Medical fraud is increasingly apperceived as one of serious social concerns. Healthcare fraud is a problem for the government and there is a need more effective detection method; which requires great amount of efforts with proper medical knowledge. Traditionally healthcare fraud detection greatly depended on the experience of domain experts, which is erroneous, expensive, and time consuming. Manual detection of healthcare fraud involves a few auditors who manually review and identify the suspicious medical insurance claims, which requires much effort. However, with the advancement in machine learning, and data mining techniques there is a way for more efficient and automated detection of healthcare frauds. There has been a growing interest in mining healthcare data for fraud detection in the recent years. 

Medical fraud detection management market actually helps in preventing healthcare fraud, waste, and abuse. Healthcare fraud is completely a misrepresentation or intentional deception of facts by healthcare professionals or patients, which can be lead to unauthorized payments or benefits. Some examples of healthcare frauds are including falsified data by physicians, multiple claims filed by different providers for the same patients, submitting claims for services which are not provided, misrepresenting dates in various treatments, frequency, and duration or description of services provided. 

The increase in number of fraudulent activities in healthcare, rise in the number of patients seeking healthcare insurance are the major driving factors for the growth of medical fraud detection management market. Moreover, the increase in pressure of fraud cases, waste, and abuse on healthcare spending are also expected to boost the growth of the market. However, the lack of skilled professionals and reluctance to adopt these fraud analytic systems in developing countries can restrain the market growth. The advancement in cloud-based analytics and development of AI techniques for healthcare fraud detection will help to open new avenues for the growth of medical fraud detection management market.

The medical fraud detection management market is segmented based on type, component, delivery mode, application by end users and region. Type wise, the healthcare fraud detection management market is segmented in descriptive analytics, predictive analytics, and prescriptive analytics. Based on component, the medical fraud detection management market is segmented into software and services. By the mode of delivery, the market is segmented into on-premise and on demand. Application wise, the medical fraud detection management market is segmented in insurance claims review, payment integrity, and other applications. The market is segmented by end users into private insurance payers, public/government agencies, employers, third party service providers. Region wise, the medical fraud detection management market has been analyzed across North America, Europe, Asia-Pacific, and LAMEA.

The key players of medical fraud detection management market include - IBM, Optum, Verscend Technologies, McKesson, Fair Isaac, SAS Institute, HCL Technologies, Wipro, Conduent, CGI Group.

KEY BENEFITS FOR STAKEHOLDERS

• This report provides a detailed quantitative analysis of the current healthcare fraud detection management market trends and forecast estimations from 2018 to 2025, which assists to identify the prevailing opportunities. 
• An in-depth market analysis includes analysis of various regions is anticipated to provide a detailed understanding of the current trends to enable stakeholders formulate region-specific plans.
• A comprehensive analysis of the factors that drive and restrain the growth of the global market is provided.
• Region-wise and country-wise market conditions are comprehensively analyzed in this report.
• The projections in this report are made by analyzing the current market trends and future market potential from 2018 to 2025 in terms of value.
• An extensive analysis of various regions provides insights that are expected to allow companies to strategically plan their business moves.
• Key market players within the market are profiled in this report and their strategies are analyzed thoroughly, which helps in understanding competitive outlook of global market.  

KEY MARKET SEGMENTS

By Type

• Descriptive Analytics
• Predictive Analytics
• Prescriptive Analytics 

By Component

• Software
• Services 

By Delivery Mode

• On-premise
• On-demand 

By Application

• Insurance claims review
• Payment integrity
• Other applications

By End Users

• Private insurance payers
• Public/government agencies
• Employers
• Third party service providers

By Region

• North America
  • U.S.
  • Canada
  • Mexico
• Europe
  • Germany
  • France
  • UK
  • Italy
  • Rest of Europe
• Asia-Pacific
  • Australia
  • Japan
  • India
  • China
  • Rest of Asia-Pacific
• LAMEA
  • Brazil
  • Saudi Arabia
  • South Africa
  • Rest of LAMEA