Global Healthcare Fraud Analytics Market is Projected to Reach USD 4.6 Billion by 2025 From USD 1.2 Billion In 2020 – ResearchAndMarkets.com

DUBLIN–(BUSINESS WIRE)–The “Healthcare Fraud Analytics Market by Solution Type (Descriptive, Predictive, Prescriptive), Application (Insurance Claim (Postpayment, Prepayment), Payment Integrity), Delivery (On-premise, Cloud), End User (Insurance, Government) – Global Forecast to 2025” report has been added to ResearchAndMarkets.com’s offering.

The emergence of social network analytics, the adoption of technologies such as AI and blockchain, and the growing use of healthcare analytics for fraud detection in emerging nations like the APAC provide growth opportunities in this market. However, the time-consuming deployment of these solutions and the need for frequent updates are some challenges faced by end-users of this market.

The on-demand segment is projected to witness the highest growth during the forecast period

On the basis of delivery model, the healthcare fraud analytics market is segmented into on-premise and on-demand models. The on-demand models include the cloud-based and web-based models. The on-demand segment is projected to register the highest CAGR during the forecast period. Factors such as on-demand self-serving analytics, the lack of up-front capital investments for hardware, extreme capacity flexibility, and a pay-as-you-go pricing model are driving the demand for on-demand fraud detection solutions.

The prepayment review model is projected to witness the highest growth during the forecast period

On the basis of application, the healthcare fraud analytics market is segmented into insurance claims review, pharmacy billing misuse, payment integrity, and other applications. The insurance claims review segment is further divided into post-payment and prepayment review, with the latter expected to register the highest growth during the forecast period. This is mainly because the use of prepayment review protocols and analytics can help organizations proactively prevent fraud prior to payment, allowing rapid action to be taken. As a result, prepayment review solutions are expected to garner greater attention in the coming years.

The Asia Pacific to witness the highest growth during the forecast period

The Asia Pacific is expected to witness the highest growth during the forecast period (2020 to 2025), owing to factors such as the growing demand for health insurance and increasing collaborations & partnerships between governments and tech giants for leveraging fraud analytics capabilities.

Reasons to Buy the Report

The report will enrich established firms as well as new entrants/smaller firms to gauge the pulse of the market to help them garner a greater share of the market. Firms purchasing the report could use one or any combination of the below-mentioned strategies to strengthen their position in the market.

This report provides insights into the following pointers:

  • Market Penetration: Comprehensive information on the portfolios of top players in the global healthcare fraud analytics market. The report analyzes this market by solution type, delivery model, application, end-user, and region.
  • Product Enhancement/Innovation: Detailed insights on upcoming technology trends in the global healthcare fraud analytics market
  • Market Development: Comprehensive information on the lucrative emerging markets
  • Market Diversification: Exhaustive information about growing geographies, recent developments, and collaborations in the healthcare fraud analytics market
  • Competitive Assessment: In-depth assessment of growth strategies, offerings, and capabilities of leading players in the global healthcare fraud analytics market

Key Topics Covered:

1 Introduction

1.1 Objectives of the Study

1.2 Market Definition

1.3 Market Scope

1.4 Currency

1.5 Limitations

1.6 Stakeholders

2 Research Methodology

2.1 Research Approach

2.2 Market Size Estimation

2.3 Market Breakdown and Data Triangulation

2.4 Assumptions for the Study

3 Executive Summary

4 Premium Insights

4.1 Healthcare Fraud Analytics Market Overview

4.2 Asia Pacific: Market, By Solution Type and Application (2019)

4.3 Market: Geographic Growth Opportunities

4.4 Market: Regional Mix

4.5 Market: Developing vs Developed Regions

5 Market Overview

5.1 Introduction

5.2 Market Dynamics

5.2.1 Drivers

5.2.2 Restraints

5.2.3 Opportunities

5.2.4 Challenges

6 Industry Insights

6.1 Industry Trends

7 Healthcare Fraud Analytics Market, By Solution Type

7.1 Introduction

7.2 Descriptive Analytics

7.3 Predictive Analytics

7.4 Prescriptive Analytics

8 Healthcare Fraud Analytics Market, By Delivery Model

8.1 Introduction

8.2 On-Premise Delivery Models

8.3 On-Demand Delivery Models

9 Healthcare Fraud Analytics Market, By Application

9.1 Introduction

9.2 Insurance Claims Review

9.3 Pharmacy Billing Misuse

9.4 Payment Integrity

9.5 Other Applications

10 Healthcare Fraud Analytics Market, By End User

10.1 Introduction

10.2 Public & Government Agencies

10.3 Private Insurance Payers

10.4 Third-Party Service Providers

10.5 Employers

11 Healthcare Fraud Analytics Market, By Region

11.1 Introduction

11.2 North America

11.3 Europe

11.4 Asia Pacific

11.5 Latin America

11.6 Middle East & Africa

12 Competitive Landscape

12.1 Introduction

12.2 Competitive Situation and Trends

12.3 Competitive Leadership Mapping

13 Company Profiles

13.1 IBM

13.2 Optum (A Part of Unitedhealth Group)

13.3 Cotiviti Holdings, Inc.

13.4 Fair Isaac Corporation

13.5 SAS Institute

13.6 Change Healthcare

13.7 EXL Service Holdings, Inc.

13.8 Wipro

13.9 Conduent, Inc.

13.10 HCL Technologies

13.11 CGI Group

13.12 DXC Technology Company

13.13 Northrop Grumman Corporation

13.14 LexisNexis (A Part of Relx Group)

13.15 Pondera Solutions

13.16 Other Players Operating in the Healthcare Fraud Analytics Market

13.16.1 WhiteHatAI

13.16.2 Healthcare Fraud Shield

13.16.3 FraudLens, Inc.

13.16.4 HMS

13.16.5 FraudScope, Inc.

For more information about this report visit https://www.researchandmarkets.com/r/jlwhbs

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