Senior Fraud, Waste, and Abuse Data Analyst

Essential Job Duties

Data Analysis & Fraud Detection

• Analyze Medicaid claims, visit, and billing datasets using SQL and other analytical tools.

• Identify patterns and anomalies that may indicate fraud, waste, or abuse, including:

• Visit overlaps and impossible or implausible service combinations

• Inflated, duplicate, or unbundled billing

• Provider billing spikes or outlier utilization patterns

• Inconsistencies in electronic visit verification (EVV) data

• Suspicious provider enrollment or credentialing indicators

• Patterns indicative of upcoding, place-of-service manipulation, or beneficiary identity issues

• Develop and refine detection queries and analytical logic that can be applied across datasets at scale.

• Conduct proactive data analysis to identify emerging fraud patterns and program integrity risks.

• Apply knowledge of the end-to-end revenue cycle — including claims submission, adjudication, remittance, and denial/appeal workflows — to contextualize billing anomalies and assess their integrity implications.

AI & Advanced Analytics

• Apply machine learning and AI techniques to fraud detection, including anomaly detection models, predictive risk scoring, and unsupervised clustering of suspicious billing behavior.

• Collaborate with data science teams on feature engineering, model validation, and the operationalization of AI-driven detection logic.

• Leverage generative AI and LLM-based tools to support investigation summarization, pattern narrative development, and analytical workflow acceleration.

• Stay current on emerging AI/ML applications in healthcare payment integrity and recommend adoption of relevant tools and techniques.

• Test, validate, and continuously improve fraud detection models and analytical tools as they are developed and refined.

Product & Engineering Collaboration

• Translate analytical findings into clear, actionable requirements for product and engineering teams.

• Contribute to the design of fraud detection dashboards, alerting systems, and investigation workflows.

• Support the development of automated detection tools and AI-driven fraud identification capabilities.

• Serve as a subject matter expert on FWA and program integrity concepts to ensure detection logic is clinically and operationally sound.

Client & Stakeholder Engagement

• Present analytical findings and insights to internal stakeholders and payer clients — including state Medicaid agencies and managed care organizations — in a clear and actionable format.

• Support client discussions related to fraud detection strategy, program integrity reporting, and regulatory compliance obligations.

• Advise payer and state partners on detection methodologies aligned with CMS program integrity expectations, Medicaid Integrity Program (MIP) standards, and applicable federal regulations.

• Document analytical methodologies and investigation approaches to support compliance, audit readiness, and regulatory expectations.

Other Job Duties

• Other duties as assigned by supervisor or HHAeXchange leader.

Travel Requirements

• Travel up to 10%, including overnight travel

Required Education, Experience, Certifications and Skills

Required

• 5–7 years of experience in healthcare analytics, payment integrity, fraud detection, program integrity, forensic data analysis, or a related field.

• Strong SQL proficiency, including the ability to independently query and analyze large, complex datasets.

• Experience identifying patterns, anomalies, or outliers in large healthcare claims or billing datasets.

• Solid understanding of the end-to-end revenue cycle, including claims submission, adjudication, remittance (EOB/835), and denial and appeal processes.

• Working knowledge of Medicaid billing structures, including procedure/service codes (HCPCS, CPT), claim types (837P/837I), and applicable billing rules for home and community-based services.

• Familiarity with federal Medicaid program integrity regulations, including 42 CFR Parts 431, 447, and 455, and CMS oversight and reporting expectations.

• Understanding of how Medicaid managed care organizations (MCOs) and state Medicaid agencies operate, contract, and oversee provider networks.

• Working knowledge of provider operations in home care or personal care settings, including how providers enroll, bill, and are reimbursed under Medicaid.

• Experience using AI or machine learning tools for anomaly detection, fraud identification, risk scoring, or predictive analytics in healthcare claims data.

• Strong analytical and investigative problem-solving skills with the ability to follow a data thread from anomaly to actionable finding.

• Ability to communicate complex analytical findings to both technical and non-technical audiences, including state regulators and managed care compliance teams.

• Comfort working in an evolving environment where new capabilities and processes are actively being developed.

Preferred

• Experience with a payment integrity organization, healthcare analytics company, managed care plan, or state Medicaid agency.

• Experience with Python, R, or advanced analytics and data visualization tools.

• Experience with electronic visit verification (EVV) data and familiarity with EVV mandates under the 21st Century Cures Act.

• Familiarity with Medicaid RAC, UPIC, or MIC audit processes and how findings are used in program integrity workflows.

• Experience with ML model development, feature engineering, or working alongside data science teams on healthcare fraud models.

• Exposure to generative AI or LLM tools applied to healthcare analytics, investigation support, or clinical/billing documentation review.

• Knowledge of CARC/RARC codes, claim edit logic, or prior authorization workflows as they relate to payment integrity.

• Experience with Medicaid home care, personal care services (PCS), or HCBS programs.

• Professional certifications such as:

• Certified Fraud Examiner (CFE)

• Accredited Healthcare Fraud Investigator (AHFI)

• Certified Professional Coder (CPC)

• Certified in Healthcare Compliance (CHC)