The global AI in fraud management market is expected to attain a valuation of US$ 10,437.3 million in 2023, and is projected to reach US$ 57,146.8 million by 2033. The market is expected to flourish at a CAGR of 18.5% from 2023 to 2033.
Increasing investments by key market players to introduce secure fraud solutions across industries such as manufacturing, BFSI, healthcare, and others are expected to fuel market growth. Governments and end-use enterprises worldwide are actively investing in advanced fraud prevention solutions. The market is also driven by the growing focus of end-users on e-commerce platforms. Prominent market players are employing diverse business strategies to expand their product offerings and capture market opportunities.
In October 2020, BAE Systems and Guidewire Software joined forces to launch and develop fraud prevention solutions by integrating NetReveal into Guidewire's ClaimCenter platform. The rise in online insurance claims has created opportunities for fraudulent activities in hospitals and government sectors. As a result, there is an increasing demand for solutions in the government and healthcare sectors to combat these fraudulent activities.
The increased adoption of online applications and mobile banking services has resulted in a surge of fake websites and mobile applications. This trend extends beyond the banking sector to industries like retail & e-commerce, manufacturing, and healthcare. These fraudulent websites and applications mimic legitimate retail stores and home delivery services, deceiving customers into engaging in fake online transactions. In the banking sector, customers heavily rely on mobile applications for tasks like online payments, statement reviews, lodging complaints, providing feedback, and more.
| Report Attribute | Details |
|---|---|
| Expected Market Value (2023) | US$ 10,437.3 million |
| Anticipated Forecast Value (2033) | US$ 57,146.8 million |
| Projected Growth Rate (2023 to 2033) | CAGR 18.5% |
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The AI in fraud management market was worth US$ 4.8 million in 2018. It accumulated a market value of US$ 8.9 million in 2022 while growing at a CAGR of 12.7% from 2023 to 2033.
Sophistication in financial crimes, cyberattacks, and digital frauds are challenging the growth of several businesses worldwide. Growing concerns regarding digital frauds, despite technological advancements facilitating the ease of payment options or data access, calls for the deployment of fraud detection solutions.
The increasing popularity of digital payment apps, cross-border transactions, and e-banking, the number of fraudulent cases involving data breaches, rising payment frauds, and identity thefts are likely to augment the demand for AI based fraud management solutions over the coming years.
The demand for AI in fraud management from the data science team is increasing significantly due to its application in enhancing security across several business sectors, including retail and financial, and others.
These advanced fraud management solutions use an AI-based detection technology assisted by human sciences and machine learning to address challenges like money laundering, reducing false alerts, and automating fintech investigations. The global AI in fraud management growth scenario is anticipated to witness an increase in revenue from US$ 10,437.3 million in 2023 to US$ 57,146.8 million by 2033.
Rapid development in technology is indicating that more business processes can now be automated. Initially this meant that machines and software can relieve workers of boring, routine tasks. Big data, machine learning and artificial intelligence are also making it possible to automate more complex tasks, but many of these projects either fail or fall short of expectations.
Digital transformation means that processes, prices and rules are changing faster than ever before. If they are to survive, businesses have to think and act with agility. To do this, they need to embrace the latest technology and develop the right mindset amongst their staff.
More than ever before, success depends on businesses being faster and more agile than their competitors. For example, online customers expect fast delivery of their goods or an instant quote when seeking to buy insurance. But providing this service usually involves some complex decisions about why a particular product or offer is suitable – or not.
Intelligent automation (IA) integrates every aspect of turning findings into actions. It combines human knowledge from subject matter experts with data-driven artificial intelligence and uses powerful automation software to enable instant action.
Principal Consultant
Lack of Skilled Professionals to Restrain Market Growth
Dearth of professionals and skilled workforce to update the fraud detection and prevention solutions across the developing countries is expected to restrain market growth during the forecast period.
North America is predicted to remain one of the most attractive markets during the forecast period. The region accumulated a revenue share of 29.4% in 2022. United States alone accounted for a revenue share of 20.9% in the same year. United States is expected to account for more than 85% of the North America share through 2033.
