查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
封面
市场调查报告书
商品编码
1401979

到 2030 年的医疗诈欺分析市场预测：按解决方案类型、部署、应用程式、最终用户和区域进行的全球分析

Healthcare Fraud Analytics Market Forecasts to 2030 - Global Analysis By Solution Type (Predictive Analytics, Prescriptive Analytics, Descriptive Analytics and Other Solution Types), Deployment, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，2023 年全球医疗诈欺分析市场规模将达到 23 亿美元，预计到 2030 年将达到 109 亿美元，预测期内复合年增长率为 24.7%。

医疗保健诈欺分析市场代表了医疗保健业务的新兴领域，它使用尖端技术和分析来检测、预防和减少诈欺。随着医疗保健实践变得更加复杂，以及电子健康记录、申请系统和索赔等多个资讯来源产生的资料量不断增加，对强大的诈骗侦测程序的需求也随之增加。

OIG 表示，医疗补助资料通常不完整且不准确，影响了诈欺申请检测流程，并导致 FWA 浪费了数十亿美元。

电子健康记录普及

随着医疗保健系统转向数位平台并能够存取大量患者资料，机会和挑战并存。电子健康记录(EHR) 的使用可以创建更广泛、更集中的医疗记录资料库，这为诈欺提供了机会。此外，为了防止这种情况，医疗保健组织正在使用先进的分析工具来审查电子健康资料是否有诈欺以及可能表明诈欺的趋势。

整合复杂度

将先进的诈欺分析系统整合到现有的医疗保健基础设施中是一项常见的实施任务，既复杂又耗时。不同的资讯格式、医疗保健组织之间不一致的标准以及与遗留系统的兼容性问题加剧了这种复杂性。与拥有不同 IT 系统的医疗机构合作时，很难实现无缝集成，因为他们需要确保高效的资料流和即时分析。然而，习惯于传统工作流程的员工可能会抵制医疗保健提供者并扰乱业务。

技术进步

分析工具、机器学习演算法和人工智慧的持续发展正在改变医疗保健部门防止诈欺的能力。这些技术进步使得更复杂、更有效的诈欺侦测技术能够即时处理大量医疗资料。进阶分析透过侦测复杂模式、异常和可疑方法来提高诈骗侦测的准确性和速度。此外，透过采用最尖端科技，医疗保健公司可以领先日益复杂的诈欺计划，同时最大限度地减少财务损失并保持系统完整性。

资料安全和隐私问题

随着越来越多的公司使用先进的分析来打击诈欺，管理大量敏感患者资料所带来的安全和隐私洩露问题越来越引起医疗保健公司的担忧。这已成为一个问题。医疗产业受到严格监管，因此存在诈欺存取、资料外洩和网路攻击的高风险。解决复杂问题需要以公正的方式从病患资料中收集关键见解，同时严格遵守 HIPAA（健康保险互通性与课责法案）等隐私法规。

COVID-19 的影响：

由于世界各地的医疗保健系统需要有效地分配资源并防止诈欺，因此诈欺分析解决方案比以往任何时候都更加重要。同时，流行病扰乱了卫生系统，转移了资源，并迅速将注意力集中在补救措施上。新医疗服务的快速推出以及与 COVID-19 相关的交易激增使诈欺侦测系统更具挑战性。此外，大流行的经济影响可能会进一步助长虚假申请。

预测分析产业预计在预测期内成长最大

预测分析细分市场预计将在预测期内成为最大的细分市场。预测分析使用先进的演算法和机器学习模型来分析历史资讯、识别趋势并预测未来的诈欺。透过采取积极主动的方法并领先于新的诈欺计划，医疗保健提供者可以防止财务损失并保护医疗保健系统的完整性。此外，预测分析透过即时分析大型资料集并提高侦测可疑行为的准确性，同时减少诈骗侦测侦测的有效性。

药房申请问题领域预计在预测期内复合年增长率最高。

预计药品申请问题的复合年增长率最高。药房申请问题，包括申请、分拆和诈欺处方笺申请，已成为医疗产业诈欺的主要手段。诈欺的增加推动了对专门分析解决方案的需求，这些解决方案旨在识别药品申请资料中的异常和差异。预测模型和机器学习演算法等即时诈欺分析工具正用于调查药房申请交易。

占有率最大的地区：

该地区的快速现代化和数位转型使得许多国家采用了电子健康记录（EHR）和其他数位医疗技术，其中亚太地区所占份额最大。由于医疗保健成本不断上升以及与诈欺相关的处罚不断增加，亚太地区的医疗保健支付者和提供者正在投资先进的分析解决方案。亚太地区也明显增加了旨在改善卫生系统课责和透明度的监管措施。

