Challenges

The U.S. Justice Department estimates that 3% of healthcare claims in the United States, worth almost a hundred billion dollars, are fraudulent. Investigating claims is time consuming and expensive with payers pursuing fraudulent cases for months or years after payments have been made. Fraudulent claims contribute to the increased cost of care, slow down valid claims, and lead to higher healthcare premiums for patients. Traditional manual review doesn’t scale across billions of claims per year and rules-based fraud detection systems are expensive and slow to adapt to new fraud techniques.

Opportunity

AI is ideally suited to fraud detection for medical claims. Machine learning models can be used to automate claims assessment and routing based on existing fraud patterns. This process flags potentially fraudulent claims for further review, but also has the added benefit of automatically identifying good transactions and streamlining their approval and payment. More advanced anomaly detection systems can be deployed to find new patterns and to flag those for review, which leads to prompt investigation of new fraud types. AI systems can also provide clear reason codes for investigators, so they can quickly see the key factors that led the AI to indicate fraud which streamlines their investigation. With AI based fraud detection, fraudulent claims can be evaluated and flagged before they are paid, which reduces costs for payers, helps keep costs lower for patients and helps catch fraudsters in the act.