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International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
Open Access

An Analytical Study of a Governed Lakehouse Framework in Multi-State Medicare Claims and Provider Data for Fraud, Waste, and Abuse Detection

DOI https://doi.org/10.55640/ijidml-v03i10-03
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Chandra Obul Reddy Nagi Reddy 1 ORCID iD ,
4 Organization: Gainwell Technologies, Designation: Advisor Database Architect

Abstract

Medicare fraud detection, waste, and abuse is hampered by the high volume of claims, highly fragmented data-analysis systems, limited visibility into data quality, and significant class imbalance. This study assessed the potential of an Original Medicare Part B (OMB) Bronze-Silver-Gold lakehouse to improve data quality, traceability, analytical efficiency, and provider-level risk identification across OMB-covered populations in California, New York, Florida, Texas, and Pennsylvania in 2024. Using Apache Spark/PySpark with Delta-format tables, the study sampled 1,500,000 provider-service records in proportion to state Traditional Medicare enrollment, representing 214,873 unique NPIs, consolidated them with CMS provider files and HHS-OIG provider-exclusion data, interpreted 30 features, and used four models: Logistic Regression, Random Forest, XGBoost, and Isolation Forest. During Silver-layer screening, 27,362 records (1.82%) triggered one or more quality rules and were quarantined; NPI enrichment had 99.7% compliance, and Gold-layer data quality had 99.2% compliance. This reduced query time from a median of 18.4 seconds to 5.7 seconds (approximately 69%). XGBoost performed best among the supervised models, with ROC-AUC = 0.91, PR-AUC = 0.34, and F1 = 0.66. State elevated/high-risk flags were not confirmed as fraud; they were a priority for investigation and ranged from 2.2%–3.4% of unique providers within each state. Manually managed, repeatable pipelines provide defensible Medicare FWA prioritization, and repeatable, source-to-feature, auditable pipelines can support multi-state, analytic workflows where needed. Areas to explore include longitudinal claims, Part D, DMEPOS, enforcement outcomes, graph analytics, and prospective investigator validation.

Keywords

Medicare fraud, waste and abuse, Governed data lakehouse, Medicare Part B claims

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