Medical Payment Fraud Detection Market Research Report - Forecast till 2027

Medical Payment Fraud Detection Market: By Type (Descriptive Analytics, Predictive Analytics and Prescriptive Analytics), Component (Services and Software), Delivery model (On-Premise and Cloud-Based), Source of service (In-House and Outsourced), End User (Private Insurance Payers, Public/ Government Agencies and Third-Party Service Providers) and Region (Americas, Europe, Asia-Pacific and the Middle East and Africa) - Forecast to 2027

ID: MRFR/MED/8300-HCR | February, 2021 | Region : Global

Medical Payment Fraud Detection Market Forecast


Medical Payment Fraud Detection Market is expected to hold a value of USD 5786.44 Million by 2027 and medical payment fraud detection market is expected to register a growth of 25.2% from 2020 to 2027.


Market Synopsis


Medical fraud is increasingly apperceived as one of the social concerns. Healthcare fraud varies, but generally, it involves filing dishonest health claims for profit. The combination of fear loosened healthcare regulations, and expected stimulus payments in response to the COVID 19 pandemic could unleash an unprecedented surge of healthcare scams and fraud. The rising number of patients opting for health insurance, increasing pressure of fraud, and abuse on healthcare spending are the key factors that are expected to drive the medical payment fraud detection market growth. For instance, according to the Centers for Disease Control and Prevention, 2018, report, 65.1% of people under the age of 65, in the US opted for private healthcare insurance. Moreover, a large number of fraudulent activities, rising healthcare expenditures, and growing pressure to increase operational efficiency and reduce healthcare spending are also expected to boost medical payment fraud detection market growth.


Market Influencer


An outbreak of COVID 19, leading to increased healthcare scams and fraud.


Medical Payment Fraud Detection Market Drivers



  • Payments in response to the COVID 19 pandemic could unleash an unpredicted surge of healthcare scams and frauds.



  • Rising number of patients opting for health insurance. Health insurance provides people with a much needed financial backlog at the time of medical emergencies. One of the ways to be financially prepared against uncertain health risks is by buying health insurance.



  • Large number of fraudulent activities. In 2018, USD 3.6 trillion was spent on healthcare in the US, representing billions of health insurance claims. It was an undisputed reality that some of these claims were fraudulent.



  • Increasing pressure of fraud and abuse on healthcare spending.



  • Rising healthcare expenditure. According to the World Health Organization (WHO) report, 2016, the rise in healthcare expenditure in low and middle-income countries was approximately 6% per annum as compared to 4% in high-income countries.



  • Growing pressure to increase operational efficiency and reduce healthcare spending.


Market Restraints



  • Lack of skilled professionals.

  • Unwillingness to adapt the fraud analytic system in developing countries.


Medical Payment Fraud Detection Market Segmentation


By Type



  • Descriptive Analytics: The descriptive analytics segment is the largest growing segment, as it forms the base for the effective application of predictive and prescriptive analytics. Furthermore, this type of analytics uses historical data to analyze the changes. Hence, it is used to analyze the revenue during a certain period.



  • Predictive Analytics: This identifies patterns that are potentially fraudulent and then develops sets of rules and flag certain claims.



  • Prescriptive Analytics: The prescriptive analytics segment is the fastest-growing segment owing to the high demand for technology. Moreover, this enables medical decision-makers to optimize business outcomes by recommending the best course of action for patients or providers. It estimates the likelihood of a future outcome based on patterns in the historical data.


By Component



  • Services: This segment holds the largest market share owing to a wide area of application such as monitoring transactions, medical payment patient check-in, insurance eligibility and verification, medical coding of diagnosis, procedures, and modifiers, charge entry, claims submission, and payment posting.



  • Software: This segment is the fastest-growing segment due to a rise in system integration, processing services, outsourcing, and packaged software support and installations with security measures. Moreover, medical payment through software application improves reimbursement rates, optimize revenue, and sustain the financial health of businesses.


By Delivery Model



  • On-premise: This stores all data on a physical server that are on respective medical site, and server management either remain own responsibility or outsourced to an IT provider.



  • Cloud-based: The factor such as the advancement of technology and the development of various artificial intelligence techniques leads the cloud-based segment as the largest and fastest-growing segment during the forecast period. This stores all information on the internet ‘the cloud’, which is then backed up by premium security data centers.


By Source of Service



  • In-house: Staff of the clinic or health department is responsible for all aspects of revenue cycle management. They submit claims to a clearinghouse or the insurance company for reimbursement, set charges, collect patient fees, and manages the account receivables.



