부정 탐지 AI 시장 예측(-2032년) : 컴포넌트별, 배포별, 조직 규모별, 기술별, 애플리케이션별, 최종사용자별, 지역별

According to Stratistics MRC, the Global Fraud Detection AI Market is accounted for $17.6 billion in 2025 and is expected to reach $70.2 billion by 2032, growing at a CAGR of 21.8% during the forecast period. The fraud detection AI involves software platforms that use machine learning and analytics to identify suspicious transactions and behaviors in real time. It serves banking, payments, insurance, e-commerce, and telecom sectors. Growth is driven by rising digital transactions, increasing sophistication of fraud schemes, regulatory pressure to reduce financial crime, the need for automated decision-making, and demand for scalable systems that improve accuracy while reducing false positives.

According to the U.S. Department of the Treasury, AI-powered tools helped the government prevent and recover over $4 billion in fraudulent payments during the 2024 fiscal year, a massive increase from the $652.7 million recovered the previous year.

Market Dynamics:

Driver:

Exponential rise in digital transactions and sophisticated fraud schemes

Modern fraudsters are increasingly employing highly sophisticated techniques, such as synthetic identity theft and account takeover, which traditional rule-based systems often fail to detect. This evolving threat landscape necessitates the adoption of AI-driven solutions that can analyze millions of data points in real time to identify subtle anomalies. Furthermore, the integration of automation in financial services has made advanced AI essential for maintaining security and protecting sensitive consumer information globally.

Restraint:

High false positive rates leading to customer friction and operational cost

High false positive rates, which mistakenly flag legitimate transactions as fraudulent, pose a significant challenge to the fraud detection market. This creates immediate friction in the customer journey, leading to transaction abandonment and potential brand loyalty erosion. Moreover, investigating these false alarms requires extensive manual intervention, which significantly increases operational overhead for financial institutions and e-commerce merchants. Additionally, the constant need to fine-tune AI models to balance sensitivity with accuracy remains a complex and resource-intensive task that can slow down the deployment of new security protocols.

Opportunity:

Explainable AI to build trust and meet regulatory compliance

Unlike "black-box" algorithms, XAI provides clear reasoning for why a specific transaction was flagged, which is crucial for meeting stringent global data protection and anti-money laundering regulations. This clarity allows fraud analysts to make more informed decisions and simplifies the auditing process for compliance officers. Furthermore, by giving clear explanations for AI decision-making, organizations can reduce consumer skepticism and foster a more secure digital ecosystem while adhering to evolving legal standards.

Threat:

Adversarial AI used by fraudsters to bypass detection systems

As security teams adopt artificial intelligence, cybercriminals are also leveraging adversarial AI to develop more deceptive and resilient attack vectors. These actors use machine learning to test and probe existing detection models, identifying vulnerabilities and crafting "deepfake" identities or automated social engineering attacks that appear authentic. This technological arms race forces organizations to continuously update their defensive models, as static defenses quickly become obsolete. Moreover, the accessibility of open-source AI tools has lowered the barrier to entry for malicious actors, posing a persistent threat to the integrity of global digital transaction networks.

Covid-19 Impact:

The COVID-19 pandemic significantly accelerated the growth of the fraud detection AI market as global lockdowns forced consumers to adopt online banking and e-commerce at an unprecedented scale. This sudden digital migration provided fertile ground for cybercriminals, resulting in a dramatic spike in phishing and payment fraud. Consequently, organizations were compelled to rapidly integrate AI-powered security to handle the surge in transaction volumes and evolving threats. This period fundamentally shifted corporate priorities, making real-time, automated fraud prevention a core component of long-term business resilience strategies.

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

The solutions segment is expected to account for the largest market share during the forecast period because organizations are prioritizing the deployment of end-to-end, integrated software platforms to combat complex financial crimes. These AI-powered solutions offer essential capabilities such as real-time transaction monitoring, behavioral biometrics, and predictive risk scoring in a single package. Furthermore, the rising demand for scalable, cloud-based fraud management tools among small and large enterprises alike continues to drive substantial revenue growth. The necessity for robust, automated defenses against identity theft and payment fraud makes software solutions the primary investment area globally.

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 as businesses move away from static, rule-based systems toward adaptive algorithms that learn from historical data. Machine learning is uniquely capable of uncovering hidden patterns and relationships across massive datasets, allowing it to stay ahead of rapidly changing fraud tactics. Additionally, the increasing availability of big data and the need for high-precision anomaly detection are fueling the rapid adoption of this technology. Its ability to continuously improve accuracy over time makes it the preferred choice for forward-thinking financial institutions.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to its advanced technological infrastructure and the high concentration of leading AI software providers. The region faces a high volume of sophisticated cyberattacks, which has led to early and widespread adoption of AI-driven security among major banks and e-commerce giants. Furthermore, North America benefits from a stringent regulatory environment that mandates the use of cutting-edge tools for fraud prevention, data protection, and compliance.