复合年增长率最高的地区：

凭藉其复杂的医疗基础设施和完善的报销系统，北美地区处于有利地位，可以继续盈利扩张。医疗保健诈欺带来的财务成本不断增加，促使监管机构在美国实施《虚假申报法》和《健康保险申请与责任法》(HIPAA) 等措施，以防止医疗产业的诈骗，并颁布了广泛的立法。此外，这些监管措施也推动了进阶分析解决方案的采用，这些措施需要更高的透明度、资料保护和诈骗侦测功能。

免费客製化服务：

订阅此报告的客户可以利用以下免费自订选项之一：

  • 公司简介
    • 其他市场参与企业的综合分析（最多 3 家公司）
    • 主要企业SWOT分析（最多3家企业）
  • 区域分割
    • 根据客户兴趣对主要国家的市场估计/预测/复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 资料分析
    • 资料检验
    • 研究途径
  • 研究资讯来源
    • 主要研究资讯来源
    • 二次研究资讯来源
    • 先决条件

第三章市场趋势分析

  • 介绍
  • 促进因素
  • 抑制因素
  • 机会
  • 威胁
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • 新型冠状病毒感染疾病（COVID-19）的影响

第4章波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争公司之间的敌对关係

第五章全球医疗诈欺分析市场：依解决方案类型

  • 介绍
  • 预测分析
  • 规范分析
  • 说明分析
  • 其他类型的解决方案

第六章全球医疗诈欺分析市场：依发展划分

  • 介绍
  • 云端基础
  • 本地

第七章全球医疗诈欺分析市场：依应用分类

  • 介绍
  • 付款诚信
  • 药局申请问题
  • 保险申请审查
    • 预付款筛检
    • 后付费考试
  • 其他用途

第八章全球医疗诈欺分析市场：依最终用户分类

  • 介绍
  • 第三方服务供应商
  • 私人保险付款人
  • 公共机构和政府机构
  • 其他最终用户

第九章全球医疗诈欺分析市场：按地区

  • 介绍
  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 欧洲其他地区
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 中东和非洲其他地区

第10章 主要进展

  • 合约、伙伴关係、协作和合资企业
  • 收购和合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十一章 公司简介

  • Conduent Inc
  • Cotiviti Inc
  • DXC Technology
  • EXL Service Holdings Inc
  • HCL Technologies Limited
  • IBM
  • Optum Inc.
  • OSP Labs
  • SAS Institute Inc
  • Wipro Limited
Product Code: SMRC24499

According to Stratistics MRC, the Global Healthcare Fraud Analytics Market is accounted for $2.3 billion in 2023 and is expected to reach $10.9 billion by 2030 growing at a CAGR of 24.7% during the forecast period. The term "Healthcare Fraud Analytics Market" describes the emerging segment of the healthcare business that uses cutting-edge technology and analytics to detect, prevent, and lessen fraudulent activity. Robust fraud detection procedures are becoming more and more necessary as the healthcare landscape grows more complicated and involves a growing amount of data generated from several sources, such as electronic health records, billing systems, and claims.

According to the OIG, Medicaid data is frequently incomplete and inaccurate, affecting the process of detecting fraudulent claims and resulting in the waste of billions of dollars due to FWA.

Market Dynamics:

Driver:

Increasing adoption of electronic health records

There are both potential and challenges when healthcare systems move to digital platforms and make enormous volumes of patient data available. The use of electronic health records (EHRs) makes it possible to create a more extensive and centralized database of medical records, which offers an opportunity for fraud. Additionally, in order to prevent this, healthcare institutions are using advanced analytics tools to closely examine electronic health data in order to search for irregularities and trends that may indicate fraud.

Restraint:

Complexity of integration

The integration of advanced fraud analytics systems into pre-existing healthcare infrastructures is a common implementation task that can be complex and time-consuming. The complexity is increased by different information formats, inconsistent standards among healthcare institutions, and compatibility problems with outdated systems. It is difficult to achieve seamless integration when dealing with institutions that have diverse IT systems, as it is necessary to ensure efficient data flow and real-time analysis. However, staff members used to traditional workflows may oppose healthcare providers and cause operational interruptions.

Opportunity:

Advancements in technology

The healthcare sector's ability to prevent fraud has been transformed by the ongoing development of analytical tools, machine learning algorithms, and artificial intelligence. These technological advancements process enormous volumes of healthcare data in real time, enabling more complex and effective fraud detection techniques. Advanced analytics improve the accuracy and speed of fraud detection by detecting complex patterns, anomalies, and suspicious measures. Moreover, by incorporating cutting-edge technologies, healthcare companies may minimize financial losses and maintain the integrity of their systems while staying ahead of ever more sophisticated fraud schemes.

