세계의 사기 감지 및 예방용 AI 시장 예측(-2032년) : 컴포넌트별, 전개 형태별, 조직 규모별, 기술별, 용도별, 최종 사용자별, 지역별 분석

According to Stratistics MRC, the Global AI for Fraud Detection & Prevention Market is accounted for $14.91 billion in 2025 and is expected to reach $53.62 billion by 2032 growing at a CAGR of 20.06% during the forecast period. AI for fraud detection and prevention uses data analytics and sophisticated machine learning algorithms to instantly spot suspicious activity, trends, and anomalies. Large volumes of transactional, behavioral, and historical data can be analyzed by AI systems to identify possible fraud more quickly and accurately than with conventional techniques. Using methods like anomaly detection, predictive modeling, and natural language processing, cyber security teams, e-commerce platforms, and financial institutions can improve decision-making, reduce false positives, and predict fraudulent activity. Because AI is constantly learning from new data, fraud prevention becomes more proactive, flexible, and effective as fraud schemes become more complex.

According to BioCatch Behavioral Biometrics Association, 74% of financial institutions are already using AI for financial-crime detection and 73% for fraud detection, indicating widespread adoption and institutional trust in AI-driven security frameworks.

Market Dynamics:

Driver:

Growing cyber threats and advanced fraud techniques

The need for more intelligent security solutions has increased due to the complexity of cyber threats, such as deep fakes, phishing, identity theft, and synthetic fraud. The inability of traditional rule-based systems to identify subtle or changing fraudulent patterns frequently results in large losses in terms of money and reputation. Behavioral analytics, anomaly detection, and machine learning are used by AI-driven platforms to continuously analyze large datasets and adjust to new threats. AI makes proactive intervention possible by detecting anomalous behaviors in real-time and learning from past patterns, lowering risk exposure. Moreover, artificial intelligence's predictive powers are essential for protecting digital ecosystems in telecommunications, e-commerce, and financial services as fraudsters get more complex.

Restraint:

High costs of implementation and upkeep

The implementation of AI-powered fraud detection systems necessitates a large initial investment in software, hardware, and qualified staff. AI platforms must frequently be integrated with an organization's current IT infrastructure, which can be difficult and expensive. Additionally, in order to maintain these systems, AI models must be continuously monitored, updated, and retrained to keep up with changing fraud strategies. Adoption may be restricted by such costs, which can be prohibitive for small and medium-sized businesses. Despite its obvious advantages, high costs can cause deployment delays, lower return on investment, and discourage some businesses from fully implementing AI-driven fraud prevention.

Opportunity:

Growing use of e-commerce and digital payments

Globally, the volume of digital transactions is soaring due to the quick development of digital banking, mobile wallets, and online shopping. Due to traditional methods' inability to handle high-frequency, multi-channel transactions, this expansion present a huge opportunity for AI-driven fraud detection systems. AI is capable of real-time analysis of enormous volumes of data, identifying irregularities, odd patterns, and possible fraud before it affects clients or companies. In order to preserve consumer confidence and minimize financial losses, e-commerce platforms, fintech startups, and digital payment providers are investing more and more in AI. Additionally, the need for strong AI fraud prevention solutions is expected to grow rapidly as digital transactions continue to increase.

Threat:

Strong rivalry between solution providers

The market for AI fraud detection is getting more and more crowded, with many local and international vendors providing overlapping solutions. Businesses are under constant pressure to innovate, lower prices, and improve service quality in order to draw in and keep customers in the face of fierce competition. Established vendors with greater resources and sophisticated technology stacks may be harder for smaller players to compete with, and newcomers may encounter difficulties establishing credibility and trust. Furthermore, this competitive environment can slow market growth overall, raise marketing and R&D expenses, and lower profit margins. To preserve market share and maintain long-term growth, businesses must set themselves apart through cutting-edge features, first-rate customer service, or strategic alliances.

