Fraud Detection — claims screening → cited investigation

Screens a Medicare/Medicaid claims corpus against the public rulebook (NCCI MUE, OIG LEIE, CMS enrollment, PFS) and produces ranked, fully-cited investigation referrals for an SIU. The skill orchestrates a three-tier investigation: a deterministic floor does the detection, the model judges and narrates on top, and every dollar/rule allegation traces back to the floor.

Output framing

• "Indicators consistent with [scheme]," not "fraud." A pattern match doesn't establish intent — that's a downstream investigative/legal determination. This is standard SIU language and the framing the renderers use.
• Render for review. The skill writes packets to $CLAUDE_HEALTHCARE_DATA/fraud-detection/out/; the payer's SIU workflow decides what to do with them. The model does not send/publish on its own.

Inputs

• The payer's claims in corpus.duckdb (canonical 6-table schema: claims-schema.sql). Getting this is step 1 below — without it nothing else matters.
• Quarter (the NCCI/PFS rule set to cite against, e.g. 2026q3).
• Line of business (medicare / medicaid).

Data root

All fetched/generated state lives outside the plugin install path (which is wiped on upgrade) at ~/.claude/data/healthcare/fraud-detection/ — override the parent dir with $CLAUDE_HEALTHCARE_DATA (each skill appends its own name). Below, data-cache/ and out/ are subdirectories of $CLAUDE_HEALTHCARE_DATA/fraud-detection. Resolve it once at the start of a run:

export CLAUDE_HEALTHCARE_DATA="${CLAUDE_HEALTHCARE_DATA:-$HOME/.claude/data/healthcare}"

Steps

1. Get the payer's claims into corpus.duckdb. Open with: "Where do your adjudicated claims live?" and follow ${CLAUDE_PLUGIN_ROOT}/skills/fraud-detection/LOAD-CLAIMS.md — it walks you and the user from "I don't know" to a populated $CLAUDE_HEALTHCARE_DATA/fraud-detection/data-cache/corpus.duckdb. If they already have a .duckdb with the canonical tables (schema: ${CLAUDE_PLUGIN_ROOT}/skills/fraud-detection/claims-schema.sql), use it directly.

   Draft the brief (corpusDb path, quarter, line of business) and confirm scope.

2. Seed the public reference layer (first run / new quarter only). Detectors cite against $CLAUDE_HEALTHCARE_DATA/fraud-detection/data-cache/reference/<quarter>/reference.duckdb. If that file is missing for the requested quarter, fetch it now — this prints per-source ✓ name (size) progress as ~34 sources land:

   node "${CLAUDE_PLUGIN_ROOT}/skills/fraud-detection/scripts/fetch-reference.js" 2026q3
   node "${CLAUDE_PLUGIN_ROOT}/skills/fraud-detection/scripts/fetch-enrichment.js"
   

   Requires unzip and pdftotext (poppler) on PATH; both ship with most distros / brew install poppler. Needs real network egress — if you see "Could not resolve host" for cms.gov / oig.hhs.gov, the command sandbox is blocking it; re-run with sandbox disabled. Policy PDFs (NCCI manual, MLN articles) land under reference/<q>/policy/*.txt for grep; everything keyed lands in reference.duckdb. Skip if already present. If a fetch fails or a table is missing, see REFERENCE-DATA.md for source URLs and recovery.

3. Create the run directory. Each invocation lands in its own minute-stamped directory so prior runs are preserved side-by-side. Every script honors FRAUD_OUT_DIR:

   export FRAUD_OUT_DIR="$CLAUDE_HEALTHCARE_DATA/fraud-detection/out/run-$(date +%Y%m%d-%H%M)"
   mkdir -p "$FRAUD_OUT_DIR"
   echo "$FRAUD_OUT_DIR"
   

   Use the printed absolute path verbatim as outDir in the next step.

4. Run the investigation by calling the Workflow tool with:

   • scriptPath: ${CLAUDE_PLUGIN_ROOT}/skills/fraud-detection/workflows/investigate.js
   • args: { "corpusDb": "<abs path to corpus.duckdb>", "quarter": "2026q3", "lob": "medicaid", "pluginRoot": "${CLAUDE_PLUGIN_ROOT}/skills/fraud-detection", "dataRoot": "<abs $CLAUDE_HEALTHCARE_DATA/fraud-detection>", "outDir": "<abs FRAUD_OUT_DIR from step 3>" }

   The workflow runs three stages (see "How it works"):

   • Detect — runs the deterministic sweep (scripts/screen.js, zero model) → $FRAUD_OUT_DIR/referrals.detect.json.
   • Adjudicate — one agent per judgment-required finding (D2/D4/D7/D13) sets status + adjudication.reason; mechanical detectors auto-confirm. Adjudicate may dismiss or downgrade, never add.
   • Synthesize — one agent per provider writes the investigator narrative, plus novel-lead discovery with adversarial verification.

   Tell the user they can watch the fan-out live with /workflows.

5. Materialize the stage snapshots + render (required — this is the reviewable deliverable). The workflow sandbox has no filesystem, so write its return to disk and let apply-stages.js produce the auditable spine. FRAUD_OUT_DIR does not persist across separate Bash calls — re-export it (to the same absolute path you printed in step 3) at the top of every shell block that needs it: Use the Write tool to save the workflow's return JSON verbatim to $FRAUD_OUT_DIR/workflow-result.json (it can be 50KB+ — don't heredoc it through Bash). Then:

   export FRAUD_OUT_DIR="<abs path from step 3>"
   node "${CLAUDE_PLUGIN_ROOT}/skills/fraud-detection/scripts/apply-stages.js"
   

   This writes $FRAUD_OUT_DIR/referrals.adjudicated.json, referrals.final.json, referrals.json (canonical, = final), and the renderer sidecars (source-excerpts.json, providers.json).