The United States is the largest market for AI in fraud management, due to the strong presence of AI-powered fraud management software and service providers, in the United States. This is attributed to the increase in demand for advanced fraud management solutions in various industries such as banking, financial services, and insurance (BFSI), consumer goods and retail, telecommunication, healthcare, and others.
The United States is the most affected nation across the globe by money laundering and terrorist financing crime activities. Hence, the demand for AI-based fraud management solutions would increase across the country, during the forecast period.
Demand for AI in fraud management platforms in the United Kingdom is expected to rise at an impressive 18.2% CAGR over the forecast period. The United Kingdom economy is increasingly powered by big data, platform business models, advanced analytics, smartphone technology and peer-to-peer networks. At the same time, innovation in the financial sector is dramatically changing the markets.
The demand for AI in fraud management solutions is growing in the United Kingdom due to the rise in network crimes and frauds and advanced cyber and bot attacks. The United Kingdom AI in fraud management market is witnessing significant growth opportunities due to the major players focusing on expanding their presence in various verticals, such as BFSI, telecommunication, retail, government/public sector, and manufacturing. Insurance frauds are the major issues faced by European countries.
Rising cases of money laundering and terrorist financing are considered primary threats in the United Kingdom because of which the European Banking Authority (EBA) has declared the fraud management to be the topmost priority for the EU in 2020.
The sales in India is estimated to increase at an impressive rate of around 19.4% CAGR between 2023 and 2033. The country is offering growth opportunities for the sales of AI in fraud management solutions, owing to the government’s policies related to financial and payment transactions and implications for international business.
Governments, banks, and financial institutes in India are facing fraud-related challenges, which are compelling them to adopt advanced technologies such as AI-based and machine learning approaches.
Demand for AI in fraud management solutions in China is estimated to total US$ 920 million by the end of 2023. The market in this region is expected to grow with a CAGR of 21.2% during the forecast period. In China, the market will gain from the penetration of smartphones and ecommerce boon, which also increased the threat of online and mobile fraud. Hence, the demand for sophisticated fraud preventive measures is on the rise.
China is a huge and growing market and card fraud, to date, has not been a major problem in relation to the value of transactions. Nevertheless, recently, Beijing prosecutors called on banks to review credit card applications more carefully and credit card fraud accounted for 88% of financial crime cases heard in Shanghai courts. The majority of these cases involved credit card fraud, ID theft, and malicious overdrafts.
As mobile and ecommerce platforms continue to grow at a rapid rate, so is the need for more advanced fraud management techniques, which will be critical for online merchants seeking to capitalize on the growing market of China.
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Based on software, AI-powered fraud prevention software accounted for a revenue share of 73.5% in 2022. It is expected to accumulate over 74% market share in 2023. This segment is anticipated to grow with a CAGR of 19.4% throughout the forecast period. AI based fraud management solutions can provide real-time screening of transactions and other confidential data related activities happening across channels, accounts, users, and processes.
Moreover, the vendors of AI in fraud management solutions are focusing on advancements in AI by the integration of machine learning. AI-powered fraud prevention software offers different features such as enhanced flexibility, complex digital fraud prevention, and AI-powered real-time monitoring systems. The reliability of such features drives the demand for AI in the fraud management solutions.
The identity theft protection application segment is projected to register growth at a CAGR of 20% over the coming years. Over the past few years, the world has witnessed several unexpected identity theft cases. These cyber-criminal acts have alarmed law enforcement agencies around the world and compelled them to implement strict rules and regulations.
Artificial intelligence can be combined with human intelligence to improve the verification process and make it effortless. Furthermore, machine learning can prove to be very competent when it comes to identity fraud prevention. Not only machine learning-based solutions are user-friendly but also capable of identifying the difference between good and bad IDs.
The large enterprises segment accounted for nearly 63.2% of the overall market share in 2022, and is expected to continue its dominance during the forecast period. The segment is expected to grow with a CAGR of 17.8% during the forecast period. This growth is attributable to the emerging trend of digitalization to adopt advanced and more sophisticated security software and applications.
