Use case
Use case3 min readBy The Data Workers Team

Fraud, waste and abuse data, run by agents

FWA analytics run on claims and payment data that are duplicated, late or remapped, so leads are weak. Here is how Data Workers runs it instead.

Key takeaways

  • •The problem today. FWA analytics run on claims and payment data that are duplicated, late or remapped, so leads are weak.
  • •What changes. Agents verify payment data on arrival, catch duplicates and keep FWA models on clean inputs.
  • •What stays with your people. Referrals to investigators: they affect people and providers.
  • •The result. Investigators get leads built on verified data.

The problem today

FWA analytics run on claims and payment data that are duplicated, late or remapped, so leads are weak.

What changes with Data Workers

Agents verify payment data on arrival, catch duplicates and keep FWA models on clean inputs.

The workflow: Data Ingestion agent (get_connector_status) → Quality Monitoring agent (get_anomalies) → Incident Debugging agent (get_root_cause) → MLOps & Models agent (detect_model_drift).
Fig. 1:The workflow: Data Ingestion agent (get_connector_status) → Quality Monitoring agent (get_anomalies) → Incident Debugging agent (get_root_cause) → MLOps & Models agent (detect_model_drift).

The workflow, step by step

  1. 1.The Data Ingestion agent calls get_connector_status and fingerprints payment files.
  2. 2.The Quality Monitoring agent calls get_anomalies and flags anomalies.
  3. 3.The Incident Debugging agent calls get_root_cause and traces data issues.
  4. 4.The MLOps & Models agent calls detect_model_drift and watches FWA models.

The impact metrics we move

  • •Duplicate file caught: from about a week to 15-20 min
  • •Anomaly traced: from about 3 days to under 1 h
  • •Model drift detected: from about a month to ~1 h
Time to complete, current practice (black) vs. with Data Workers (green). Illustrative.
Fig. 2:Time to complete, current practice (black) vs. with Data Workers (green). Illustrative.

What goes on autopilot

  • •Payment files are verified on arrival, so leads are not data errors.
  • •FWA models are watched for drift, so leads stay strong.

Where your people stay in charge

Referrals to investigators always go to a named approver, because they affect people and providers. Everything starts at L1, observing only, and autonomy rises only on evidence.

The pilot

  • •Scope. Payment-file verification for one program.
  • •First week. Agents run at L1, observing only, and show the fixes they would have made, with receipts.
  • •By week two. The routine work above runs on its own; anything sensitive stays with a named approver.
  • •At day 30. We review the impact metrics against your baseline:
  • •Duplicate file caught: from about a week to 15-20 min
  • •Anomaly traced: from about 3 days to under 1 h
  • •Model drift detected: from about a month to ~1 h
  • •Cost. Start with a pilot ($7,500 one-time, credited in full against the first year). Scale is from $1,000 a month and Enterprise from $3,000 a month, billed annually, with unlimited seats, no usage meter and no markup on model spend. See pricing.

Related: The government hub · The full team playbook

Related Posts

Use case

Account intelligence data, run by agents

Account data is split across CRM, billing, product usage and enrichment vendors that identify accounts differently. Here is how Data Workers runs it instead.

Use case

Accruals and prepaids data, run by agents

Accrual schedules live in spreadsheets, and missed or stale accruals surface in review. Here is how Data Workers runs it instead.

Use case

Agent governance and audit, run by agents

AI agents act on data with no consistent approval, record or rollback. Here is how Data Workers runs it instead.

Use case

Agentic controllership, run by agents

Controllers spend the month chasing reconciliations, approvals and evidence across ERP, billing and spreadsheets. Here is how Data Workers runs it instead.