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MediBuddy Unveils AI-Powered 'Sherlock' To Enhance Healthcare Fraud Prevention

Sherlock's integration into MediBuddy's cashless network aims to address significant challenges associated with fraud, waste, and abuse (FWA) in healthcare

MediBuddy, India’s leading digital healthcare platform, has introduced an innovative AI-powered fraud detection system named Sherlock. 

In a press statement on Thursday, the company said that this advanced platform uses artificial intelligence (AI), machine learning (ML), and data analytics to combat fraudulent claims in healthcare reimbursement, revolutionising the process for providers, insurers, and patients.

Sherlock's integration into MediBuddy's cashless network aims to address significant challenges associated with fraud, waste, and abuse (FWA) in healthcare. By eliminating the reimbursement process and enabling cashless solutions, the system can reduce costs by up to 20 per cent. This includes a 10 per cent cost reduction from the cashless network and an additional 10 per cent from Sherlock’s FWA detection capabilities.

Satish Kannan, Co-founder and CEO of MediBuddy, commented, “Fraud in healthcare reimbursement undermines system integrity. Our AI-driven solution empowers partners and users to combat fraudulent activities, ensuring a seamless and efficient claims process. Our approach helps identify risks early, enhancing trust, fairness, and cost-efficiency. Our extensive cashless network and advanced technology have already saved our partners Rs 6.3 crore in a single policy year.”

Sherlock utilises sophisticated AI and ML algorithms to perform real-time analysis, detecting potential errors or fraud before they impact the system. It identifies issues such as claim duplication, document tampering, and pricing discrepancies, thus reducing the need for manual reviews.

Key Features:

  • Advanced AI and ML Algorithms: Adapt to new fraud types for high accuracy.
  • Real-time Analysis and Alerts: Instantaneous claim analysis with automated alerts.
  • Pattern Recognition: Detects common fraudulent patterns such as inflated amounts and duplicates.
  • User Behavior Monitoring: Identifies anomalies in user behaviour indicating potential fraud.

 

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