Investments in deploying preventive measures are among critical business strategies undertaken to ensure organizational data security. Fraudulent activities ranging from money laundering and phishing to distributed denial-of-service are prevalent among large enterprises. Therefore, it is essential for large enterprises to adopt preventive fraud management solutions and services.
The BFSI segment is expected to contribute a revenue share of close to 25.4% in 2023 and is expected to maintain its dominance in the upcoming years owing to rapid digitization. Automation of operations in the sector have made the banking and financial services industry a popular target among cybercriminals.
The growing popularity of products, such as mutual funds, stockbroking, and insurance, among consumers to digitally access their bank accounts and complete transactions has fuelled the need for the adoption of preventive tools to track frauds and their activities.
Start-ups are crucial in identifying growth opportunities, including AI in fraud management market. They efficiently convert inputs to outputs and adapt to market changes, contributing to the industry's expansion. Some start-ups are expected to drive growth in the AI in fraud management market.
The AI in fraud management market is highly competitive, with several key industry players investing heavily in the production of these services.
The key industry players are IBM Corporation, Cognizant, Temenos AG, Capgemini SE, Subex Limited, JuicyScore, Hewlett Packard Enterprise, MaxMind, Inc., BAE Systems plc, Pelican, SAS Institute Inc., Splunk, Inc., DataVisor, Inc., Matellio Inc., ACTICO GmbH.
Some recent developments in the market are:
Key industry players are utilizing organic growth strategies like acquisition, mergers, tie-ups, and collaboration to bolster their product portfolio. This is expected to propel the global AI in fraud management market.
| Report Attribute | Details |
|---|---|
| Market Value in 2023 | US$ 10,437.3 million |
| Market Value in 2033 | US$ 57,146.8 million |
| Growth Rate | CAGR of 18.5% from 2023 to 2033 |
| Base Year for Estimation | 2022 |
| Historical Data | 2018 to 2022 |
| Forecast Period | 2023 to 2033 |
| Quantitative Units | Revenue in US$ million and CAGR from 2023 to 2033 |
| Report Coverage | Revenue Forecast, Volume Forecast, Company Ranking, Competitive Landscape, Growth Factors, Trends, and Pricing Analysis |
| Segments Covered |
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| Regions Covered |
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| Key Countries Profiled |
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| Key Companies Profiled |
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| Customization & Pricing | Available Upon Request |
The United States market will generate 85% revenue by 2033.
China may witness significant growth in the AI in fraud management market.
Shift and Owl technologies are expected to drive AI in fraud management sales.
Machine learning environments may drive market growth over the coming years.
The market recorded a CAGR of 12.7% in 2022.
1. Executive Summary
1.1. Global Market Outlook
1.2. Demand-side Trends
1.3. Supply-side Trends
1.4. Technology Roadmap Analysis
1.5. Analysis and Recommendations
2. Market Overview
2.1. Market Coverage / Taxonomy
2.2. Market Definition / Scope / Limitations
3. Market Background
3.1. Market Dynamics
3.1.1. Drivers
3.1.2. Restraints
3.1.3. Opportunity
3.1.4. Trends
3.2. Scenario Forecast
3.2.1. Demand in Optimistic Scenario
3.2.2. Demand in Likely Scenario
3.2.3. Demand in Conservative Scenario
3.3. Opportunity Map Analysis
3.4. Investment Feasibility Matrix
3.5. PESTLE and Porter’s Analysis
3.6. Regulatory Landscape
3.6.1. By Key Regions
3.6.2. By Key Countries
3.7. Regional Parent Market Outlook
4. Global Market Analysis 2018 to 2022 and Forecast, 2023 to 2033
4.1. Historical Market Size Value (US$ Million) Analysis, 2018 to 2022
4.2. Current and Future Market Size Value (US$ Million) Projections, 2023 to 2033
4.2.1. Y-o-Y Growth Trend Analysis
4.2.2. Absolute $ Opportunity Analysis
5. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Solution
5.1. Introduction / Key Findings
5.2. Historical Market Size Value (US$ Million) Analysis By Solution, 2018 to 2022
5.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Solution, 2023 to 2033
5.3.1. AI-powered Fraud Prevention Software
5.3.1.1. Cloud-based
5.3.1.2. On-Premises
5.3.2. Services
5.3.2.1. Risk Assessment Services
5.3.2.2. Fraud & Risk Consulting
5.3.2.3. Integration & Implementation
5.3.2.4. Support & Maintenance
5.3.2.5. Managed Services
5.4. Y-o-Y Growth Trend Analysis By Solution, 2018 to 2022
5.5. Absolute $ Opportunity Analysis By Solution, 2023 to 2033
6. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Application
6.1. Introduction / Key Findings
6.2. Historical Market Size Value (US$ Million) Analysis By Application, 2018 to 2022
6.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Application, 2023 to 2033
6.3.1. Identity Theft Protection
6.3.2. Payment Fraud Prevention
6.3.3. Anti-Money Laundering
6.3.4. Others
6.4. Y-o-Y Growth Trend Analysis By Application, 2018 to 2022
6.5. Absolute $ Opportunity Analysis By Application, 2023 to 2033
7. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Enterprise Size
7.1. Introduction / Key Findings
7.2. Historical Market Size Value (US$ Million) Analysis By Enterprise Size, 2018 to 2022
7.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Enterprise Size, 2023 to 2033