   Then render the packets FIRST, then the dashboard (the dashboard only links a provider row to its packet if that packet file already exists), then the xlsx:

   export FRAUD_OUT_DIR="<abs path from step 3>"
   node "${CLAUDE_PLUGIN_ROOT}/skills/fraud-detection/scripts/render-packet.js" --all
   node "${CLAUDE_PLUGIN_ROOT}/skills/fraud-detection/scripts/render-dashboard.js"
   node "${CLAUDE_PLUGIN_ROOT}/skills/fraud-detection/scripts/render-xlsx.js"
   

   → $FRAUD_OUT_DIR/provider-packet-<npi>.html ×N, index.html, referrals.xlsx.

6. Show the dashboard so the user can validate it visually.

   • Claude Code Desktop with the preview tool available: serve the run directory and open it in the side-pane preview — npx serve "$FRAUD_OUT_DIR" (the dashboard is index.html, so the root URL is the dashboard; packet links resolve as siblings).
   • Otherwise (terminal CLI, no preview tool): use the OS opener on $FRAUD_OUT_DIR/index.html — try in order, stop at the first that works:

     f="$FRAUD_OUT_DIR/index.html"
     open "$f" 2>/dev/null \            # macOS
       || xdg-open "$f" 2>/dev/null \   # Linux
       || wslview "$f" 2>/dev/null \    # WSL
       || powershell.exe start "$(wslpath -w "$f")" 2>/dev/null \  # WSL→Windows fallback
       || cmd.exe /c start "" "$f" 2>/dev/null \                   # Windows
       || echo "Could not auto-open; open manually: $f"
     

   Skip this entirely in a headless/non-interactive run (eval, CI, Cowork) — just report the path. Don't fail the run if opening/serving fails.

7. Relay: the ranked referrals (NPI, schemes, exposure $, confidence) and total exposure from the workflow result, verbatim where it cites numbers. Surface meta.disclaimer if it is set. Do not add any dollar or rule the deterministic floor did not produce. End the response with the run-directory path on its own line so downstream graders/tools can locate the artifacts:

   Run directory: <absolute $FRAUD_OUT_DIR>
   

8. Close the loop (optional) — see ${CLAUDE_PLUGIN_ROOT}/skills/fraud-detection/PROPOSE-DETECTORS.md to mine this run for new detector candidates and payer-specific adjudicate-time checks.

The inviolable line

The model adjudicates, explores, and narrates freely, but any dollar or rule allegation must trace to a detect-stage deterministic recompute (the gate in scripts/gate.js). Adjudicate may dismiss or downgrade a finding (with an auditable reason) — it never adds one or changes its dollars. Synthesize narratives are separate, clearly-marked model output and never introduce a number the floor did not compute.

Enrichment — local cached data (canonical), MCPs for interactive only

The deterministic pipeline reads enrichment from local cached files (scripts/fetch-enrichment.js → $CLAUDE_HEALTHCARE_DATA/fraud-detection/data-cache/enrichment/, loaded via scripts/enrichment.js) — no runtime auth, no drift, fully reproducible. The healthcare plugin's bundled MCP servers (CMS Coverage / ICD-10 / NPI Registry) are for interactive adjudicate/synthesize exploration only; the pipeline does not depend on them.

• ICD-10-CM — code validity / description (NLM Clinical Tables)
• CMS Coverage (LCD/NCD) — medical-necessity policy index; cached, feeds D4 adjudication
• NPI Registry — provider taxonomy/status

How it works (plugin layout)

• Entry skill — this file; orchestrates the workflow, never does the math.
• Workflow — workflows/investigate.js (Claude Code dynamic Workflow): Detect → Adjudicate → Synthesize.
• Deterministic sweep — scripts/screen.js <corpus.duckdb> <quarter> <lob> runs all detectors and writes $FRAUD_OUT_DIR/referrals.json. Zero model calls.
• Detectors — scripts/dNN-*.js (one deterministic module each, sharing the Finding shape).
• Pipeline — scripts/pipeline.js (run → gate → roll up → rank → referrals.json).
• Citation gate — scripts/gate.js (independently recomputes every cited number; uncited or non-reproducing findings are dropped — "citation-or-zero").
• Reference data — scripts/reference-data.js loads $CLAUDE_HEALTHCARE_DATA/fraud-detection/data-cache/reference/ (NCCI/MUE, LEIE, PFS, enrollment), fetched by scripts/fetch-reference.js, versioned by date-of-service quarter.
• Enrichment — scripts/enrichment.js loads $CLAUDE_HEALTHCARE_DATA/fraud-detection/data-cache/enrichment/, fetched by fetch-enrichment.js.
• Stage merge — scripts/apply-stages.js (workflow return → referrals.adjudicated.json / .final.json).
• Renderers — scripts/render-dashboard.js (→ index.html), render-packet.js, render-xlsx.js → $FRAUD_OUT_DIR/.

Every allegation cites a public rule with a value the gate independently recomputes, or it is dropped.