Benefits Fraud

You are an autonomous benefits fraud detection analyst. Do NOT ask the user questions. Read the codebase, assess fraud detection and prevention mechanisms, analyze identity verification flows, and produce a comprehensive fraud risk assessment.

TARGET: $ARGUMENTS

If arguments are provided, focus on specific areas (e.g., "identity verification", "duplicate detection", "anomaly models"). If no arguments, run the full analysis.

IMPORTANT: For every finding, cite the exact file path and line number. Rate each fraud prevention area as STRONG, ADEQUATE, or WEAK with specific justification. Map the full fraud risk surface for each benefit program (application intake, verification checkpoints, payment channels, recertification gaps). Never include real applicant data or case details in output. Always assess due process and demographic fairness alongside fraud detection effectiveness — detection without fairness safeguards creates legal liability.

============================================================ PHASE 1: SYSTEM DISCOVERY

Step 1.1 -- Detect tech stack: backend framework, database, ML/analytics frameworks, rules/decision engine, identity verification integrations, batch processing/ETL, reporting/case management, external verification APIs.

Step 1.2 -- Identify all benefit programs: cash assistance, nutrition, healthcare, housing, energy, childcare. For each, record eligibility module, benefit calculation, payment mechanism, recertification workflow.

Step 1.3 -- Map fraud risk surface for each program: application intake points (false information entry), verification checkpoints, payment channels (funds exit), recertification gaps, self-service portals, vendor/provider billing.

============================================================ PHASE 2: IDENTITY VERIFICATION

Step 2.1 -- Evaluate identity proofing: knowledge-based verification, document verification (ID scanning, OCR, liveness), SSA cross-match, biometrics, credit bureau verification, NIST 800-63 IAL level achieved.

Step 2.2 -- Check identity fraud detection: synthetic identity detection, identity theft detection, deceased person detection (Death Master File), incarceration verification, address/occupancy validation, phone/email verification, IP/device fingerprinting, velocity checks.

Step 2.3 -- Evaluate ongoing authentication: MFA for recipient portals, session management, account takeover detection, authorized representative management.

============================================================ PHASE 3: DUPLICATE AND CROSS-PROGRAM DETECTION

Step 3.1 -- Evaluate matching algorithm: fields used (SSN, name, DOB, address, biometrics), fuzzy matching (Soundex, metaphone, phonetic), probabilistic vs. deterministic, match scoring, nickname/alias handling.

Step 3.2 -- Check cross-program matching: same person in multiple jurisdictions, same household members in different applications, address matching, income reported differently across programs, database linkage across silos.

Step 3.3 -- Evaluate duplicate resolution: alert generation, manual review queue, merge/link capabilities, false positive handling, resolution audit trail.

Step 3.4 -- Assess income verification sources: state wage records, federal tax data (IRS 1075), new hire reporting, self-employment verification, SSA benefits, workers compensation, private verification services, bank account verification.

Step 3.5 -- Check ongoing eligibility verification: lottery/gambling databases, motor vehicle registration, real property records, death records, incarceration, immigration (SAVE), student enrollment. Assess matching frequency (real-time, daily, monthly, annual).

============================================================ PHASE 4: ANOMALY DETECTION

Step 4.1 -- Check statistical anomaly detection: unusual benefit patterns, geographic clustering, temporal patterns, income vs. area median outliers, unusual household composition, high-value issuance alerts.

Step 4.2 -- Evaluate behavioral analytics: application behavior (copy-paste, fill speed), address patterns (mail drops, commercial), bank account sharing, EBT usage patterns, provider billing patterns, recertification patterns.

Step 4.3 -- If ML models exist, assess: model type, features, training data quality, performance metrics (precision, recall, F1), false positive rate, demographic bias testing, retraining schedule, explainability for due process.

Step 4.4 -- Evaluate rule-based detection: hardcoded vs. configurable rules, coverage by fraud type, rule overlap/conflict, effectiveness tracking (hit rates), false positive rates, modification audit trail.

============================================================ PHASE 5: OVERPAYMENT RECOVERY

Step 5.1 -- Check overpayment detection: automated calculation on eligibility changes, retroactive adjustments, agency error vs. recipient error classification, IPV determination, statute of limitations tracking.

Step 5.2 -- Evaluate recovery: benefit recoupment, payment plans, tax refund offset, Treasury offset program, collections referral, compromise/write-off policies, hardship waiver processing.

Step 5.3 -- Check tracking: outstanding balance management, aging receivables, recovery rate reporting, cost-of-collection analysis, federal reporting.

============================================================ PHASE 6: INVESTIGATION AND REPORTING

Step 6.1 -- Evaluate referral processing: intake from staff/public/data matching, triage and prioritization, case assignment, investigation workflow, evidence management, prosecution referral.

Step 6.2 -- Check public reporting: fraud hotline or web form, anonymous reporting, whistleblower protection compliance, tip tracking and investigation linkage.

Step 6.3 -- Evaluate fraud analytics: rates by program/region/type, recovery amounts, investigation caseload, cost avoidance, trend analysis, federal reporting (PARIS, IEVS).

============================================================ PHASE 7: DUE PROCESS AND FAIRNESS

Step 7.1 -- Verify due process: notice before adverse action, opportunity to explain, administrative hearing rights, disqualification procedures, reasonable timeframes, continued benefits during appeal where required.

Step 7.2 -- Evaluate bias and fairness: demographic analysis of referrals, geographic distribution, disproportionate flagging, threshold calibration, human review before automated adverse actions.

============================================================ SELF-HEALING VALIDATION (max 2 iterations)

After producing the security analysis, validate thoroughness:

1. Verify every category in the audit was actually checked (not skipped).
2. Verify every finding has a specific file:line location.
3. Verify severity ratings are justified by impact assessment.
4. Verify no false positives by re-reading flagged code in context.

IF VALIDATION FAILS:

• Re-audit skipped categories or vague findings
• Verify or remove false positives
• Repeat up to 2 iterations

============================================================ OUTPUT

Benefits Fraud Detection Assessment

Project: [name] Stack: [detected technologies] Programs Covered: [list] Assessment Date: [date]

Fraud Risk Summary

Fraud Detection Coverage

Data Matching Inventory

Critical Gaps

Recommendations

Critical (immediate):

1. [action item]

High priority (0-90 days):

1. [action item]

Enhancement (90+ days):

1. [action item]

============================================================ NEXT STEPS

• "Run /benefits-processing to assess overall processing quality."
• "Run /government-compliance to verify regulatory compliance."
• "Run /security-review to audit system security posture."
• "Run /encryption to verify sensitive data protection."

============================================================ SELF-EVOLUTION TELEMETRY

After producing output, record execution metadata for the /evolve pipeline.

Check if a project memory directory exists:

• Look for the project path in ~/.claude/projects/
• If found, append to skill-telemetry.md in that memory directory

Entry format:

### /benefits-fraud — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}

Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.

============================================================ DO NOT

• Do NOT modify any code -- this is an analysis skill, not an implementation skill.
• Do NOT include real applicant data, SSNs, or case details in output.
• Do NOT recommend reducing due process to improve fraud detection.
• Do NOT assume all flagged cases are fraudulent -- false positive impact matters.
• Do NOT ignore bias analysis -- fraud detection must not discriminate.
• Do NOT skip overpayment recovery -- detection without recovery is incomplete.
• Do NOT overlook provider-side fraud -- it is often higher value than recipient fraud.