7.3.1. Small and Medium Enterprises (SMEs)
7.3.2. Large Enterprises
7.4. Y-o-Y Growth Trend Analysis By Enterprise Size, 2018 to 2022
7.5. Absolute $ Opportunity Analysis By Enterprise Size, 2023 to 2033
8. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Industry
8.1. Introduction / Key Findings
8.2. Historical Market Size Value (US$ Million) Analysis By Industry, 2018 to 2022
8.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Industry, 2023 to 2033
8.3.1. BFSI
8.3.2. IT & Telecom
8.3.3. Healthcare
8.3.4. Education
8.3.5. Government
8.3.6. Retail & CPG
8.3.7. Media & Entertainment
8.3.8. Others
8.4. Y-o-Y Growth Trend Analysis By Industry, 2018 to 2022
8.5. Absolute $ Opportunity Analysis By Industry, 2023 to 2033
9. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Region
9.1. Introduction
9.2. Historical Market Size Value (US$ Million) Analysis By Region, 2018 to 2022
9.3. Current Market Size Value (US$ Million) Analysis and Forecast By Region, 2023 to 2033
9.3.1. North America
9.3.2. Latin America
9.3.3. Europe
9.3.4. South Asia
9.3.5. East Asia
9.3.6. Oceania
9.3.7. MEA
9.4. Market Attractiveness Analysis By Region
10. North America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
10.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
10.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
10.2.1. By Country
10.2.1.1. U.S.
10.2.1.2. Canada
10.2.2. By Solution
10.2.3. By Application
10.2.4. By Enterprise Size
10.2.5. By Industry
10.3. Market Attractiveness Analysis
10.3.1. By Country
10.3.2. By Solution
10.3.3. By Application
10.3.4. By Enterprise Size
10.3.5. By Industry
10.4. Key Takeaways
11. Latin America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
11.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
11.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
11.2.1. By Country
11.2.1.1. Brazil
11.2.1.2. Mexico
11.2.1.3. Rest of Latin America
11.2.2. By Solution
11.2.3. By Application
11.2.4. By Enterprise Size
11.2.5. By Industry
11.3. Market Attractiveness Analysis
11.3.1. By Country
11.3.2. By Solution
11.3.3. By Application
11.3.4. By Enterprise Size
11.3.5. By Industry
11.4. Key Takeaways
12. Europe Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
12.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
12.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
12.2.1. By Country
12.2.1.1. Germany
12.2.1.2. U.K.
12.2.1.3. France
12.2.1.4. Spain
12.2.1.5. Italy
12.2.1.6. Rest of Europe
12.2.2. By Solution
12.2.3. By Application
12.2.4. By Enterprise Size
12.2.5. By Industry
12.3. Market Attractiveness Analysis
12.3.1. By Country
12.3.2. By Solution
12.3.3. By Application
12.3.4. By Enterprise Size
12.3.5. By Industry
12.4. Key Takeaways
13. South Asia Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
13.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
13.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
13.2.1. By Country
13.2.1.1. India
13.2.1.2. Malaysia
13.2.1.3. Singapore
13.2.1.4. Thailand
