This report examines the merchant fraud detection and prevention market landscape in detail; assessing different market trends and factors that are shaping the evolution of this growing market, such as biometric verification, artificial intelligence, and machine learning, as well as exploring and analysing the different types of fraud that target online merchants, including credit card fraud and friendly fraud. The report delivers comprehensive analysis of the strategic opportunities for merchant fraud detection and prevention providers; addressing key vertical and developing challenges, and how vendors should navigate these. As well as looking into merchant fraud detection and prevention use cases where fraudulent transactions occur, it also includes evaluation of the different markets that are targeted by fraudsters and the key challenges that online merchants are likely to face, such as the nefarious use of artificial intelligence and machine learning by fraudsters.
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- 1. Key Takeaways & Strategic Recommendations
- 1.1 Key Takeaways
- 1.2 Strategic Recommendations
- 2. Market Landscape
- 2.1 Introduction
- 2.2 Definitions & Scope
- 2.3 Types of Merchant Fraud
- Figure 2.1: Visualisation of Merchant Fraud
- 2.3.1 First-party Fraud
- i. Chargeback Fraud
- ii. Friendly Fraud
- iii. Policy Abuse
- 2.3.2 ATO Fraud
- 2.3.3 Other Types of Fraud
- i. Clean Fraud
- ii. Affiliate Fraud
- iii. Botnets
- iv. Triangulation Fraud
- Figure 2.2: Visualisation of Triangulation Fraud
- v. Synthetic Identity Fraud
- 2.4 Solutions Used in Merchant Fraud Detection & Prevention
- 2.4.1 Merchant Fraud Detection & Prevention Tools
- Figure 2.3: Methods of Merchant Fraud Prevention
- i. Biometrics
- ii. Behavioural Analytics
- iii. Tokenisation
- iv. APIs
- v. 3D Secure Authentication
- 2.5 Physical & Digital Goods
- Figure 2.4: Total Value of Fraudulent CNP Transactions Globally ($m), Split by Segment, 2024-
- 2.5.1 Remote Physical Goods
- Figure 2.5: Total Value of Fraudulent Remote Physical Goods Purchases Globally ($m), