온라인 결제 사기 방지 시장 분석 및 예측(-2035년) : 사기 유형, 사기 탐지, 도입 모델, 업계별

The Online Payment Fraud Prevention Market is poised to expand from $8.5 billion in 2025 to $22.3 billion by 2035, reflecting a CAGR of 10.2%. The online payment fraud prevention market is rapidly evolving toward AI-enabled, real-time and intelligence-led security architectures as digital commerce expands. Recent developments illustrate this shift: in November 2025 EverC, now part of G2 Risk Solutions, introduced Scam Network Intelligence to map how scammers operate across advertising networks, fake support portals, messaging apps and social media; in October 2025 Phi Commerce launched the Phi-ter platform to flag suspicious activity within milliseconds using rule profiling, behavioral scoring and self-learning ML across issuing and acquiring channels; and in September 2025 SWIFT worked with 13 global institutions using privacy-enhancing technologies, where AI trained on synthetic data from ten million transactions delivered twice the detection performance. Innovation is also stretching into future computing models, with AdvanThink and Quandela partnering in June 2025 to test quantum machine learning within real-time payment pipelines.

The push for modernization is reinforced by regulatory compliance needs, government digitization programs, and clear evidence of escalating fraud volumes. TAC Securitya™s July 2023 PCI ASV integration, Bluefina™s April 2022 deployment of PCI-validated P2PE across 458 U.S. locations, and Indiaa™s establishment of the Payments Infrastructure Development Fund alongside UPI and BHIM expansion highlight the scale of ecosystem upgrades. In 2024, 79% of organizations reported attempted or actual payments fraud, 62% of institutions observed more sophisticated tactics, and UK Finance recorded over 1.5 million cases with a 5% year-on-year increase, while the U.S. Treasury prevented and recovered more than $4 billion through advanced analytics. Momentum toward next-generation capabilities continues as ACFE expects 83% of organizations to adopt generative AI by 2026, 40% already use physical biometrics with more planning adoption, DataVisor launched its AI Co-Pilot in October 2023 to automate detection, and Trulioo expanded AI-powered biometric authentication in November 2025 to strengthen digital trust.

Based on Fruad Detection, the Network-based fraud detection is experiencing rapid growth because businesses increasingly need real-time visibility into suspicious activity across complex digital traffic and transactional networks, especially as e-commerce, digital banking, and cloud-enabled services expand the attack surface and fraudsters use AI and automation to evade traditional defenses. Organizations are investing heavily in network-layer analytics and AI-driven platforms that can detect coordinated attacks, anomalous flows, and lateral movement before significant damage occurs, making these solutions indispensable for risk mitigation and compliance. This momentum is reflected in notable industry activity: Mastercarda™s acquisition of Recorded Future to integrate threat intelligence into its fraud tools and launch advanced network-level fraud solutions, strategic investments such as Invictus Growth Partnersa™ acquisition of Informed.IQ to scale AI-based fraud detection capabilities, and partnerships like Nasdaq Verafin with BioCatch to enhance payment fraud prevention by combining behavioral and transactional insights all underscoring how innovation, consolidation, and product expansion are driving growth in this segment.

Based on Deployment, Cloud-based deployment in fraud detection is growing rapidly because it offers scalability, flexibility, real-time analytics, and cost-efficiency that traditional on-premise systems struggle to match. Businesses today face fluctuating and high transaction volumes, driven by e-commerce, digital banking, and mobile payments, making elastic cloud resources ideal for handling spikes without expensive infrastructure investments. Cloud platforms also enable faster deployment, continuous updates, centralized data aggregation, and seamless integration with advanced AI/ML fraud models capabilities that improve accuracy and response times while reducing operational burden. These advantages have led many organizations, from startups to large enterprises, to favor cloud solutions, propelling this segment to be one of the fastest-growing in the fraud detection market.

