Mr. Kiran Veernapu
Deep Learning in Detecting Healthcare Fraud: The Economic Burden on Public and Private Insurers
Abstract:
Healthcare fraud continues to be a significant challenge, inflating costs and undermining the effectiveness of healthcare systems worldwide. The economic burden, which exceeds billions of dollars annually, affects both public and private insurers, leading to higher premiums, reduced patient access, and strained resources. While traditional fraud detection methods like manual audits and rule-based systems have been in use for decades, they are often slow, resource-intensive, and unable to adapt to evolving fraudulent schemes. This speech will explore how deep learning—an advanced subset of artificial intelligence—can transform healthcare fraud detection by automating complex data analysis and recognizing patterns that traditional methods miss. I will highlight the economic benefits of leveraging deep learning techniques such as neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) in identifying fraudulent activities, such as upcoding, unnecessary services, and phantom billing. Additionally, I will discuss how AI-driven systems can reduce operational costs, improve detection accuracy, and enhance resource allocation, ultimately mitigating the financial strain on insurers and improving patient care outcomes. However, the adoption of these technologies is not without challenges, including data quality, implementation costs, and ethical concerns regarding patient privacy. This speech will provide insights into the future of healthcare fraud detection and the crucial role deep learning will play in fostering more sustainable, cost-efficient healthcare systems.
Profile
Kiran Veernapu is an accomplished leader with 25 years of experience in developing innovative software solutions to address complex business challenges across diverse industries, with a particular emphasis on healthcare. He is passionate about harnessing the power of AI and data science to build high-performance systems that enable business leaders to make informed, data-driven decisions. As a published author of numerous research papers on AI, including applications of Machine Learning, Deep Learning, CNN, and RNN in healthcare, Kiran has made substantial contributions to advancements in patient care. Committed to improving healthcare outcomes and saving lives, he has successfully driven millions of dollars in cost savings through cutting-edge research and AI-powered process automation.