AI in Fraud Management Market (By Solution: AI-powered Fraud Prevention Software, Services; By Application: Identity Theft Protection, Payment Fraud Prevention, Anti-Money Laundering, Others, By Enterprises: Large Enterprises, Small and Medium Enterprises; By Industry: BFSI, IT and Telecom, Healthcare, Government, Education, Retail and CPG, Media and Entertainment, Others) - Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2024-2033
The global AI in fraud management market size was USD 10.48 billion in 2023, calculated at USD 12.42 billion in 2024 and is expected to reach around USD 57.32 billion by 2033, expanding at a CAGR of 18.52% from 2024 to 2033. The rising demand for an efficient fraud management system that drives the growth of the AI in fraud management market.
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The U.S. AI in fraud management market size was exhibited at USD 2.49 billion in 2023 and is projected to be worth around USD 13.94 billion by 2033, poised to grow at a CAGR of 18.79% from 2024 to 2033.
North America led the global AI in fraud management market in 2023. The growth of the market is attributed to the rising digitization in the industries, the acceptance of online services, and online fraud cases that are driving the demand for an efficient fraud management system that boosts the growth of the market. The higher availability of AI service providers in fraud management in regional countries like the United States is driving further expansion of the market. The rising demand for artificial intelligence in fraud management from the various end-use industries such as healthcare, retail, manufacturing, e-commerce, and others is fueling the growth of the AI in fraud management market in the region.
Asia Pacific is expected to witness the fastest growth in the AI in fraud management market during the forecast period. The growth of the market in the region is increasing due to the rising fraudulent activities in banking and financial institutions, which are driving the demand for artificial intelligence (AI) in fraud management systems for detecting and analyzing anomalies in transactions. The rising integration of AI into various other applications is driving the growth of the market.
The increasing trends towards the online lifestyle and use of online applications and the rising cases of fraud cases, cyberattacks, and others are driving the demand for efficient and effective technology for fraud management. Artificial intelligence plays a vital role in fraud management. The integration of smart and modern technologies, such as AI and machine learning (ML) algorithms, can help detect and analyze anomalies that may indicate fraudulent activities.
AI-powered fraud management helps in analyzing and detecting the various types of fraud, such as identity theft, payment fraud, and phishing attacks. Thus, the rising adoption of online services and the increasing concern about cyberattacks are driving the growth of the AI in fraud management market.
Report Coverage | Details |
Market Size by 2033 | USD 57.32 Billion |
Market Size in 2023 | USD 10.48 Billion |
Market Size in 2024 | USD 12.42 Billion |
Market Growth Rate from 2024 to 2033 | CAGR of 18.52% |
Largest Market | North America |
Base Year | 2023 |
Forecast Period | 2024 to 2033 |
Segments Covered | Solution, Application, Enterprises, Industry, and Regions |
Regions Covered | North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa |
Benefits associated with the integration of AI in fraud management system
The rising adoption of the AI in the several end-use industries for the enhancement in operation and efficiency in productivity are contributing in the growth of the AI in fraud management market. The increasing cases of cyber threats, data theft, identity theft, and other cybercrimes in different industries are driving the demand for an effective solution that integrates AI into the fraud management system to detect and analyze fraudulent activities efficiently.
The integration of fraud management into artificial intelligence and machine learning is working on the principles of learning from data, such as data processing, data collection, training, detection, and feedback loop. Further, there are several types of fraud detected by artificial intelligence, including credit card fraud, identity theft, insurance fraud, account takeover, phishing attacks, payment fraud, and money laundering. Thus, all these benefits of the AI in fraud management market drives the expansion of the market into several industries.
Lack of awareness
The lack of awareness about the technologies and the insufficiency of professionally skilled forces to operate the technology is limiting the growth of the AI in fraud management market.
Advancement in technologies
The advancement in AI technologies, such as predictive analytics and automation, enhanced the efficiency of fraud management systems. Predictive analytics leverages large amounts of data to detect and analyze anomalies and patterns that are suspicious of fraudulent activities. Automation involves reducing information technologies and control systems to minimize human participation in operations. Automation offers efficient analysis and processing of large amounts of data. Thus, the integration of AI tools like predictive analytics and automation revolutionizes fraud management and drives the opportunity for the growth of the AI in fraud management market.