13.2.1.5. Rest of South Asia
13.2.2. By Solution
13.2.3. By Application
13.2.4. By Enterprise Size
13.2.5. By Industry
13.3. Market Attractiveness Analysis
13.3.1. By Country
13.3.2. By Solution
13.3.3. By Application
13.3.4. By Enterprise Size
13.3.5. By Industry
13.4. Key Takeaways
14. East Asia Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
14.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
14.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
14.2.1. By Country
14.2.1.1. China
14.2.1.2. Japan
14.2.1.3. South Korea
14.2.2. By Solution
14.2.3. By Application
14.2.4. By Enterprise Size
14.2.5. By Industry
14.3. Market Attractiveness Analysis
14.3.1. By Country
14.3.2. By Solution
14.3.3. By Application
14.3.4. By Enterprise Size
14.3.5. By Industry
14.4. Key Takeaways
15. Oceania Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
15.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
15.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
15.2.1. By Country
15.2.1.1. Australia
15.2.1.2. New Zealand
15.2.2. By Solution
15.2.3. By Application
15.2.4. By Enterprise Size
15.2.5. By Industry
15.3. Market Attractiveness Analysis
15.3.1. By Country
15.3.2. By Solution
15.3.3. By Application
15.3.4. By Enterprise Size
15.3.5. By Industry
15.4. Key Takeaways
16. MEA Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
16.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
16.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
16.2.1. By Country
16.2.1.1. GCC Countries
16.2.1.2. South Africa
16.2.1.3. Israel
16.2.1.4. Rest of MEA
16.2.2. By Solution
16.2.3. By Application
16.2.4. By Enterprise Size
16.2.5. By Industry
16.3. Market Attractiveness Analysis
16.3.1. By Country
16.3.2. By Solution
16.3.3. By Application
16.3.4. By Enterprise Size
16.3.5. By Industry
16.4. Key Takeaways
17. Key Countries Market Analysis
17.1. U.S.
17.1.1. Pricing Analysis
17.1.2. Market Share Analysis, 2022
17.1.2.1. By Solution
17.1.2.2. By Application
17.1.2.3. By Enterprise Size
17.1.2.4. By Industry
17.2. Canada
17.2.1. Pricing Analysis
17.2.2. Market Share Analysis, 2022
17.2.2.1. By Solution
17.2.2.2. By Application
17.2.2.3. By Enterprise Size
17.2.2.4. By Industry
17.3. Brazil
17.3.1. Pricing Analysis
17.3.2. Market Share Analysis, 2022
17.3.2.1. By Solution
17.3.2.2. By Application
17.3.2.3. By Enterprise Size
17.3.2.4. By Industry
17.4. Mexico
17.4.1. Pricing Analysis
17.4.2. Market Share Analysis, 2022
17.4.2.1. By Solution
17.4.2.2. By Application
17.4.2.3. By Enterprise Size
17.4.2.4. By Industry
17.5. Germany
17.5.1. Pricing Analysis
17.5.2. Market Share Analysis, 2022
17.5.2.1. By Solution
17.5.2.2. By Application
17.5.2.3. By Enterprise Size
17.5.2.4. By Industry
17.6. U.K.
17.6.1. Pricing Analysis
17.6.2. Market Share Analysis, 2022
17.6.2.1. By Solution
17.6.2.2. By Application
17.6.2.3. By Enterprise Size
17.6.2.4. By Industry
17.7. France
17.7.1. Pricing Analysis
17.7.2. Market Share Analysis, 2022
17.7.2.1. By Solution
