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The Generative AI field is experiencing explosive growth, with a projected market size surpassing  $36 billion by the end of 2024 . Generative AI is not merely a buzzword—an astounding  92% of Fortune 500 companies , including industry giants like Coca-Cola, Walmart, and Amazon, are already leveraging this technology. Generative AI isn’t fancy applications; it is driving real-world advantages, with companies reporting a  30-50% increase in productivity . However, achieving the top ranks requires high-quality data and significant resources in data planning. Nonetheless, optimizing generative AI models is essential to enhance their performance, accuracy, and generalization. McKinsey estimates that the economic impact can be undeniable, with a prospective annual contribution of  $6.1 trillion to $7.9 trillion  to the international economy. Generative AI  has vast applications, from revolutionizing healthcare with drug discovery and personalized remedies to str...

Healthcare fraud is a pervasive problem that costs individuals and insurance companies billions of dollars each year. According to the National Health Care Anti-Fraud Association, over  $54 billion  is lost annually due to fraudulent and illegal medical charges. As the volume and complexity of healthcare data continue to grow, traditional fraud detection methods are struggling to keep pace. This is where generative AI-powered fraud detection can make a significant difference. In this blog, we will discuss five ways in which generative AI helps with fraud detection in healthcare. Applications of Gen AI in preventing healthcare fraud 1. Automated Detection of Unusual Billing Patterns Generative AI in healthcare can analyze large datasets to spot unusual billing patterns that may indicate fraudulent activities. By continuously learning from historical data, AI models can identify discrepancies such as overbilling, upcoding, or billing for never-rendered services. Here’s an exampl...