  • Outsourced: Providers may outsource their medical payment to a third party known as a medical billing service. These billing services typically take a percentage of a practice’s collections as payment for managing many aspects of the clinic's revenue cycle management.


By End-User



  • Private Insurance Payers: Private payers are insurance companies that offer different types of plans that must meet or exceed basic standards.



  • Public/Government Agencies: Federal and state agencies that reimburse healthcare providers can significantly enhance their risk modeling and fraud collection.



  • Third-Party Service Providers: Third-party service providers have the responsibility of managing claims, getting reimbursement from the insurance company, and paying the healthcare provider.


By Region



  • Americas: Review of insurance claims plays a major role in healthcare fraud detection. As per the estimates of the National Healthcare Anti-Fraud Association (NHCAA), Healthcare fraud costs the US around USD 68 billion annually. Hence, healthcare insurance fraud detection leads to drive the medical payment fraud detection market.



  • Europe: Europe holds the second-largest market. According to the Organization for Economic Cooperation and Development (OECD) report, 2018, over 75% of health spending was financed through government and compulsory insurance across European countries. Rising healthcare expenditure is expected to boost the medical payment fraud detection market growth.



  • Asia-Pacific: Asia-Pacific is expected to be the fastest-growing region. According to the Asian Hospital and Healthcare Management report, the private health insurance market is projected to continue expanding with almost 3 million actively seek health insurance by end of 2020, and that number is projected to continue increasing. Moreover, a rising number of patients are opting for healthcare insurance which is expected to increase the medical payment fraud detection market growth during the forecast period.



  • Middle East and Africa: The medical payment fraud detection market is expected for remarkable growth in the development of healthcare services. Rise in healthcare expenditure and growing pressure to increase operational efficiency are expected to boost the medical payment fraud detection market growth.


Medical Payment Fraud Detection Market  Key Players



  • LexisNexis Risk Solutions

  • International Business Machines Corporation

  • Optuminsight

  • OSP Labs

  • DXC Technology Company

  • Unitedhealth Group

  • SAS Institute

  • Fair Isaac Corporation

  • EXL Service Holdings, Inc.