Regionally, North America dominates the market, propelled by advanced technological infrastructure and high adoption rates of digital payment solutions. Europe follows closely, with stringent regulatory frameworks and a strong focus on cybersecurity. Within Europe, the United Kingdom stands out as a top-performing country due to its proactive measures in combating online fraud. The Asia-Pacific region is experiencing rapid growth, driven by the proliferation of smartphones and the burgeoning e-commerce sector, with China and India leading the charge.

Geographical Overview

North America dominates the online payment fraud prevention market. The region's advanced digital infrastructure supports this leadership. The United States, in particular, invests heavily in cybersecurity technologies. This investment is driven by the increasing frequency of cyber-attacks. The region's regulatory framework also plays a crucial role in market growth.

Europe follows closely, with a strong emphasis on data protection. The General Data Protection Regulation (GDPR) enhances security measures. Countries like Germany and the United Kingdom lead in adopting fraud prevention solutions. These efforts are crucial in safeguarding digital transactions.

Asia Pacific exhibits rapid growth potential. The region's expanding e-commerce sector fuels demand for fraud prevention. China and India are key players, investing in sophisticated security solutions. The increasing internet penetration and mobile payment adoption drive the market.

Latin America is emerging as a significant market. Brazil and Mexico lead in adopting online payment systems. The region's growing digital economy necessitates robust fraud prevention measures. Government initiatives and collaborations with tech firms bolster the market.

The Middle East and Africa are witnessing gradual market development. The rise in digital banking and e-commerce activities contributes to this growth. The United Arab Emirates and South Africa are pivotal markets. Investments in cybersecurity infrastructure are crucial for future expansion.

Key Trends and Drivers

The Online Payment Fraud Prevention Market is experiencing robust growth, driven by the rapid expansion of e-commerce and digital payment platforms. Key trends include the increasing adoption of artificial intelligence and machine learning to detect and mitigate fraudulent activities more efficiently. Enhanced security protocols and multi-factor authentication are becoming standard practices to safeguard transactions and build consumer trust.

The rise of mobile payments and contactless transactions has necessitated more sophisticated fraud prevention measures, as cybercriminals exploit new vulnerabilities. Regulatory frameworks are evolving, with governments implementing stricter compliance requirements to protect consumers and businesses alike. As a result, companies are investing in advanced analytics and real-time monitoring tools to stay ahead of potential threats.

Moreover, the integration of blockchain technology is emerging as a promising trend, offering enhanced transparency and security in financial transactions. This technology is poised to revolutionize fraud prevention by providing immutable records and reducing the risk of data breaches. With the continuous evolution of payment technologies, the demand for comprehensive fraud prevention solutions will continue to rise, presenting lucrative opportunities for market players.

Research Scope

• Estimates and forecasts the overall market size across fraud type, fraud detection, and region.
• Provides detailed information and key takeaways on qualitative and quantitative trends, dynamics, business framework, competitive landscape, and company profiling.
• Identifies factors influencing market growth and challenges, opportunities, drivers, and restraints.
• Identifies factors that could limit company participation in international markets to help calibrate market share expectations and growth rates.
• Evaluates key development strategies like acquisitions, product launches, mergers, collaborations, business expansions, agreements, partnerships, and R&D activities.
• Analyzes smaller market segments strategically, focusing on their potential, growth patterns, and impact on the overall market.
• Outlines the competitive landscape, assessing business and corporate strategies to monitor and dissect competitive advancements.

Our research scope provides comprehensive market data, insights, and analysis across a variety of critical areas. We cover Local Market Analysis, assessing consumer demographics, purchasing behaviors, and market size within specific regions to identify growth opportunities. Our Local Competition Review offers a detailed evaluation of competitors, including their strengths, weaknesses, and market positioning. We also conduct Local Regulatory Reviews to ensure businesses comply with relevant laws and regulations. Industry Analysis provides an in-depth look at market dynamics, key players, and trends. Additionally, we offer Cross-Segmental Analysis to identify synergies between different market segments, as well as Production-Consumption and Demand-Supply Analysis to optimize supply chain efficiency. Our Import-Export Analysis helps businesses navigate global trade environments by evaluating trade flows and policies. These insights empower clients to make informed strategic decisions, mitigate risks, and capitalize on market opportunities.

