Healthcare Fraud Detection market Size, Share, Potential Growth, Demand And Analysis Of Key Players- Research Forecasts To 2028

 

Healthcare fraud detection market has been expanding due to the increasing incidence of healthcare fraud that has led to greater burdens for the healthcare industry as well as reimbursement infrastructure. The fraud essentially involves misrepresentation and intentional submission of false claims. For instance, a fraud physician, in alliance with a pharmacy can add more expensive medicines to a prescription without the knowledge of the patient. National Healthcare Anti-Fraud Association opines that most of such frauds are committed by a small number of healthcare providers and mostly by organized crime groups.

According to experts, though the size of the U.S. healthcare industry is nearly $2.7 trillion, much of the revenue is wasted through mismanagement and fraud. Some of the common fraudulent behaviors include illegal medical billing practices that falsify claims, claiming of multiple claims by different providers for the same patient, stealing of patient identities to gain reimbursement for medical services, patients and dishonest providers coming together to make false claims and sharing the monetary gains. Apparently, fraudulent billing leads to nearly 3%-10% of annual healthcare costs in the U.S. To restrain this increasing tendency for healthcare fraud, government as well as private agencies are resorting to solutions based on AI and predictive analysis that is expected to add impetus to Healthcare Fraud Detection market Size. The global healthcare fraud detection market is expected to register a CAGR of 28.83% to reach USD 3,787.68 million by 2024.

Competitive Landscape:

Some of the significant fraud detection companies include

·         IBM,

·         DXC Technology Company,

·         FAIR ISAAC Corporation,

·         UNITEDHEALTH group,

·         WIPRO LIMITED,

·         LEXISNEXIS,

·         EXLSERVICE Holdings,

·         McKesson Corporation, Inc.,

·         SAS Institute Inc.,

·         CGI INC. and

·         COTIVITI INC.

Different segments of healthcare fraud detection market and growth implications:

The healthcare fraud detection market has been segmented into type, component, application, delivery model and end user.

·         Healthcare fraud detection market classification on the basis of descriptive analytics, prescriptive analytics and predictive analytics.

·         These methods are used to mitigate various types of healthcare frauds. For instance, descriptive analytics analyzes historical data to scrutinize the changes. It reflects total revenue generated per patient, monthly sales growth and yearly pricing changes, thus precise maintenance of related records. Since the information can analyze the revenue cycle it is considered an efficient means of healthcare fraud.

·         Predictive analytics is yet another type of fraud detection technique that is built upon past data which includes fraud or non-fraud indicators as well as different elements such as bill amount, number of patients, treatment characteristics, years of experience of the doctor, reporting lags and the number of patient visits.

·         On the basis of component, the market has been bifurcated into services and software. By application healthcare fraud detection market is classified as payment integrity and insurance claims review.

·         End-user classification of healthcare fraud detection market comprises public or government agencies, private insurance payers and third party service providers.

North America to hold a significant share in healthcare fraud detection market

Healthcare fraud detection market has been classified geographically as the Americas, Europe, Asia Pacific and the Middle East & Africa.

The Americas accounted for a market share of 49.97% in 2018. Healthcare fraud has been rampant in the U.S. and recently the nation’s federal authorities have reported on breaking up a $1.2 billion Medicare scam through which fraudsters were peddling orthodontic braces to senior patients irrespective of whether they needed it. Apparently the scam was spread over various continents. Officials had been able to crackdown on the ring of fraudsters with the use of techniques used by credit card companies. As a result, the healthcare fraud detection market in North America has been growing at a significant pace.

Combating healthcare scams to receive increased priority among public and private organizations

Recently, Centers for Medicare and Medicaid Services or CMS has submitted a RFI (Request for Information) to analyze how AI can help in enhancement of services. CMS aims to identify and prevent fraud, waste, and abuse and hopes that AI as well as other technologies can be leveraged to that end. CMS wants to conduct program integrity activities, reduce provider burden and to ensure proper claims payment.

AI technology can be utilized to detect fraud much faster than other conventional methods. Studies indicate that nearly $20 to $ 30 billion can be saved by US health insurance companies by avoiding waste through fraud. CMS is endeavoring to stop fraud before payment is made rather than the traditional pay and chase method used by government bodies.

Table Of Content:

1 EXECUTIVE SUMMARY

1.1 GLOBAL HEALTHCARE FRAUD DETECTION MARKET, BY TYPE 19

1.2 GLOBAL HEALTHCARE FRAUD DETECTION MARKET, BY COMPONENT 20

1.3 GLOBAL HEALTHCARE FRAUD DETECTION MARKET, BY DELIVERY MODEL 21

1.4 GLOBAL HEALTHCARE FRAUD DETECTION MARKET, BY APPLICATION 22

1.5 GLOBAL HEALTHCARE FRAUD DETECTION MARKET, BY END USER 23

2 MARKET INTRODUCTION

2.1 DEFINITION 24

2.2 SCOPE OF THE STUDY 24

2.3 RESEARCH OBJECTIVE 24

2.4 MARKET STRUCTURE 25

3 RESEARCH METHODOLOGY

3.1 RESEARCH PROCESS 26

3.2 PRIMARY RESEARCH 27

3.3 SECONDARY RESEARCH 28

3.4 MARKET SIZE ESTIMATION 29

3.5 FORECAST MODEL 30

4 MARKET DYNAMICS

4.1 INTRODUCTION 31

4.2 DRIVERS 32

4.2.1 INCREASE IN THE NUMBER OF FRAUDULENT ACTIVITIES IN HEALTHCARE 32

4.2.2 THE RISING NUMBER OF PATIENTS OPTING FOR HEALTH INSURANCE 32

4.2.3 THE ESCALATION IN HEALTHCARE EXPENDITURE 32

4.3 RESTRAINTS 33

4.3.1 UNWILLINGNESS TO ADOPT HEALTHCARE FRAUD ANALYTICS IN DEVELOPING REGIONS 33

4.4 OPPORTUNITIES 33

4.4.1 AI IN HEALTHCARE FRAUD DETECTION 33

5 MARKET FACTOR ANALYSIS

5.1 VALUE CHAIN ANALYSIS 34

5.1.1 INPUTS 34

5.1.2 SOFTWARE DEVELOPMENT PROCESSES 35

5.1.3 OUTPUT 35

5.1.4 MARKETING AND DISTRIBUTION 35

5.2 PORTER’S FIVE FORCES MODEL 35

5.2.1 BARGAINING POWER OF SUPPLIERS 36

5.2.2 BARGAINING POWER OF BUYERS 36

5.2.3 THE THREAT OF NEW ENTRANTS 36

5.2.4 THREAT OF SUBSTITUTES 37

5.2.5 INTENSITY OF RIVALRY 37

 

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