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Home Cybersecurity

Healthcare fraud detection market to grow steadily by 2026

New York Tech Editorial Team by New York Tech Editorial Team
January 25, 2022
in Cybersecurity
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Healthcare fraud detection market to grow steadily by 2026
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43% of the growth will originate from North America for the healthcare fraud detection market. The US and Canada are the key markets for healthcare fraud detection in North America. Market growth in North America will be faster than the growth of the market in other regions.

healthcare fraud detection 2026

The healthcare fraud detection market is set to grow by $1.26 billion from 2021 to 2026 at a CAGR of 14.26% as per the latest research report from Technavio. The healthcare fraud detection market to record a 10.37% Y-O-Y growth rate in 2022.

Healthcare fraud detection market driver

The key factor driving growth in the healthcare fraud detection market is the increased number of patients seeking health insurance. The number of people benefitting from various healthcare schemes has grown considerably over the years. The rise in the aging population increased the burden of diseases, and growth in healthcare expenditure are the major factors that contribute to the growth of the health insurance market.

Developing countries such as Asia are significantly growing in health insurance coverage, primarily due to the rising government initiatives, increasing government and private investments for promoting medical insurance, and growing income levels. This growth is aided by the increasing affordability of health insurance for the middle class in this region and the rising awareness regarding the benefits of health insurance.

Market challenge

The time-consuming deployment and need for frequent upgrades will be a major challenge for the healthcare fraud detection market during the forecast period. The placement of fraud analytics solutions is a time-consuming process. The process involves creating user interfaces, new databases, and predictive models; evaluating and deploying models, and monitoring their effectiveness. In this process, data analysts continuously run algorithms until they get the most effective predictive model.

At times, analysts may not find the appropriate predictive model for expected outcomes, which results in time wastage. This means they must start the same process again with new data. Furthermore, the software requires frequent upgrades, as fraudsters constantly change tactics. This adds to the total cost of fraud analytics solutions. Such challenges can hinder the growth of the market in focus.

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