  • CGI GROUP

Table of Contents

1. EXECUTIVE SUMMARY

1.1. Market Attractiveness Analysis

1.1.1. Global Medical Payment Fraud Detection Market, by Type

1.1.2. Global Medical Payment Fraud Detection Market, by Component

1.1.3. Global Medical Payment Fraud Detection Market, by Delivery Model

1.1.4. Global Medical Payment Fraud Detection Market, by Source of Services

1.1.5. Global Medical Payment Fraud Detection Market, by End User

2. MARKET INTRODUCTION

2.1. Definition

2.2. Scope of the Study

2.2.1. Research Objective

2.2.2. Assumptions

2.2.3. Limitations

3. RESEARCH METHODOLOGY

3.1. Overview

3.2. Data Mining

3.3. Secondary Research

3.4. Primary Research

3.4.1. Breakdown of Primary Respondents

3.5. Forecasting Mode

3.6. Research Methodology for Market Size Estimation

3.6.1. Bottom-Up Approach

3.6.2. Top-Down Approach

3.7. Data Triangulation

3.8. Validation

4. MARKET DYNAMICS

4.1. Overview

4.2. Drivers

4.3. Restraints

4.4. Opportunities

5. MARKET FACTOR ANALYSIS

5.1. Porter’s Five Forces Analysis

5.1.1. Bargaining Power of Suppliers

5.1.2. Bargaining Power of Buyers

5.1.3. Threat of New Entrants

5.1.4. Threat of Substitutes

5.1.5. Intensity of Rivalry

5.2. Value Chain Analysis

5.2.1. R&D and Designing

5.2.2. Manufacturing

5.2.3. Distribution & Sales

5.2.4. Post Sales Type

5.3. Impact of Coronavirus (COVID-19) on Global Medical Payment Fraud Detection Market

5.3.1. Demand-Supply Analysis

5.3.2. Capital Expenditure

5.3.3. Pricing Analysis

5.3.4. Consumption Analysis

5.3.5. Impact on Key Players

5.3.6. Role of Government

6. GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET, BY TYPE

6.1. Overview

6.2. Descriptive Analytics

Market Estimates & Forecast, by Region, 2017–2027

Market Estimates & Forecast, by Country, 2017–2027

6.3. Predictive Analytics

Market Estimates & Forecast, by Region, 2017–2027

Market Estimates & Forecast, by Country, 2017–2027

6.4. Prescriptive Analytics

Market Estimates & Forecast, by Region, 2017–2027

Market Estimates & Forecast, by Country, 2017–2027

7. GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET, BY COMPONENT

7.1. Overview

7.2. Services

Market Estimates & Forecast, by Region, 2017–2027

Market Estimates & Forecast, by Country, 2017–2027

7.3. Software

Market Estimates & Forecast, by Region, 2017–2027

Market Estimates & Forecast, by Country, 2017–2027

8. GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET, BY DELIVERY MODEL

8.1. Overview

8.2. On-Premise

Market Estimates & Forecast, by Region, 2017–2027

Market Estimates & Forecast, by Country, 2017–2027

8.3. Cloud-Based

Market Estimates & Forecast, by Region, 2017–2027

Market Estimates & Forecast, by Country, 2017–2027

9. GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET, BY SOURCE OF SERVICES

9.1. Overview

9.2. In-house

Market Estimates & Forecast, by Region, 2017–2027

Market Estimates & Forecast, by Country, 2017–2027

9.3. Outsourced

Market Estimates & Forecast, by Region, 2017–2027

Market Estimates & Forecast, by Country, 2017–2027

10. GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET, BY END USER

10.1. Overview

10.2. Private Insurance Payers

Market Estimates & Forecast, by Region, 2017–2027

Market Estimates & Forecast, by Country, 2017–2027

10.3. Public/Government Agencies

Market Estimates & Forecast, by Region, 2017–2027

Market Estimates & Forecast, by Country, 2017–2027

10.4. Third-Party Service Providers

Market Estimates & Forecast, by Region, 2017–2027

Market Estimates & Forecast, by Country, 2017–2027

11. GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET, BY REGION

11.1. Overview

11.2. Americas

11.2.1. North America

11.2.1.1. US

11.2.1.2. Canada

11.2.2. Latin America

11.3. Europe

11.3.1. Western Europe

11.3.1.1. Germany

11.3.1.2. France

11.3.1.3. Italy

11.3.1.4. Spain

11.3.1.5. UK

11.3.1.6. Rest of Western Europe

11.3.2. Eastern Europe

11.4. Asia-Pacific

11.4.1. Japan

11.4.2. China

11.4.3. India

11.4.4. Australia

11.4.5. South Korea

11.4.6. Rest of Asia-Pacific

11.5. Middle East & Africa

11.5.1. Middle East

11.5.2. Africa

12. COMPANY LANDSCAPE

12.1. Overview

12.2. Competitor Dashboard

12.3. Major Growth Strategy in the Global Medical Payment Fraud Detection Market

12.4. Competitive Benchmarking

12.5. The Leading Player in terms of Number of Developments in the Global Medical Payment Fraud Detection Market

12.6. Key Developments & Growth Strategies

12.6.1. New Product Launch/Service Deployment

12.6.2. Merger & Acquisition

12.6.3. Joint Ventures

12.7. Major Players Financial Matrix & Market Ratio

12.7.1. Sales & Operating Income 2018

12.7.2. Major Players R&D Expenditure 2018

13. COMPANY PROFILES

13.1. LexisNexis Risk Solutions

13.1.1. Company Overview

13.1.2. Products/Type Offered

13.1.3. Financial Overview

13.1.4. Key Developments

13.1.5. SWOT Analysis

13.1.6. Key Strategies

13.2. International Business Machines Corporation

13.3. Optuminsight

13.4. OSP Labs

13.5. DXC Technology Company

13.6. Unitedhealth Group

13.7. SAS Institute

13.8. Fair Isaac Corporation

13.9. EXL Service Holdings, Inc.

13.10. CGI GROUP

13.11. Others

14. APPENDIX

14.1. References

14.2. Related Reports

NOTE:

This table of content is tentative and subject to change as the research progresses.

  In section 13, only the top companies will be profiled. Each company will be profiled based on the Market Overview, Financials, Product Portfolio, Business Strategies, and Recent Developments parameters.

  Please note: The financial details of the company cannot be provided if the information is not available in the public domain and or from reliable sources.

LIST OF TABLES

TABLE 1 GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET SYNOPSIS, 2017–2027

TABLE 2 GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET ESTIMATES & FORECAST, 2017–2027 (USD MILLION)

TABLE 3 GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET, BY TYPE, 2017–2027 (USD MILLION)

TABLE 4 GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET, BY COMPONENT, 2017–2027 (USD MILLION)

TABLE 5 GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET, BY DELIVERY MODEL, 2017–2027 (USD MILLION)

TABLE 6 GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET, BY SOURCE OF TYPE, 2017–2027 (USD MILLION)

TABLE 7 GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET, BY END USER, 2017–2027 (USD MILLION)

TABLE 8 GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET, BY REGION, 2017–2027 (USD MILLION)

