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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AI in Fraud Management Market
