Healthcare Provider Fraud Detection

Eliminate fraudulent healthcare claims, improve the efficiency of your claims processing and protect your policyholders from fraudulent healthcare providers

This turnkey enterprise AI solution helps health insurers analyze vast amounts of claims data and take into account several variables such as billing patterns, healthcare provider demographics, and patient diagnosis codes to detect fraudulent activities. Contact us to know more about how this solution can help you detect fraud, mitigate losses and take preventive measures.

Key Metrics

Increase in Fraud Detection by

20-40%

Decrease in Loss due to Fraud by

50-70%

Insurance companies are investing in fraud detection solutions to reduce losses arising due to fraudulent claims. In the context of healthcare claims, analyzing historical claims data needs to accompany an analysis of billing, provider details such as location, speciality and ownership, patient codes and diagnosis from a social network analysis lens. This allows the insurers in understanding both the salient mechanisms of fraud and the subtle mechanisms such as involving provider fraud. 

 

This solution takes into account the relationships between providers, patients, and payers to discover if any patterns exist. Additionally it studies the type of device used to submit claims, and the IP address from which the claims are submitted and processed. By augmenting this with third-party data sources such as public records, legal documents, third-party audit reports, and news reports, this solution can be used to identify potential fraudulent activities, such as a provider being involved in lawsuits or having a history of fraudulent activities.

 

Implementing this solution reduces financial losses by detecting fraud early and accurately. It also helps the insurance company in improving the efficiency of claim processing by reducing the time and effort needed from expert fraud investigators. In addition, this solution can also ensure compliance in claims submission and processing, helping you in protecting the best interests of your policy holders and improving their trust and satisfaction. 

Highlights

  • Collect and analyze claims data, including billing patterns, provider demographics, and patient diagnosis codes
  • Build and use an AI-based fraud detection model using historical claims data
  • Continuously update the model with real-time claims data and information from third-party audit agencies to improve fraud detection
  • Use the AI model to automate the fraud detection process and identify fraudulent claims early in the claim processing cycle
  • Monitor and analyze the performance of the AI model and its impact on fraud detection rate, financial loss and fraud prevention
  • Continuously update the model with new data and fine tune the model to improve the accuracy and detect new types of fraud.

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* Values are approximates arrived at based on earlier experience and/or existing literature. Contact us to find out how you can measure the ROI on this solution for your business