Threat:

Data security and privacy concerns

Concerns regarding security breaches and privacy violations are raised by the management of enormous amounts of sensitive patient data, which is a concern for healthcare companies as they use advanced analytics to combat fraud in increasing numbers. Because the healthcare industry is heavily regulated, there is a significant risk of unauthorized access, data leaks, or cyberattacks. Achieving a complicated problem requires strict compliance with privacy rules such as HIPAA (Health Insurance Portability and Accountability Act) while also collecting important insights from patient data in an equitable manner.

COVID-19 Impact:

Fraud analytics solutions are more important than ever because of the growing pressure on healthcare systems throughout the world to allocate resources efficiently and prevent fraud. On the other hand, the epidemic has also caused disruptions in the healthcare system, diverting resources and rapid attention to remedies. The quick adoption of new healthcare services and the surge in transactions associated with COVID-19 have made fraud detection systems more challenging. Furthermore, the pandemic's economic effects could promote further false claims.

The predictive analytics segment is expected to be the largest during the forecast period

Predictive analytics segment is expected to be the largest during the forecast period. Predictive analytics analyzes prior information, identifies trends, and projects future fraudulent activity using sophisticated algorithms and machine learning models. Healthcare businesses can prevent financial losses and safeguard the integrity of healthcare systems by adopting a proactive approach and staying ahead of emerging fraud schemes. Furthermore, predictive analytics improves the effectiveness of fraud detection by analyzing large datasets in real time and increasing the accuracy of spotting suspicious behavior while reducing false positives.

The pharmacy billing issue segment is expected to have the highest CAGR during the forecast period

Pharmacy billing issue segment is expected to have the highest CAGR. Pharmacy billing problems, like overbilling, unbundling, or charging for fraudulent prescriptions, have emerged as major avenues for fraud in the healthcare industry. The need for specialist analytics solutions designed to identify anomalies and discrepancies in pharmacy billing data has increased due to the rise in these fraudulent activities. Real-time fraud analytics tools such as predictive modeling and machine learning algorithms are being used to examine pharmacy billing transactions.

Region with largest share:

Due to the region's rapid modernization and digital transformation, many of its nations have adopted electronic health records (EHRs) and other digital health technologies, the Asia-Pacific area accounted for the largest percentage. Healthcare payers and providers in Asia Pacific are investing in advanced analytics solutions as a result of rising healthcare costs and growing penalties associated with fraud. In addition, there is an apparent rise in regulatory actions in the Asia-Pacific area that are intended to improve accountability and transparency in healthcare systems.

Region with highest CAGR:

Because of the complex healthcare infrastructure and sophisticated reimbursement system, the North American region is better positioned to continue profitable expansion. Because of the growing financial damage that healthcare fraud causes, regulatory agencies have enacted extensive laws, such as the False Claims Act and the Health Insurance Portability and Accountability Act (HIPAA) in the United States, to prevent fraud in the healthcare industry. Moreover, the adoption of advanced analytics solutions is urged by these regulatory measures, which need more transparency, data protection, and fraud detection capabilities.

Key players in the market:

Some of the key players in Healthcare Fraud Analytics market include Conduent Inc, Cotiviti Inc, DXC Technology, EXL Service Holdings Inc, HCL Technologies Limited, IBM, Optum Inc., OSP Labs, SAS Institute Inc and Wipro Limited.

Key Developments:

In November 2023, IBM launches new sustainability initiatives for global climate action. IBM's operations span a broad spectrum of technological fields, from AI and cloud computing to cybersecurity and data analytics.

In July 2023, HCLTech, the third largest IT services company in India, has acquired a 100 per cent equity stake in German automotive engineering services provider ASAP Group for €251 million ($279.72 million).

Solution Types Covered:

  • Predictive Analytics
  • Prescriptive Analytics
  • Descriptive Analytics
  • Other Solution Types

Deployments Covered:

  • Cloud-Based
  • On-Premises

Applications Covered:

  • Payment Integrity
  • Pharmacy Billing Issue
  • Insurance Claims Review
  • Other Applications

End Users Covered:

  • Third Party Service Providers
  • Private Insurance Payers
  • Public & Government Agencies
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2021, 2022, 2023, 2026, and 2030
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Healthcare Fraud Analytics Market, By Solution Type

  • 5.1 Introduction
  • 5.2 Predictive Analytics
  • 5.3 Prescriptive Analytics
  • 5.4 Descriptive Analytics
  • 5.5 Other Solution Types

6 Global Healthcare Fraud Analytics Market, By Deployment

  • 6.1 Introduction
  • 6.2 Cloud-Based
  • 6.3 On-Premises

7 Global Healthcare Fraud Analytics Market, By Application

  • 7.1 Introduction
  • 7.2 Payment Integrity
  • 7.3 Pharmacy Billing Issue
  • 7.4 Insurance Claims Review
    • 7.4.1 Prepayment Review
    • 7.4.2 Postpayment Review
  • 7.5 Other Applications