17.7.2.2. By Application
17.7.2.3. By Enterprise Size
17.7.2.4. By Industry
17.8. Spain
17.8.1. Pricing Analysis
17.8.2. Market Share Analysis, 2022
17.8.2.1. By Solution
17.8.2.2. By Application
17.8.2.3. By Enterprise Size
17.8.2.4. By Industry
17.9. Italy
17.9.1. Pricing Analysis
17.9.2. Market Share Analysis, 2022
17.9.2.1. By Solution
17.9.2.2. By Application
17.9.2.3. By Enterprise Size
17.9.2.4. By Industry
17.10. India
17.10.1. Pricing Analysis
17.10.2. Market Share Analysis, 2022
17.10.2.1. By Solution
17.10.2.2. By Application
17.10.2.3. By Enterprise Size
17.10.2.4. By Industry
17.11. Malaysia
17.11.1. Pricing Analysis
17.11.2. Market Share Analysis, 2022
17.11.2.1. By Solution
17.11.2.2. By Application
17.11.2.3. By Enterprise Size
17.11.2.4. By Industry
17.12. Singapore
17.12.1. Pricing Analysis
17.12.2. Market Share Analysis, 2022
17.12.2.1. By Solution
17.12.2.2. By Application
17.12.2.3. By Enterprise Size
17.12.2.4. By Industry
17.13. Thailand
17.13.1. Pricing Analysis
17.13.2. Market Share Analysis, 2022
17.13.2.1. By Solution
17.13.2.2. By Application
17.13.2.3. By Enterprise Size
17.13.2.4. By Industry
17.14. China
17.14.1. Pricing Analysis
17.14.2. Market Share Analysis, 2022
17.14.2.1. By Solution
17.14.2.2. By Application
17.14.2.3. By Enterprise Size
17.14.2.4. By Industry
17.15. Japan
17.15.1. Pricing Analysis
17.15.2. Market Share Analysis, 2022
17.15.2.1. By Solution
17.15.2.2. By Application
17.15.2.3. By Enterprise Size
17.15.2.4. By Industry
17.16. South Korea
17.16.1. Pricing Analysis
17.16.2. Market Share Analysis, 2022
17.16.2.1. By Solution
17.16.2.2. By Application
17.16.2.3. By Enterprise Size
17.16.2.4. By Industry
17.17. Australia
17.17.1. Pricing Analysis
17.17.2. Market Share Analysis, 2022
17.17.2.1. By Solution
17.17.2.2. By Application
17.17.2.3. By Enterprise Size
17.17.2.4. By Industry
17.18. New Zealand
17.18.1. Pricing Analysis
17.18.2. Market Share Analysis, 2022
17.18.2.1. By Solution
17.18.2.2. By Application
17.18.2.3. By Enterprise Size
17.18.2.4. By Industry
17.19. GCC Countries
17.19.1. Pricing Analysis
17.19.2. Market Share Analysis, 2022
17.19.2.1. By Solution
17.19.2.2. By Application
17.19.2.3. By Enterprise Size
17.19.2.4. By Industry
17.20. South Africa
17.20.1. Pricing Analysis
17.20.2. Market Share Analysis, 2022
17.20.2.1. By Solution
17.20.2.2. By Application
17.20.2.3. By Enterprise Size
17.20.2.4. By Industry
17.21. Israel
17.21.1. Pricing Analysis
17.21.2. Market Share Analysis, 2022
17.21.2.1. By Solution
17.21.2.2. By Application
17.21.2.3. By Enterprise Size
17.21.2.4. By Industry
18. Market Structure Analysis
18.1. Competition Dashboard
18.2. Competition Benchmarking
18.3. Market Share Analysis of Top Players
18.3.1. By Regional
18.3.2. By Solution
18.3.3. By Application
18.3.4. By Enterprise Size
18.3.5. By Industry
19. Competition Analysis
19.1. Competition Deep Dive
19.1.1. IBM Corporation
19.1.1.1. Overview
19.1.1.2. Product Portfolio
19.1.1.3. Profitability by Market Segments
