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Fraud, Waste and Abuse Detection in Health Insurance Claims: Leveraging AI

Updated: Jun 12


Fraudulent health insurance claims cost the Indian healthcare system crores of rupees annually. Industry estimates peg the loss between 7% to 15% of the Gross Premium. Some analysts believe the overall impact of Fraud, Waste and Abuse may be higher than estimates. The Indian health insurance industry has witnessed significant growth, with a Gross Written Premium (GWP) exceeding Rs 78,000 Cr [Source: Insurance Regulatory and Development Authority of India (IRDAI)]. AI and machine learning (ML) offer powerful tools for insurers to detect and prevent such fraud. The IRDAI is actively promoting AI/ML adoption with draft guidelines and a focus on data quality. Collaboration between insurers, healthcare providers, and regulators is crucial to build a robust anti-fraud ecosystem.


The Problem of Fraudulent Claims

Health insurance fraud takes various forms and usually covers waste and abuse as well. Some examples of fraud, waste and abuse in health claims includes:


  • Billing for unnecessary or inflated services

  • Fabrication of medical records

  • Staged accidents or illnesses

  • Duplicate claims

  • Suspicious close proximity cases

  • Related party claim transactions (Practitioner or Provider or Agent and Patient)

  • Billing enhancement based on patients limit


There is a need to identify and minimise fraud, waste and abuse in health claims as these fraudulent activities not only increase healthcare costs for insurers but also burden policyholders with rising premiums. 


The Role of AI and Machine Learning in Fraud Detection

Artificial intelligence (AI) and machine learning (ML) offer powerful tools for insurers to combat health insurance fraud. Here's how AI/ML can be leveraged:


  • Advanced Analytics: Analysing massive datasets of medical claims, historical data, and patient records can identify patterns indicative of fraudulent activities.


  • Predictive Modeling: ML algorithms can predict the likelihood of fraud based on historical patterns and risk factors associated with certain procedures, providers, or policyholders.


  • Anomaly Detection: AI can identify suspicious claims that deviate significantly from established treatment protocols or cost norms.


  • Social Network Analysis: AI can analyze relationships between healthcare providers, patients, and pharmacies to uncover potential collusion in fraudulent schemes.


Benefits of AI/ML for Fraud Detection

  • Improved Accuracy: AI/ML can analyze vast amounts of data with greater accuracy than manual methods, leading to more efficient fraud detection.


  • Real-time Monitoring: AI systems can continuously monitor claim submissions for suspicious activity, enabling faster intervention.


  • Reduced Costs: By preventing fraudulent payouts, AI/ML can significantly reduce healthcare costs for insurers and ultimately policyholders.


Challenges and Considerations

While AI/ML offers significant potential, implementing robust fraud detection systems comes with challenges:


  • Data Quality: The effectiveness of AI/ML models heavily relies on the quality and accuracy of healthcare data. Incomplete or inconsistent data can hinder the system's performance.


  • Compliance and Regulation: Data privacy regulations and concerns around explainability of AI decisions need to be addressed for wider adoption.


  • Continuous Evolution of Fraud Schemes: Fraudsters constantly develop new methods. AI/ML systems require continuous monitoring and improvement to stay ahead of evolving tactics.


Recent Actions by IRDAI:

The IRDAI recognizes the potential of auto adjudication and is actively working towards its implementation:


  • Focus on Standardisation: The IRDAI is emphasising the importance of data quality in medical records and claim forms. Standardisation of coding practices and data formats like FHIR, ICD-10, SNOMED are key focus areas.


  • Promoting NHCX: National Health Claims Exchange is a digital health claims platform developed by the National Health Authority (NHA) in collaboration with the Insurance Regulatory and Development Authority of India (IRDAI)


  • Draft Guidelines on Claim Settlement Processes: These guidelines outline the framework for auto adjudication, highlighting data standardisation, claim processing timelines, and grievance redressal mechanisms.


Moving Forward:

Despite the challenges, auto adjudication holds immense promise for the Indian health insurance industry. Here are some key steps to ensure its successful implementation:


  • Improved Data Quality: Insurers and healthcare providers need to collaborate to improve data quality in medical records and claims submissions. Standardisation of coding practices and data formats is crucial. The implementation of NHCX along with standards like FHIR, ICD-10, SNOMED, LOINC etc will ensure standardisation of  electronic health records (EHR). Drona Pay has pre existing libraries to process standardised EHR data to automate claims processing. 


  • Investment in Analytics: Insurers need to invest in advanced analytics and machine learning capabilities to develop robust claim processing systems. There is a need to develop strong profiles for various treatments and diagnosis to identify outliers and anomalies which need adjuster / expert review. Drona Pay offers a new age Decisioning Platform to help automate claims processing. 


  • Transparency and Explainability: The claims adjudication process needs to be transparent, with clear explanations provided for claim decisions, especially denials. This builds trust in the system. Explainability and usage of Profiling and Rules is a key building block of the Drona Pay platform. 


  • Human-in-the-Loop Approach: A human-in-the-loop approach is crucial. While automation handles routine claims efficiently, human expertise remains essential for complex cases and medical judgement. Drona Pay offers a case management and BPMN modeller to support review by Adjudicators, Medical Professionals, Surveyors and Risk Analysts along with facilitating communication with Hospitals and Patients. 


  • Continuous Monitoring and Improvement: Continuously monitor the performance of auto adjudication systems and refine algorithms to address emerging trends and prevent bias. Back testing and Simulation are key features of the Drona Pay platform which helps insurers test and monitor the platform. 

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