The AI-powered fraud prevention software segment dominated the AI in fraud management market in 2023. The increase in online service applications, the inclination towards digitization, and the rising fraudulent activities are driving the demand for AI-powered fraud prevention software. The AI-powered fraud prevention software works on the principle of machine learning, which aims to detect and analyze anomalies and behaviors that indicate fraud. The system analyzes the transaction data, patterns, and user behavior. There are several stages or mechanisms used in AI fraud prevention software, including data collection, model training, feature engineering, anomaly detection, continuous learning, and alerting and reporting.
AI fraud prevention software is divided into two major categories: on-premise and cloud-based. There are several benefits associated with AI fraud prevention software, such as providing real-time prevention and detection, enhancing accuracy and efficiency, and cost reduction. There are several end-use industries that are adopting AI-powered fraud prevention software to prevent data theft or any type of cyberattack that contributed to the expansion of the segment in the market.
The application segment is further divided into identity theft protection, payment fraud management, and anti-money laundering, in which the identity theft protection segment was estimated to account for the highest share of the market in 2023. The rise in digitization has resulted in a rising number of identity theft cases. Identity theft is impacting both customers and the financial institutions. It can cause legal issues, financial losses, and damage to institutions. The AI in fraud management market provides practical solution for the same.
The AI in fraud management market plays a crucial role in detecting and minimizing identity theft. The integration of technologies such as data analytics, machine learning, and real-time monitoring, as well as artificial intelligence, completely revolutionizes the process of prevention and identification of fraud. AI helps prevent and detect fraudulent activities using a large amount of data; it identifies patterns and problems that can suggest fraudulent activities. AI platforms help companies prevent identity theft.
The large enterprises segment dominated the AI in fraud management market in 2023. The rising adoption of digitization and adaptation of technologies are driving the demand for the AI in fraud management.
The increasing adoption of artificial intelligence in fraud management in the end-use industries such as healthcare, automotive, manufacturing, and others is driving the expansion of the market. The higher availability of complexities in operation, the increasing number of fraud cases, and the investment in technologies boost the growth of the market.
The BFSI segment dominated the global AI in fraud management market in 2023. The increasing adoption of digitization and artificial intelligence in the banking and financial sector for enhancement in operations, customer experience, and efficiency in other services. The BFSI sector is highly experienced with security threats in terms of customer data, payment transactions, fraudulent transactions, and other issues that drive the demand for the technologically advanced fraud management system that drives the growth of the market.
Segments Covered in the Report
By Solution
By Application
By Enterprises
By Industry
By Geography
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology (Premium Insights)
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Distribution Channel Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on AI in Fraud Management Market
5.1. COVID-19 Landscape: AI in Fraud Management Industry Impact
5.2. COVID 19 - Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global AI in Fraud Management Market, By Solution
8.1. AI in Fraud Management Market, by Solution, 2024-2033
8.1.1. AI-powered Fraud Prevention Software
8.1.1.1. Market Revenue and Forecast (2021-2033)
8.1.2. Services
8.1.2.1. Market Revenue and Forecast (2021-2033)
Chapter 9. Global AI in Fraud Management Market, By Application
9.1. AI in Fraud Management Market, by Application, 2024-2033
9.1.1. Identity Theft Protection
9.1.1.1. Market Revenue and Forecast (2021-2033)
9.1.2. Payment Fraud Prevention
9.1.2.1. Market Revenue and Forecast (2021-2033)
9.1.3. Anti-Money Laundering
9.1.3.1. Market Revenue and Forecast (2021-2033)
9.1.4. Others
9.1.4.1. Market Revenue and Forecast (2021-2033)
Chapter 10. Global AI in Fraud Management Market, By Enterprises
10.1. AI in Fraud Management Market, by Enterprises, 2024-2033
10.1.1. Large Enterprises
10.1.1.1. Market Revenue and Forecast (2021-2033)
10.1.2. Small and Medium Enterprises
10.1.2.1. Market Revenue and Forecast (2021-2033)
Chapter 11. Global AI in Fraud Management Market, By Industry
11.1. AI in Fraud Management Market, by Industry, 2024-2033
11.1.1. BFSI
11.1.1.1. Market Revenue and Forecast (2021-2033)
11.1.2. IT and Telecom
11.1.2.1. Market Revenue and Forecast (2021-2033)
11.1.3. Healthcare
11.1.3.1. Market Revenue and Forecast (2021-2033)
11.1.4. Government
11.1.4.1. Market Revenue and Forecast (2021-2033)
11.1.5. Education
11.1.5.1. Market Revenue and Forecast (2021-2033)
11.1.6. Retail and CPG
11.1.6.1. Market Revenue and Forecast (2021-2033)
11.1.7. Media and Entertainment
11.1.57.1. Market Revenue and Forecast (2021-2033)
11.1.8. Others
11.1.8.1. Market Revenue and Forecast (2021-2033)
Chapter 12. Global AI in Fraud Management Market, Regional Estimates and Trend Forecast
12.1. North America
12.1.1. Market Revenue and Forecast, by Solution (2021-2033)
12.1.2. Market Revenue and Forecast, by Application (2021-2033)
12.1.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.1.4. Market Revenue and Forecast, by Industry (2021-2033)