TABLE OF CONTENTS

1 Executive Summary

• 1.1 Market Size and Forecast
• 1.2 Market Overview
• 1.3 Market Snapshot
• 1.4 Regional Snapshot
• 1.5 Strategic Recommendations
• 1.6 Analyst Notes

2 Market Highlights

• 2.1 Key Market Highlights by Fraud Type
• 2.2 Key Market Highlights by Fraud Detection
• 2.3 Key Market Highlights by Deployment Model
• 2.4 Key Market Highlights by Vertical

3 Market Dynamics

• 3.1 Macroeconomic Analysis
• 3.2 Market Trends
• 3.3 Market Drivers
• 3.4 Market Opportunities
• 3.5 Market Restraints
• 3.6 CAGR Growth Analysis
• 3.7 Impact Analysis
• 3.8 Emerging Markets
• 3.9 Technology Roadmap
• 3.10 Strategic Frameworks
  • 3.10.1 PORTER's 5 Forces Model
  • 3.10.2 ANSOFF Matrix
  • 3.10.3 4P's Model
  • 3.10.4 PESTEL Analysis

4 Segment Analysis

• 4.1 Market Size & Forecast by Fraud Type (2020-2035)
  • 4.1.1 Identity Theft
  • 4.1.2 Account Takeover
  • 4.1.3 Card Not Present Fraud
  • 4.1.4 Transaction Laundering
• 4.2 Market Size & Forecast by Fraud Detection (2020-2035)
  • 4.2.1 Network-Based Fraud Detection
  • 4.2.2 Host-Based Fraud Detection
  • 4.2.3 Device-Based Fraud Detection
• 4.3 Market Size & Forecast by Deployment Model (2020-2035)
  • 4.3.1 On-Premise
  • 4.3.2 Cloud-Based
• 4.4 Market Size & Forecast by Vertical (2020-2035)
  • 4.4.1 BFSI
  • 4.4.2 IT and Telecom
  • 4.4.3 Retail and Consumer Packaged Goods
  • 4.4.4 Government
  • 4.4.5 Real Estate and Construction