Covid-19 Impact:

The COVID-19 pandemic dramatically sped up digital transformation in many industries, increasing the likelihood of fraudulent activity by causing a spike in online transactions, remote banking, e-commerce, and digital payments. Due to traditional methods' inability to handle the volume and complexity of online transactions, this abrupt shift increased demand for AI-powered fraud detection and prevention solutions. In order to ensure business continuity and customer trust, organizations swiftly embraced AI technologies to monitor, analyze, and react to suspicious activities in real time. Moreover, the pandemic also highlighted the need for cloud-based, scalable AI systems that can adjust to new fraud trends and quickly evolving digital behaviors.

The cloud segment is expected to be the largest during the forecast period

The cloud segment is expected to account for the largest market share during the forecast period. This preference stems from cloud solutions' scalability, cost-effectiveness, and flexibility, which allow businesses to swiftly adjust to changing fraud strategies. Cloud-based platforms improve the detection and prevention of fraudulent activities by enabling real-time data processing and integration across multiple channels. Furthermore, sophisticated AI models, machine learning algorithms, and behavioral analytics are supported by the cloud's centralized infrastructure and are essential for spotting intricate fraud trends. Because of these features, cloud deployment is the go-to option for companies looking to improve their fraud detection systems without sacrificing operational flexibility.

The machine learning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the machine learning segment is predicted to witness the highest growth rate. Real-time fraud detection is made possible by machine learning, which is widely used to find patterns and anomalies in massive datasets. Over time, machine learning (ML) systems can predict and prevent fraud with ever-increasing accuracy by utilizing algorithms that continuously learn from transactional and historical data. Because of its versatility across sectors like banking, e-commerce, insurance, and telecommunications, this segment leads the market. Moreover, machine learning is a key component of contemporary fraud prevention solutions due to its capacity to minimize false positives, automate fraud detection procedures, and improve decision-making effectiveness.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share. The region's high rates of digital payment method adoption, sophisticated technological infrastructure, and the presence of big players like IBM, Microsoft, and Oracle-all of which encourage competition and innovation in fraud detection solutions-are the main causes of this dominance. Due to an increase in digital transactions and the sophistication of cyber threats, the United States in particular has been at the forefront. Additionally, North America is now a leader in this field owing to the integration of AI technologies, such as machine learning and deep learning, which have greatly improved the capabilities of fraud detection systems.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Rapid digital transformation in important economies like China, India, Japan, Australia, and Southeast Asian nations is the main driver of this strong growth. The ecosystem of digital transactions has grown dramatically as a result of the quick uptake of digital wallets, e-commerce, online banking, and mobile payment systems. Furthermore, this has also increased the risk of fraud and cyberattacks. In order to protect their business operations and client information, companies in the area are progressively implementing AI-based fraud detection systems.

Key players in the market

Some of the key players in AI for Fraud Detection & Prevention Market include IBM Corporation, BAE Systems, ACI Worldwide Inc, Fiserv Inc, Mastercard Inc, Feedzai Inc, Oracle Inc, Experian Inc, Cisco, Lexis Nexis Risk Solutions Inc, NOOS Technologies Inc, Forter Inc, Onfido Inc, PayPal and Abrigo Inc.

Key Developments:

In June 2025, BAE Systems has signed a new contract with the Swedish Defence Materiel Administration to supply additional BONUS precision-guided munitions to the Swedish Armed Forces. This contract marks a continued partnership between BAE Systems Bofors and the Swedish Armed Forces, reinforcing their shared commitment to delivering cutting-edge defense solutions.

In April 2025, IBM and Tokyo Electron (TEL) announced an extension of their agreement for the joint research and development of advanced semiconductor technologies. The new 5-year agreement will focus on the continued advancement of technology for next-generation semiconductor nodes and architectures to power the age of generative AI.

In March 2025, ACI Worldwide has announced an extension of their strategic technology partnership. The agreement will see Co-op continue to use the full range of solutions offered by ACI's Payments Orchestration Platform, including in-store, online and mobile payment processing as well as end-to-end payments and fraud management.