Region with highest CAGR:

During the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by a booming digital economy and the rapid expansion of mobile payment systems in countries like China and India. The region's large, tech-savvy population is increasingly adopting digital financial services, which has unfortunately led to a corresponding rise in regional fraud cases. Additionally, governments across Asia are introducing new cybersecurity frameworks and promoting fintech innovation, encouraging businesses to invest in advanced AI defenses. The combination of rapid urbanization and improving internet accessibility further accelerates the demand for sophisticated fraud detection solutions.

Key players in the market

Some of the key players in Fraud Detection AI Market include SAS Institute Inc., Fair Isaac Corporation, NICE Ltd., International Business Machines Corporation, Microsoft Corporation, Oracle Corporation, Experian plc, LexisNexis Risk Solutions Group Inc., Mastercard Incorporated, Visa Inc., PayPal Holdings, Inc., Feedzai, Inc., Forter, Inc., Featurespace Limited, DataVisor, Inc., Sift Science, Inc., and ACI Worldwide, Inc.

Key Developments:

In December 2025, Forter introduced Prism, an AI copilot that gives eCommerce team's instant insights to fight automated, AI driven fraud and streamline decisioning across the customer journey.

In November 2025, SAS and the Association of Certified Fraud Examiners released new survey findings for International Fraud Awareness Week, spotlighting rising AI driven deception and how SAS's analytics help organizations counter deepfakes and synthetic identities.

In June 2025, Feedzai launched Feedzai IQ, a privacy preserving, federated learning suite that shares intelligence across institutions to detect AI powered fraud while keeping customer data protected.

Components Covered:

• Solutions
• Services

Deployments Covered:

• Sensors
• Probes and Analyzers
• Software and Services

Organization Sizes Covered:

• Large Enterprises
• Small & Medium Enterprises (SMEs)

Technologies Covered:

• Machine Learning
• Natural Language Processing
• Computer Vision
• Behavioral Analytics

Applications Covered:

• Payment Fraud
• Identity Theft & Account Takeover (ATO)
• Money Laundering (AML) & Sanctions Screening
• Insurance Claims Fraud
• Multi-channel/Omnichannel Fraud

End Users Covered:

• BFSI (Banking, Financial Services, and Insurance)
• Retail & E-commerce
• Healthcare & Life Sciences
• Government & Defense
• IT & Telecommunications
• Travel
• Real Estate
• 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 Fraud Detection AI Market, By Component

• 5.1 Introduction
• 5.2 Solutions
  • 5.2.1 Fraud Analytics Tools
  • 5.2.2 Authentication & Identity Management
  • 5.2.3 Governance, Risk, and Compliance (GRC)
• 5.3 Services
  • 5.3.1 Professional Services
  • 5.3.2 Managed Services

6 Global Fraud Detection AI Market, By Deployment

• 6.1 Introduction
• 6.2 Cloud-based
• 6.3 On-premise

7 Global Fraud Detection AI Market, By Organization Size

• 7.1 Introduction
• 7.2 Large Enterprises
• 7.3 Small & Medium Enterprises (SMEs)

8 Global Fraud Detection AI Market, By Technology

• 8.1 Introduction
• 8.2 Machine Learning
• 8.3 Natural Language Processing
• 8.4 Computer Vision
• 8.5 Behavioral Analytics

9 Global Fraud Detection AI Market, By Application

• 9.1 Introduction
• 9.2 Payment Fraud
• 9.3 Identity Theft & Account Takeover (ATO)
• 9.4 Money Laundering (AML) & Sanctions Screening
• 9.5 Insurance Claims Fraud
• 9.6 Multi-channel/Omnichannel Fraud

10 Global Fraud Detection AI Market, By End User

• 10.1 Introduction
• 10.2 BFSI (Banking, Financial Services, and Insurance)
• 10.3 Retail & E-commerce
• 10.4 Healthcare & Life Sciences
• 10.5 Government & Defense
• 10.6 IT & Telecommunications
• 10.7 Travel
• 10.8 Real Estate
• 10.9 Other End Users

11 Global Fraud Detection AI 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 SAS Institute Inc.
• 13.2 Fair Isaac Corporation
• 13.3 NICE Ltd.
• 13.4 International Business Machines Corporation
• 13.5 Microsoft Corporation
• 13.6 Oracle Corporation
• 13.7 Experian plc
• 13.8 LexisNexis Risk Solutions Group Inc.
• 13.9 Mastercard Incorporated
• 13.10 Visa Inc.
• 13.11 PayPal Holdings, Inc.
• 13.12 Feedzai, Inc.
• 13.13 Forter, Inc.
• 13.14 Featurespace Limited
• 13.15 DataVisor, Inc.
• 13.16 Sift Science, Inc.
• 13.17 ACI Worldwide, Inc.