Devi Manoharan

AI-Driven Anomaly Detection for Preventing Claims Denials and Revenue Leakage in Healthcare

Abstract:

Healthcare organisations process vast volumes of claims annually, yet even minor coding, mapping, or documentation inconsistencies can trigger costly denials and significant revenue leakage. Traditional rule-based validation frameworks struggle to detect emerging denial patterns, payer-specific variations, and complex documentation gaps. This session presents a comprehensive AI-driven anomaly detection framework integrated into enterprise Quality Engineering pipelines to proactively prevent denials before adjudication.

Leveraging unsupervised learning techniques such as clustering, isolation forests, and autoencoders, organizations can uncover previously unknown billing anomalies without requiring labeled data. Supervised machine learning models enhance denial prediction accuracy by analyzing historical adjudication outcomes, provider patterns, payer requirements, and financial attributes. Natural language processing further strengthens validation by identifying inconsistencies between clinical documentation and coded data, reducing medical necessity denials and appeal cycles.

When deployed within a scalable microservices architecture, AI-enabled validation supports real-time feedback during claim submission as well as high-volume batch processing. Risk-based routing mechanisms prioritize high-risk claims for expert review while allowing low-risk claims to move efficiently through the adjudication pipeline. Closed-loop learning continuously incorporates payer responses and manual review feedback to improve model performance over time.

This presentation outlines a practical evaluation framework, governance blueprint, and deployment strategy that align with HIPAA, CMS, and ethical AI standards. Attendees will learn how AI-powered Quality Engineering transforms revenue cycle management from reactive defect correction to predictive quality assurance accelerating reimbursement cycles, improving first-pass payment accuracy, reducing manual intervention, and strengthening financial sustainability across the healthcare ecosystem.

Profile:

Devi Manoharan is an Enterprise Quality Engineering Specialist with advanced expertise in healthcare claims automation, X12 EDI compliance, and intelligent testing frameworks for payer and provider ecosystems. With extensive experience across healthcare, insurance, and financial domains, she has led quality engineering initiatives for large-scale claims adjudication systems, HIPAA-compliant EDI transactions, real-time processing pipelines, and enterprise data modernisation programs.
Her technical strengths include Selenium WebDriver, Java, Python, Cucumber BDD, TestNG, API testing, microservices validation, Kafka event-driven workflows, ETL/ELT data quality, and complex integration testing across SFTP, MQ, and cloud-based ecosystems. Devi has delivered high-impact automation frameworks, adaptive QE strategies, risk-based validation techniques, and scalable test architectures that improve first-pass adjudication accuracy, reduce operational defects, and accelerate payer modernisation.
Recognised for her analytical depth, leadership, and collaborative problem-solving, Devi plays a key role in driving quality, compliance, and innovation across mission-critical healthcare platforms. She is deeply committed to advancing automation, intelligent validation, and next-generation Quality Engineering to support faster, more accurate, and interoperable healthcare systems.