TABLE 9 NORTH AMERICA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY TYPE, 2017–2027 (USD MILLION)

TABLE 10 NORTH AMERICA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY COMPONENT, 2017–2027 (USD MILLION)

TABLE 11 NORTH AMERICA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY DELIVERY MODEL, 2017–2027 (USD MILLION)

TABLE 12 NORTH AMERICA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY SOURCE OF TYPE, 2017–2027 (USD MILLION)

TABLE 13 NORTH AMERICA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY END USER, 2017–2027 (USD MILLION)

TABLE 14 US: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY TYPE, 2017–2027 (USD MILLION)

TABLE 15 US: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY COMPONENT, 2017–2027 (USD MILLION)

TABLE 16 US: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY DELIVERY MODEL, 2017–2027 (USD MILLION)

TABLE 17 US: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY SOURCE OF TYPE, 2017–2027 (USD MILLION)

TABLE 18 US: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY END USER, 2017–2027 (USD MILLION)

TABLE 19 CANADA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY TYPE, 2017–2027 (USD MILLION)

TABLE 20 CANADA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY COMPONENT, 2017–2027 (USD MILLION)

TABLE 21 CANADA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY DELIVERY MODEL, 2017–2027 (USD MILLION)

TABLE 22 CANADA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY SOURCE OF TYPE, 2017–2027 (USD MILLION)

TABLE 23 CANADA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY END USER, 2017–2027 (USD MILLION)

TABLE 24 LATIN AMERICA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY TYPE, 2017–2027 (USD MILLION)

TABLE 25 LATIN AMERICA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY COMPONENT, 2017–2027 (USD MILLION)

TABLE 26 LATIN AMERICA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY DELIVERY MODEL, 2017–2027 (USD MILLION)

TABLE 27 LATIN AMERICA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY SOURCE OF TYPE, 2017–2027 (USD MILLION)

TABLE 28 LATIN AMERICA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY END USER, 2017–2027 (USD MILLION)

TABLE 29 EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY TYPE, 2017–2027 (USD MILLION)

TABLE 30 EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY COMPONENT, 2017–2027 (USD MILLION)

TABLE 31 EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY DELIVERY MODEL, 2017–2027 (USD MILLION)

TABLE 32 EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY SOURCE OF TYPE, 2017–2027 (USD MILLION)

TABLE 33 EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY END USER, 2017–2027 (USD MILLION)

TABLE 34

TABLE 35 WESTERN EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY TYPE, 2017–2027 (USD MILLION)

TABLE 36 WESTERN EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY COMPONENT, 2017–2027 (USD MILLION)

TABLE 37

TABLE 38 WESTERN EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY DELIVERY MODEL, 2017–2027 (USD MILLION)

TABLE 39 WESTERN EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY SOURCE OF TYPE, 2017–2027 (USD MILLION)

TABLE 40 WESTERN EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY END USER, 2017–2027 (USD MILLION)

TABLE 41 EASTERN EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY TYPE, 2017–2027 (USD MILLION)

TABLE 42 EASTERN EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY COMPONENT, 2017–2027 (USD MILLION)

TABLE 43 EASTERN EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY DELIVERY MODEL, 2017–2027 (USD MILLION)

TABLE 44 EASTERN EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY SOURCE OF TYPE, 2017–2027 (USD MILLION)

TABLE 45 EASTERN EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY END USER, 2017–2027 (USD MILLION)

TABLE 46 ASIA-PACIFIC: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY TYPE, 2017–2027 (USD MILLION)

TABLE 47 ASIA-PACIFIC: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY COMPONENT, 2017–2027 (USD MILLION)

TABLE 48 ASIA-PACIFIC: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY DELIVERY MODEL, 2017–2027 (USD MILLION)

TABLE 49 ASIA-PACIFIC: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY SOURCE OF TYPE, 2017–2027 (USD MILLION)

TABLE 50 ASIA-PACIFIC: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY END USER, 2017–2027 (USD MILLION)

TABLE 51 MIDDLE EAST & AFRICA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY TYPE, 2017–2027 (USD MILLION)

TABLE 52 MIDDLE EAST & AFRICA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY COMPONENT, 2017–2027 (USD MILLION)

TABLE 53 MIDDLE EAST & AFRICA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY DELIVERY MODEL, 2017–2027 (USD MILLION)

TABLE 54 MIDDLE EAST & AFRICA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY SOURCE OF TYPE, 2017–2027 (USD MILLION)