12.1.5. U.S.
12.1.5.1. Market Revenue and Forecast, by Solution (2021-2033)
12.1.5.2. Market Revenue and Forecast, by Application (2021-2033)
12.1.5.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.1.5.4. Market Revenue and Forecast, by Industry (2021-2033)
12.1.6. Rest of North America
12.1.6.1. Market Revenue and Forecast, by Solution (2021-2033)
12.1.6.2. Market Revenue and Forecast, by Application (2021-2033)
12.1.6.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.1.6.4. Market Revenue and Forecast, by Industry (2021-2033)
12.2. Europe
12.2.1. Market Revenue and Forecast, by Solution (2021-2033)
12.2.2. Market Revenue and Forecast, by Application (2021-2033)
12.2.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.2.4. Market Revenue and Forecast, by Industry (2021-2033)
12.2.5. UK
12.2.5.1. Market Revenue and Forecast, by Solution (2021-2033)
12.2.5.2. Market Revenue and Forecast, by Application (2021-2033)
12.2.5.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.2.5.4. Market Revenue and Forecast, by Industry (2021-2033)
12.2.6. Germany
12.2.6.1. Market Revenue and Forecast, by Solution (2021-2033)
12.2.6.2. Market Revenue and Forecast, by Application (2021-2033)
12.2.6.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.2.6.4. Market Revenue and Forecast, by Industry (2021-2033)
12.2.7. France
12.2.7.1. Market Revenue and Forecast, by Solution (2021-2033)
12.2.7.2. Market Revenue and Forecast, by Application (2021-2033)
12.2.7.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.2.7.4. Market Revenue and Forecast, by Industry (2021-2033)
12.2.8. Rest of Europe
12.2.8.1. Market Revenue and Forecast, by Solution (2021-2033)
12.2.8.2. Market Revenue and Forecast, by Application (2021-2033)
12.2.8.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.2.8.4. Market Revenue and Forecast, by Industry (2021-2033)
12.3. APAC
12.3.1. Market Revenue and Forecast, by Solution (2021-2033)
12.3.2. Market Revenue and Forecast, by Application (2021-2033)
12.3.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.3.4. Market Revenue and Forecast, by Industry (2021-2033)
12.3.5. India
12.3.5.1. Market Revenue and Forecast, by Solution (2021-2033)
12.3.5.2. Market Revenue and Forecast, by Application (2021-2033)
12.3.5.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.3.5.4. Market Revenue and Forecast, by Industry (2021-2033)
12.3.6. China
12.3.6.1. Market Revenue and Forecast, by Solution (2021-2033)
12.3.6.2. Market Revenue and Forecast, by Application (2021-2033)
12.3.6.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.3.6.4. Market Revenue and Forecast, by Industry (2021-2033)
12.3.7. Japan
12.3.7.1. Market Revenue and Forecast, by Solution (2021-2033)
12.3.7.2. Market Revenue and Forecast, by Application (2021-2033)
12.3.7.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.3.7.4. Market Revenue and Forecast, by Industry (2021-2033)
12.3.8. Rest of APAC
12.3.8.1. Market Revenue and Forecast, by Solution (2021-2033)
12.3.8.2. Market Revenue and Forecast, by Application (2021-2033)
12.3.8.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.3.8.4. Market Revenue and Forecast, by Industry (2021-2033)
12.4. MEA
12.4.1. Market Revenue and Forecast, by Solution (2021-2033)
12.4.2. Market Revenue and Forecast, by Application (2021-2033)
12.4.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.4.4. Market Revenue and Forecast, by Industry (2021-2033)