8 Global Healthcare Fraud Analytics Market, By End User

  • 8.1 Introduction
  • 8.2 Third Party Service Providers
  • 8.3 Private Insurance Payers
  • 8.4 Public & Government Agencies
  • 8.5 Other End Users

9 Global Healthcare Fraud Analytics Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 Conduent Inc
  • 11.2 Cotiviti Inc
  • 11.3 DXC Technology
  • 11.4 EXL Service Holdings Inc
  • 11.5 HCL Technologies Limited
  • 11.6 IBM
  • 11.7 Optum Inc.
  • 11.8 OSP Labs
  • 11.9 SAS Institute Inc
  • 11.10 Wipro Limited

List of Tables

  • Table 1 Global Healthcare Fraud Analytics Market Outlook, By Region (2021-2030) ($MN)
  • Table 2 Global Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
  • Table 3 Global Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
  • Table 4 Global Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
  • Table 5 Global Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
  • Table 6 Global Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
  • Table 7 Global Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
  • Table 8 Global Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
  • Table 9 Global Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
  • Table 10 Global Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
  • Table 11 Global Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
  • Table 12 Global Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
  • Table 13 Global Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
  • Table 14 Global Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
  • Table 15 Global Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
  • Table 16 Global Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
  • Table 17 Global Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
  • Table 18 Global Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
  • Table 19 Global Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
  • Table 20 Global Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
  • Table 21 Global Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
  • Table 22 North America Healthcare Fraud Analytics Market Outlook, By Country (2021-2030) ($MN)
  • Table 23 North America Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
  • Table 24 North America Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
  • Table 25 North America Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
  • Table 26 North America Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
  • Table 27 North America Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
  • Table 28 North America Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
  • Table 29 North America Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
  • Table 30 North America Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
  • Table 31 North America Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
  • Table 32 North America Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
  • Table 33 North America Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
  • Table 34 North America Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
  • Table 35 North America Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
  • Table 36 North America Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
  • Table 37 North America Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
  • Table 38 North America Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
  • Table 39 North America Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
  • Table 40 North America Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
  • Table 41 North America Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
  • Table 42 North America Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
  • Table 43 Europe Healthcare Fraud Analytics Market Outlook, By Country (2021-2030) ($MN)
  • Table 44 Europe Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
  • Table 45 Europe Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
  • Table 46 Europe Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
  • Table 47 Europe Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
  • Table 48 Europe Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
  • Table 49 Europe Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
  • Table 50 Europe Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
  • Table 51 Europe Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
  • Table 52 Europe Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
  • Table 53 Europe Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
  • Table 54 Europe Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
  • Table 55 Europe Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
  • Table 56 Europe Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
  • Table 57 Europe Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
  • Table 58 Europe Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
  • Table 59 Europe Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
  • Table 60 Europe Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
  • Table 61 Europe Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
  • Table 62 Europe Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
  • Table 63 Europe Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
  • Table 64 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Country (2021-2030) ($MN)
  • Table 65 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
  • Table 66 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
  • Table 67 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
  • Table 68 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
  • Table 69 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
  • Table 70 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
  • Table 71 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
  • Table 72 Asia Pacific Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
  • Table 73 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
  • Table 74 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
  • Table 75 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
  • Table 76 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
  • Table 77 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
  • Table 78 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
  • Table 79 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
  • Table 80 Asia Pacific Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
  • Table 81 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
  • Table 82 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
  • Table 83 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
  • Table 84 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
  • Table 85 South America Healthcare Fraud Analytics Market Outlook, By Country (2021-2030) ($MN)
  • Table 86 South America Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
  • Table 87 South America Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
  • Table 88 South America Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
  • Table 89 South America Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
  • Table 90 South America Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
  • Table 91 South America Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
  • Table 92 South America Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
  • Table 93 South America Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
  • Table 94 South America Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
  • Table 95 South America Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
  • Table 96 South America Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
  • Table 97 South America Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
  • Table 98 South America Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
  • Table 99 South America Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
  • Table 100 South America Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
  • Table 101 South America Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
  • Table 102 South America Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
  • Table 103 South America Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
  • Table 104 South America Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
  • Table 105 South America Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
  • Table 106 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Country (2021-2030) ($MN)
  • Table 107 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
  • Table 108 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
  • Table 109 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
  • Table 110 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
  • Table 111 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
  • Table 112 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
  • Table 113 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
  • Table 114 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
  • Table 115 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
  • Table 116 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
  • Table 117 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
  • Table 118 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
  • Table 119 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
  • Table 120 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
  • Table 121 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
  • Table 122 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
  • Table 123 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
  • Table 124 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
  • Table 125 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
  • Table 126 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)