TABLE 55 MIDDLE EAST & AFRICA: MEDICAL PAYMENT FRAUD DETECTION MARKET, BY END USER, 2017–2027 (USD MILLION)


LIST OF FIGURES

FIGURE 1 RESEARCH PROCESS

FIGURE 2 MARKET STRUCTURE FOR THE GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET

FIGURE 3 MARKET DYNAMICS FOR THE GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET

FIGURE 4 GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET SHARE, BY TYPE, 2018 (%)

FIGURE 5 GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET SHARE, BY COMPONENT, 2018 (%)

FIGURE 6 GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET SHARE, BY DELIVERY MODEL 2018 (%)

FIGURE 7 GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET SHARE, BY SOURCE OF TYPE, 2018 (%)

FIGURE 8 GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET SHARE, BY END USER, 2018 (%)

FIGURE 9 GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET SHARE, BY REGION, 2018 (%)

FIGURE 10 AMERICAS: MEDICAL PAYMENT FRAUD DETECTION MARKET SHARE BY REGION, 2018 (%)

FIGURE 11 NORTH AMERICA: MEDICAL PAYMENT FRAUD DETECTION MARKET SHARE, BY COUNTRY, 2018 (%)

FIGURE 12 EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET SHARE, BY REGION, 2018 (%)

FIGURE 13 WESTERN EUROPE: MEDICAL PAYMENT FRAUD DETECTION MARKET SHARE, BY COUNTRY, 2018 (%)

FIGURE 14 ASIA-PACIFIC: MEDICAL PAYMENT FRAUD DETECTION MARKET SHARE, BY COUNTRY, 2018 (%)

FIGURE 15 MIDDLE EAST & AFRICA: MEDICAL PAYMENT FRAUD DETECTION MARKET SHARE, BY COUNTRY, 2018 (%)

FIGURE 16 GLOBAL MEDICAL PAYMENT FRAUD DETECTION MARKET: COMPANY SHARE ANALYSIS, 2018 (%)

FIGURE 17 LEXISNEXIS RISK SOLUTIONS: KEY FINANCIALS

FIGURE 18 LEXISNEXIS RISK SOLUTIONS: SEGMENTAL REVENUE

FIGURE 19 LEXISNEXIS RISK SOLUTIONS: REGIONAL REVENUE

FIGURE 20 INTERNATIONAL BUSINESS MACHINES CORPORATION: KEY FINANCIALS

FIGURE 21 INTERNATIONAL BUSINESS MACHINES CORPORATION.: SEGMENTAL REVENUE

FIGURE 22 INTERNATIONAL BUSINESS MACHINES CORPORATION: REGIONAL REVENUE

FIGURE 23 OPTUMINSIGHT: KEY FINANCIALS

FIGURE 24 OPTUMINSIGHT: SEGMENTAL REVENUE

FIGURE 25 OPTUMINSIGHT: REGIONAL REVENUE

FIGURE 26 OSP LABS: KEY FINANCIALS

FIGURE 27 OSP LABS: SEGMENTAL REVENUE

FIGURE 28 OSP LABS: REGIONAL REVENUE

FIGURE 29 DXC TECHNOLOGY COMPANY: KEY FINANCIALS

FIGURE 30 DXC TECHNOLOGY COMPANY: SEGMENTAL REVENUE

FIGURE 31 DXC TECHNOLOGY COMPANY: REGIONAL REVENUE

FIGURE 32 UNITEDHEALTH GROUP: KEY FINANCIALS

FIGURE 33 UNITEDHEALTH GROUP: SEGMENTAL REVENUE

FIGURE 34 UNITEDHEALTH GROUP: REGIONAL REVENUE

FIGURE 35 SAS INSTITUTE: KEY FINANCIALS

FIGURE 36 SAS INSTITUTE: SEGMENTAL REVENUE

FIGURE 37 SAS INSTITUTE: REGIONAL REVENUE

FIGURE 38 FAIR ISAAC CORPORATION: KEY FINANCIALS

FIGURE 39 FAIR ISAAC CORPORATION: SEGMENTAL REVENUE

FIGURE 40 FAIR ISAAC CORPORATION: REGIONAL REVENUE

FIGURE 41 EXL SERVICE HOLDINGS, INC.: KEY FINANCIALS

FIGURE 42 EXL SERVICE HOLDINGS, INC.: SEGMENTAL REVENUE

FIGURE 43 EXL SERVICE HOLDINGS, INC.: REGIONAL REVENUE

FIGURE 44 CGI GROUP: KEY FINANCIALS

FIGURE 45 CGI GROUP: SEGMENTAL REVENUE

FIGURE 46 CGI GROUP: REGIONAL REVENUE