12.4.5. GCC
12.4.5.1. Market Revenue and Forecast, by Solution (2021-2033)
12.4.5.2. Market Revenue and Forecast, by Application (2021-2033)
12.4.5.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.4.5.4. Market Revenue and Forecast, by Industry (2021-2033)
12.4.6. North Africa
12.4.6.1. Market Revenue and Forecast, by Solution (2021-2033)
12.4.6.2. Market Revenue and Forecast, by Application (2021-2033)
12.4.6.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.4.6.4. Market Revenue and Forecast, by Industry (2021-2033)
12.4.7. South Africa
12.4.7.1. Market Revenue and Forecast, by Solution (2021-2033)
12.4.7.2. Market Revenue and Forecast, by Application (2021-2033)
12.4.7.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.4.7.4. Market Revenue and Forecast, by Industry (2021-2033)
12.4.8. Rest of MEA
12.4.8.1. Market Revenue and Forecast, by Solution (2021-2033)
12.4.8.2. Market Revenue and Forecast, by Application (2021-2033)
12.4.8.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.4.8.4. Market Revenue and Forecast, by Industry (2021-2033)
12.5. Latin America
12.5.1. Market Revenue and Forecast, by Solution (2021-2033)
12.5.2. Market Revenue and Forecast, by Application (2021-2033)
12.5.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.5.4. Market Revenue and Forecast, by Industry (2021-2033)
12.5.5. Brazil
12.5.5.1. Market Revenue and Forecast, by Solution (2021-2033)
12.5.5.2. Market Revenue and Forecast, by Application (2021-2033)
12.5.5.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.5.5.4. Market Revenue and Forecast, by Industry (2021-2033)
12.5.6. Rest of LATAM
12.5.6.1. Market Revenue and Forecast, by Solution (2021-2033)
12.5.6.2. Market Revenue and Forecast, by Application (2021-2033)
12.5.6.3. Market Revenue and Forecast, by Enterprises (2021-2033)
12.5.6.4. Market Revenue and Forecast, by Industry (2021-2033)
Chapter 13. Company Profiles
13.1. IBM Corporation
13.1.1. Company Overview
13.1.2. Product Offerings
13.1.3. Financial Performance
13.1.4. Recent Initiatives
13.2. Cognizant
13.2.1. Company Overview
13.2.2. Product Offerings
13.2.3. Financial Performance
13.2.4. Recent Initiatives
13.3. Temenos AG
13.3.1. Company Overview
13.3.2. Product Offerings
13.3.3. Financial Performance
13.3.4. Recent Initiatives
13.4. Capgemini SE
13.4.1. Company Overview
13.4.2. Product Offerings
13.4.3. Financial Performance
13.4.4. Recent Initiatives
13.5. Subex Limited
13.5.1. Company Overview
13.5.2. Product Offerings
13.5.3. Financial Performance
13.5.4. Recent Initiatives
13.6. JuicyScore
13.6.1. Company Overview
13.6.2. Product Offerings
13.6.3. Financial Performance
13.6.4. Recent Initiatives
13.7. Hewlett Packard Enterprise
13.7.1. Company Overview
13.7.2. Product Offerings
13.7.3. Financial Performance
13.7.4. Recent Initiatives
13.8. Maxmind, Inc.
13.8.1. Company Overview
13.8.2. Product Offerings
13.8.3. Financial Performance
13.8.4. Recent Initiatives
13.9. BAE Systems plc
13.9.1. Company Overview
13.9.2. Product Offerings
13.9.3. Financial Performance
13.9.4. Recent Initiatives
13.10. Pelican
13.10.1. Company Overview
13.10.2. Product Offerings
13.10.3. Financial Performance
13.10.4. Recent Initiatives
Chapter 14. Research Methodology
14.1. Primary Research
14.2. Secondary Research
14.3. Assumptions
Chapter 15. Appendix
15.1. About Us
15.2. Glossary of Terms
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