19.1.1.4. Sales Footprint
19.1.1.5. Strategy Overview
19.1.1.5.1. Marketing Strategy
19.1.2. Cognizant
19.1.2.1. Overview
19.1.2.2. Product Portfolio
19.1.2.3. Profitability by Market Segments
19.1.2.4. Sales Footprint
19.1.2.5. Strategy Overview
19.1.2.5.1. Marketing Strategy
19.1.3. Temenos AG
19.1.3.1. Overview
19.1.3.2. Product Portfolio
19.1.3.3. Profitability by Market Segments
19.1.3.4. Sales Footprint
19.1.3.5. Strategy Overview
19.1.3.5.1. Marketing Strategy
19.1.4. Capgemini SE
19.1.4.1. Overview
19.1.4.2. Product Portfolio
19.1.4.3. Profitability by Market Segments
19.1.4.4. Sales Footprint
19.1.4.5. Strategy Overview
19.1.4.5.1. Marketing Strategy
19.1.5. Subex Limited
19.1.5.1. Overview
19.1.5.2. Product Portfolio
19.1.5.3. Profitability by Market Segments
19.1.5.4. Sales Footprint
19.1.5.5. Strategy Overview
19.1.5.5.1. Marketing Strategy
19.1.6. JuicyScore
19.1.6.1. Overview
19.1.6.2. Product Portfolio
19.1.6.3. Profitability by Market Segments
19.1.6.4. Sales Footprint
19.1.6.5. Strategy Overview
19.1.6.5.1. Marketing Strategy
19.1.7. Hewlett Packard Enterprise
19.1.7.1. Overview
19.1.7.2. Product Portfolio
19.1.7.3. Profitability by Market Segments
19.1.7.4. Sales Footprint
19.1.7.5. Strategy Overview
19.1.7.5.1. Marketing Strategy
19.1.8. MaxMind, Inc.
19.1.8.1. Overview
19.1.8.2. Product Portfolio
19.1.8.3. Profitability by Market Segments
19.1.8.4. Sales Footprint
19.1.8.5. Strategy Overview
19.1.8.5.1. Marketing Strategy
19.1.9. BAE Systems plc
19.1.9.1. Overview
19.1.9.2. Product Portfolio
19.1.9.3. Profitability by Market Segments
19.1.9.4. Sales Footprint
19.1.9.5. Strategy Overview
19.1.9.5.1. Marketing Strategy
19.1.10. Pelican
19.1.10.1. Overview
19.1.10.2. Product Portfolio
19.1.10.3. Profitability by Market Segments
19.1.10.4. Sales Footprint
19.1.10.5. Strategy Overview
19.1.10.5.1. Marketing Strategy
19.1.11. SAS Institute Inc.
19.1.11.1. Overview
19.1.11.2. Product Portfolio
19.1.11.3. Profitability by Market Segments
19.1.11.4. Sales Footprint
19.1.11.5. Strategy Overview
19.1.11.5.1. Marketing Strategy
19.1.12. Splunk, Inc.
19.1.12.1. Overview
19.1.12.2. Product Portfolio
19.1.12.3. Profitability by Market Segments
19.1.12.4. Sales Footprint
19.1.12.5. Strategy Overview
19.1.12.5.1. Marketing Strategy
19.1.13. DataVisor, Inc.
19.1.13.1. Overview
19.1.13.2. Product Portfolio
19.1.13.3. Profitability by Market Segments
19.1.13.4. Sales Footprint
19.1.13.5. Strategy Overview
19.1.13.5.1. Marketing Strategy
19.1.14. Matellio Inc.
19.1.14.1. Overview
19.1.14.2. Product Portfolio
19.1.14.3. Profitability by Market Segments
19.1.14.4. Sales Footprint
19.1.14.5. Strategy Overview
19.1.14.5.1. Marketing Strategy
19.1.15. ACTICO GmbH
19.1.15.1. Overview
19.1.15.2. Product Portfolio
19.1.15.3. Profitability by Market Segments
19.1.15.4. Sales Footprint
19.1.15.5. Strategy Overview
19.1.15.5.1. Marketing Strategy
20. Assumptions & Acronyms Used
21. Research Methodology
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