5 Regional Analysis

• 5.1 Global Market Overview
• 5.2 North America Market Size (2020-2035)
  • 5.2.1 United States
    • 5.2.1.1 Fraud Type
    • 5.2.1.2 Fraud Detection
    • 5.2.1.3 Deployment Model
    • 5.2.1.4 Vertical
  • 5.2.2 Canada
    • 5.2.2.1 Fraud Type
    • 5.2.2.2 Fraud Detection
    • 5.2.2.3 Deployment Model
    • 5.2.2.4 Vertical
  • 5.2.3 Mexico
    • 5.2.3.1 Fraud Type
    • 5.2.3.2 Fraud Detection
    • 5.2.3.3 Deployment Model
    • 5.2.3.4 Vertical
• 5.3 Latin America Market Size (2020-2035)
  • 5.3.1 Brazil
    • 5.3.1.1 Fraud Type
    • 5.3.1.2 Fraud Detection
    • 5.3.1.3 Deployment Model
    • 5.3.1.4 Vertical
  • 5.3.2 Argentina
    • 5.3.2.1 Fraud Type
    • 5.3.2.2 Fraud Detection
    • 5.3.2.3 Deployment Model
    • 5.3.2.4 Vertical
  • 5.3.3 Rest of Latin America
    • 5.3.3.1 Fraud Type
    • 5.3.3.2 Fraud Detection
    • 5.3.3.3 Deployment Model
    • 5.3.3.4 Vertical
• 5.4 Asia-Pacific Market Size (2020-2035)
  • 5.4.1 China
    • 5.4.1.1 Fraud Type
    • 5.4.1.2 Fraud Detection
    • 5.4.1.3 Deployment Model
    • 5.4.1.4 Vertical
  • 5.4.2 India
    • 5.4.2.1 Fraud Type
    • 5.4.2.2 Fraud Detection
    • 5.4.2.3 Deployment Model
    • 5.4.2.4 Vertical
  • 5.4.3 South Korea
    • 5.4.3.1 Fraud Type
    • 5.4.3.2 Fraud Detection
    • 5.4.3.3 Deployment Model
    • 5.4.3.4 Vertical
  • 5.4.4 Japan
    • 5.4.4.1 Fraud Type
    • 5.4.4.2 Fraud Detection
    • 5.4.4.3 Deployment Model
    • 5.4.4.4 Vertical
  • 5.4.5 Australia
    • 5.4.5.1 Fraud Type
    • 5.4.5.2 Fraud Detection
    • 5.4.5.3 Deployment Model
    • 5.4.5.4 Vertical
  • 5.4.6 Taiwan
    • 5.4.6.1 Fraud Type
    • 5.4.6.2 Fraud Detection
    • 5.4.6.3 Deployment Model
    • 5.4.6.4 Vertical
  • 5.4.7 Rest of APAC
    • 5.4.7.1 Fraud Type
    • 5.4.7.2 Fraud Detection
    • 5.4.7.3 Deployment Model
    • 5.4.7.4 Vertical
• 5.5 Europe Market Size (2020-2035)
  • 5.5.1 Germany
    • 5.5.1.1 Fraud Type
    • 5.5.1.2 Fraud Detection
    • 5.5.1.3 Deployment Model
    • 5.5.1.4 Vertical
  • 5.5.2 France
    • 5.5.2.1 Fraud Type
    • 5.5.2.2 Fraud Detection
    • 5.5.2.3 Deployment Model
    • 5.5.2.4 Vertical
  • 5.5.3 United Kingdom
    • 5.5.3.1 Fraud Type
    • 5.5.3.2 Fraud Detection
    • 5.5.3.3 Deployment Model
    • 5.5.3.4 Vertical
  • 5.5.4 Spain
    • 5.5.4.1 Fraud Type
    • 5.5.4.2 Fraud Detection
    • 5.5.4.3 Deployment Model
    • 5.5.4.4 Vertical
  • 5.5.5 Italy
    • 5.5.5.1 Fraud Type
    • 5.5.5.2 Fraud Detection
    • 5.5.5.3 Deployment Model
    • 5.5.5.4 Vertical
  • 5.5.6 Rest of Europe
    • 5.5.6.1 Fraud Type
    • 5.5.6.2 Fraud Detection
    • 5.5.6.3 Deployment Model
    • 5.5.6.4 Vertical
• 5.6 Middle East & Africa Market Size (2020-2035)
  • 5.6.1 Saudi Arabia
    • 5.6.1.1 Fraud Type
    • 5.6.1.2 Fraud Detection
    • 5.6.1.3 Deployment Model
    • 5.6.1.4 Vertical
  • 5.6.2 United Arab Emirates
    • 5.6.2.1 Fraud Type
    • 5.6.2.2 Fraud Detection
    • 5.6.2.3 Deployment Model
    • 5.6.2.4 Vertical
  • 5.6.3 South Africa
    • 5.6.3.1 Fraud Type
    • 5.6.3.2 Fraud Detection
    • 5.6.3.3 Deployment Model
    • 5.6.3.4 Vertical
  • 5.6.4 Sub-Saharan Africa
    • 5.6.4.1 Fraud Type
    • 5.6.4.2 Fraud Detection
    • 5.6.4.3 Deployment Model
    • 5.6.4.4 Vertical
  • 5.6.5 Rest of MEA
    • 5.6.5.1 Fraud Type
    • 5.6.5.2 Fraud Detection
    • 5.6.5.3 Deployment Model
    • 5.6.5.4 Vertical

6 Market Strategy

• 6.1 Demand-Supply Gap Analysis
• 6.2 Trade & Logistics Constraints
• 6.3 Price-Cost-Margin Trends
• 6.4 Market Penetration
• 6.5 Consumer Analysis
• 6.6 Regulatory Snapshot

7 Competitive Intelligence

• 7.1 Market Positioning
• 7.2 Market Share
• 7.3 Competition Benchmarking
• 7.4 Top Company Strategies

