Healthcare Fraud Detection Market Size, 2022 Outlook, Current and Future Market Landscape Analysis 2030

Market Research Future (MRFR) evaluates that the Global Healthcare Fraud Detection Market Size can attain a strong valuation of USD 18,300.31 Million by 2030.

Market Insights:

Market Research Future (MRFR) evaluates that the Global Healthcare Fraud Detection Market Size can attain a strong valuation of USD 18,300.31 Million by 2030. The increasing count of causes related to medical scams, false insurance claims, exploitation of medical funds, etc. has necessitated the adoption of healthcare fraud detection technology, and the trend is anticipated to continue across the review period.

With the developments in the healthcare sector, the number of frauds has been on the rise, which includes drug frauds, medical insurance frauds, and medicine frauds. The growth of Healthcare Fraud Detection Market is expected to accelerate considering the increasing number of scams.

The embracement of fraud analytics ensures optimum utilization of medical funds by minimizing embezzlement of funds and subsidies. Thus, the Healthcare Fraud Detection market Outlook is likely to proliferate rapidly over the next couple of years.

Some of the factors responsible for holding the market growth through the review period are lack of skilled personnel, reluctance in the adoption of the technology, etc.

Healthcare Fraud Detection Market Overview and Report Analysis By Type (Descriptive Analytics, Predictive Analytics and Prescriptive Analytics), By Component (Services and Software), By Delivery Model (On-Premise and Cloud-Based), By Application (Insurance Claims Review and Payment Integrity), By End User (Private Insurance Payers, Public/Government Agencies and Third Party Service Providers) - Forecast till 2030

 

Market Segmentation:

  • By components, the Healthcare Fraud Detection Market Trends is segmented into software and services. Among these, the services segment is likely to register a relatively higher CAGR during the projection period.
  • By kinds, the global healthcare fraud detection market has been segmented into predictive analytics, descriptive analytics, and prescriptive analytics. The prescriptive analytics segment is expected to show the steepest rise in the growth while descriptive analytics segment is estimated to be the primary revenue generator in the market.
  • By delivery models, the healthcare fraud detection market is segmented into on-demand, and on-premise. Among these, the on-demand segment is expected to demonstrate a relatively higher growth rate.
  • By end-users, the global healthcare fraud detection market has been segmented into employers, regulatory/public agencies, private insurance payers, third-party services, payment integrity, insurance claim reviews, and identity case management.

Competitive Dashboard:

Some of the key profiled by MRFR in Healthcare Fraud Detection Market Players are SAS, Conduent, Optum, Pondera, IBM, and Wipro among others. The strategies devised by the players for garnering share and control over the market include acquisitions developments, strategic alliances, technological developments, product portfolio expansion, innovation, etc.

Regional Analysis:

By region, the global Healthcare Fraud Detection Market Outlook has been segmented into Americas, Asia Pacific, Europe, and the Middle East Africa. Americas currently accounts for the largest share of the global market and is expected to retain its dominance over the forecast period. Europe is likely to hold the second spot in the global healthcare fraud detection market. The growth of the market is attributable to technological developments, accessibility to products services, etc. Asia Pacific is expected to remain a highly lucrative market throughout the assessment period.

Industry News:

  • In July 2018, Zelis Healthcare, a market-leading healthcare technology company, announced the launch of fully integrated Zelis Intelligent Claims Routing Platform that leverages Microsoft Azure Artificial Intelligence. Zelis will help in minimizing the complexity that occurs due to errors, waste, fraud, and

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shweta shweta

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