Fraud Waste and Abuse Data Analyst

Role Overview

The Sr. Fraud, Waste, and Abuse (FWA) Data Analyst helps build fraud detection capabilities for Medicaid home and community-based care. You will analyze large healthcare datasets to identify suspicious billing patterns and convert insights into scalable detection logic within the HHAeXchange platform.

Responsibilities

• Fraud Detection & Data Analysis:

  • Analyze Medicaid claims, visit, and billing datasets using SQL and other analytical tools.
  • Identify anomalies/patterns that may indicate fraud, waste, or abuse, including:
    • Visit overlaps and impossible/implausible service combinations
    • Inflated, duplicate, or unbundled billing
    • Provider billing spikes/outlier utilization patterns
    • EVV (Electronic Visit Verification) inconsistencies
    • Suspicious provider enrollment/credentialing indicators
    • Signals of upcoding, place-of-service manipulation, or beneficiary identity issues
  • Develop/refine detection queries and analytical logic that can be applied at scale.
  • Proactively analyze data to detect emerging fraud and program integrity risks.
  • Apply end-to-end revenue cycle knowledge (claims submission, adjudication, remittance, denial/appeal) to contextualize billing anomalies.

• AI & Advanced Analytics:

  • Use machine learning/AI for fraud detection (anomaly detection, predictive risk scoring, unsupervised clustering).
  • Partner with data science teams on feature engineering, model validation, and operationalizing AI detection.
  • Use generative AI / LLM-based tools to accelerate investigations (e.g., summarization and narrative development).
  • Stay current on healthcare payment integrity AI/ML approaches; recommend tool/technique adoption.
  • Test, validate, and continuously improve fraud detection models and analytical tools.

• Product & Engineering Collaboration:

  • Translate analytical findings into clear, actionable requirements for product and engineering to embed fraud detection into the platform.