AI-Powered Claims Analytics & Fraud Detection

Client Background.

A national health insurer sought to leverage FHIR APIs and AI-driven analytics to detect fraudulent claims, reduce manual adjudication efforts, and improve cost savings. The objective was to enhance claims processing efficiency while minimizing financial losses due to fraud.

Business Problem.

  • High Volume of Fraudulent Claims – The insurer faced increasing fraudulent activities, leading to financial losses and administrative burdens.
  • Manual Claims Adjudication Delays – Claims processing required extensive manual review, slowing reimbursements and increasing operational costs.
  • Lack of Real-Time Fraud Detection – Traditional fraud detection methods relied on retrospective audits, making it difficult to prevent fraudulent payouts in real-time.
  • Data Silos Across Multiple Systems – Claims data was fragmented across multiple platforms, making it challenging to analyze and detect anomalies.

Our Solution.

  • FHIR-Enabled Claims Data Integration – Implemented FHIR APIs using HL7 standards to standardize and consolidate claims data from multiple sources, improving visibility and accessibility.
  • AI-Driven Fraud Detection Models – Utilized machine learning frameworks like TensorFlow and Scikit-learn to develop predictive models for anomaly detection, reducing fraudulent claims.
  • Automated Claims Adjudication System – Developed a rules-based engine with Apache Drools, integrated with AI insights to streamline claims review and decision-making.
  • Real-Time Monitoring & Alerts – Built a dashboard using Power BI and Grafana for real-time fraud detection, alerting investigators to high-risk claims instantly.

Technology Stack.

  • FHIR Integration: Microsoft Azure FHIR API, Google Cloud Healthcare API
  • AI & Machine Learning: TensorFlow, Scikit-learn, PyTorch
  • Data Processing: Apache Spark, Pandas, SQL Server
  • Automation & Workflow: Apache Drools, Camunda BPM
  • Monitoring & Reporting: Power BI, Grafana, ELK Stack

Implementation Process.

  • Month 1: FHIR-based data ingestion and integration with insurer’s claims systems.
  • Month 2: Development of AI-powered fraud detection models and anomaly detection algorithms.
  • Month 3: Deployment of automated claims adjudication workflows and fraud alert mechanisms.
  • Month 4: Launch of real-time monitoring dashboards and investigator tools.

Team Composition.

  • Data Scientists: Developed and fine-tuned fraud detection algorithms.
  • FHIR Integration Specialists: Implemented HL7-compliant data exchange mechanisms & data Mapping.
  • Software Engineers: Built automation workflows and integrated AI models.
  • Cloud Architects: Managed deployment on Azure and Google Cloud.
  • Security Analysts: Ensured compliance with HIPAA and SOC 2 regulations.

Results & Impact.

  • 30% Reduction in Fraudulent Payouts – AI-driven fraud detection minimized financial losses due to fraudulent claims.
  • 50% Faster Claims Processing – Automation and AI-powered adjudication accelerated claims approvals and reimbursements.
  • Improved Accuracy in Fraud Detection – Machine learning algorithms identified patterns and flagged suspicious claims with high precision.
  • FHIR-Based Standardization – Enabled seamless data exchange between claims processing systems, improving interoperability.

Key Takeaways.

  • FHIR-based data integration enhances claims processing efficiency and fraud detection.
  • AI-driven analytics reduce manual adjudication efforts and improve cost savings.
  • Real-time fraud monitoring prevents financial losses and enhances regulatory compliance.
  • Standardized claims data fosters interoperability across the insurance ecosystem.