Components Covered:

• Solution
• Services

Deployment Modes Covered:

• Cloud
• On-Premises
• Hybrid

Organization Sizes Covered:

• Small & Medium Enterprises
• Large Enterprises

Technologies Covered:

• Machine Learning
• Deep Learning
• Natural Language Processing
• Graph Analytics
• Federated Learning & Privacy-Preserving AI
• Other Technologies

Applications Covered:

• Transaction Monitoring
• Identity Theft Detection
• Account Takeover Prevention
• Payment Fraud Detection
• Insurance Claim Fraud
• Anti-Money Laundering (AML)
• Behavioral Biometrics
• Synthetic Identity Detection
• Other Applications

End Users Covered:

• Banking, Financial Services, and Insurance (BFSI)
• Government and Public Sector
• Healthcare
• IT and Telecommunications
• Manufacturing
• Energy
• Other End Users

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Technology Analysis
• 3.7 Application Analysis
• 3.8 End User Analysis
• 3.9 Emerging Markets
• 3.10 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global AI for Fraud Detection & Prevention Market, By Component

• 5.1 Introduction
• 5.2 Solution
• 5.3 Services

6 Global AI for Fraud Detection & Prevention Market, By Deployment Mode

• 6.1 Introduction
• 6.2 Cloud
• 6.3 On-Premises
• 6.4 Hybrid

7 Global AI for Fraud Detection & Prevention Market, By Organization Size

• 7.1 Introduction
• 7.2 Small & Medium Enterprises
• 7.3 Large Enterprises

8 Global AI for Fraud Detection & Prevention Market, By Technology

• 8.1 Introduction
• 8.2 Machine Learning
• 8.3 Deep Learning
• 8.4 Natural Language Processing
• 8.5 Graph Analytics
• 8.6 Federated Learning & Privacy-Preserving AI
• 8.7 Other Technologies

9 Global AI for Fraud Detection & Prevention Market, By Application

• 9.1 Introduction
• 9.2 Transaction Monitoring
• 9.3 Identity Theft Detection
• 9.4 Account Takeover Prevention
• 9.5 Payment Fraud Detection
• 9.6 Insurance Claim Fraud
• 9.7 Anti-Money Laundering (AML)
• 9.8 Behavioral Biometrics
• 9.9 Synthetic Identity Detection
• 9.10 Other Applications

10 Global AI for Fraud Detection & Prevention Market, By End User

• 10.1 Introduction
• 10.2 Banking, Financial Services, and Insurance (BFSI)
• 10.3 Government and Public Sector
• 10.4 Healthcare
• 10.5 IT and Telecommunications
• 10.6 Manufacturing
• 10.7 Energy
• 10.8 Other End Users

11 Global AI for Fraud Detection & Prevention Market, By Geography

• 11.1 Introduction
• 11.2 North America
  • 11.2.1 US
  • 11.2.2 Canada
  • 11.2.3 Mexico
• 11.3 Europe
  • 11.3.1 Germany
  • 11.3.2 UK
  • 11.3.3 Italy
  • 11.3.4 France
  • 11.3.5 Spain
  • 11.3.6 Rest of Europe
• 11.4 Asia Pacific
  • 11.4.1 Japan
  • 11.4.2 China
  • 11.4.3 India
  • 11.4.4 Australia
  • 11.4.5 New Zealand
  • 11.4.6 South Korea
  • 11.4.7 Rest of Asia Pacific
• 11.5 South America
  • 11.5.1 Argentina
  • 11.5.2 Brazil
  • 11.5.3 Chile
  • 11.5.4 Rest of South America
• 11.6 Middle East & Africa
  • 11.6.1 Saudi Arabia
  • 11.6.2 UAE
  • 11.6.3 Qatar
  • 11.6.4 South Africa
  • 11.6.5 Rest of Middle East & Africa

12 Key Developments

• 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 12.2 Acquisitions & Mergers
• 12.3 New Product Launch
• 12.4 Expansions
• 12.5 Other Key Strategies

13 Company Profiling

• 13.1 IBM Corporation
• 13.2 BAE Systems
• 13.3 ACI Worldwide Inc
• 13.4 Fiserv Inc
• 13.5 Mastercard Inc
• 13.6 Feedzai Inc
• 13.7 Oracle Inc
• 13.8 Experian Inc
• 13.9 Cisco
• 13.10 Lexis Nexis Risk Solutions Inc
• 13.11 NOOS Technologies Inc
• 13.12 Forter Inc
• 13.13 Onfido Inc
• 13.14 PayPal
• 13.15 Abrigo Inc