paid-claim patterns worth pursuing for recovery, provider education, or audit.
Assemble defensible evidence packages for provider audits in minutes instead of weeks.
ArqFWA connects to the claims infrastructure the team already operates and begins adding value within weeks, not quarters — with governance-first review loops and auditable recommendations on every case.
A two-week analysis of a claims sample slice against 120+ fraud, waste, and abuse patterns not currently covered by your existing rule engine. Delivers a risk distribution map, a comparison of ArqFWA prioritization against your current review queue, and a recommended accelerator configuration — before any build commitment.
Book itArqFWA is an AI payment-integrity accelerator for healthcare payers. It reads across claims, providers, policy, and member context to surface the cases most likely to be fraud, waste, abuse, or claims leakage, and attaches the evidence trail investigators need to act on and defend every case.
Healthcare payers, third-party administrators (TPAs), pharmacy benefit managers (PBMs), and program-integrity teams — typically owned by the VP of Payment Integrity, Director of SIU, or VP of Compliance.
Rule engines generate thousands of isolated, low-value flags. ArqFWA correlates billing patterns, provider behavior, and member history into one risk picture, scores cases with explainable evidence rather than black-box scores, and keeps calibrating against your real investigation outcomes.
Most teams start with the FWA Blind Spot Assessment: a two-week analysis of a claims sample against 120+ FWA patterns your current rules don't cover. You get a risk distribution map and a recommended configuration before committing to any build.