8 Company Profiles

• 8.1 Apple
  • 8.1.1 Overview
  • 8.1.2 Product Summary
  • 8.1.3 Financial Performance
  • 8.1.4 SWOT Analysis
• 8.2 Samsung Electronics
  • 8.2.1 Overview
  • 8.2.2 Product Summary
  • 8.2.3 Financial Performance
  • 8.2.4 SWOT Analysis
• 8.3 Google
  • 8.3.1 Overview
  • 8.3.2 Product Summary
  • 8.3.3 Financial Performance
  • 8.3.4 SWOT Analysis
• 8.4 Alibaba Group
  • 8.4.1 Overview
  • 8.4.2 Product Summary
  • 8.4.3 Financial Performance
  • 8.4.4 SWOT Analysis
• 8.5 Tencent Holdings
  • 8.5.1 Overview
  • 8.5.2 Product Summary
  • 8.5.3 Financial Performance
  • 8.5.4 SWOT Analysis
• 8.6 Visa
  • 8.6.1 Overview
  • 8.6.2 Product Summary
  • 8.6.3 Financial Performance
  • 8.6.4 SWOT Analysis
• 8.7 Mastercard
  • 8.7.1 Overview
  • 8.7.2 Product Summary
  • 8.7.3 Financial Performance
  • 8.7.4 SWOT Analysis
• 8.8 PayPal
  • 8.8.1 Overview
  • 8.8.2 Product Summary
  • 8.8.3 Financial Performance
  • 8.8.4 SWOT Analysis
• 8.9 Square
  • 8.9.1 Overview
  • 8.9.2 Product Summary
  • 8.9.3 Financial Performance
  • 8.9.4 SWOT Analysis
• 8.10 Amazon
  • 8.10.1 Overview
  • 8.10.2 Product Summary
  • 8.10.3 Financial Performance
  • 8.10.4 SWOT Analysis
• 8.11 Ant Group
  • 8.11.1 Overview
  • 8.11.2 Product Summary
  • 8.11.3 Financial Performance
  • 8.11.4 SWOT Analysis
• 8.12 American Express
  • 8.12.1 Overview
  • 8.12.2 Product Summary
  • 8.12.3 Financial Performance
  • 8.12.4 SWOT Analysis
• 8.13 Huawei
  • 8.13.1 Overview
  • 8.13.2 Product Summary
  • 8.13.3 Financial Performance
  • 8.13.4 SWOT Analysis
• 8.14 Nokia
  • 8.14.1 Overview
  • 8.14.2 Product Summary
  • 8.14.3 Financial Performance
  • 8.14.4 SWOT Analysis
• 8.15 LG Electronics
  • 8.15.1 Overview
  • 8.15.2 Product Summary
  • 8.15.3 Financial Performance
  • 8.15.4 SWOT Analysis
• 8.16 Sony
  • 8.16.1 Overview
  • 8.16.2 Product Summary
  • 8.16.3 Financial Performance
  • 8.16.4 SWOT Analysis
• 8.17 FIS
  • 8.17.1 Overview
  • 8.17.2 Product Summary
  • 8.17.3 Financial Performance
  • 8.17.4 SWOT Analysis
• 8.18 Stripe
  • 8.18.1 Overview
  • 8.18.2 Product Summary
  • 8.18.3 Financial Performance
  • 8.18.4 SWOT Analysis
• 8.19 NXP Semiconductors
  • 8.19.1 Overview
  • 8.19.2 Product Summary
  • 8.19.3 Financial Performance
  • 8.19.4 SWOT Analysis
• 8.20 Gemalto
  • 8.20.1 Overview
  • 8.20.2 Product Summary
  • 8.20.3 Financial Performance
  • 8.20.4 SWOT Analysis

9 About Us

• 9.1 About Us
• 9.2 Research Methodology
• 9.3 Research Workflow
• 9.4 Consulting Services
• 9.5 Our Clients
• 9.6 Client Testimonials
• 9.7 Contact Us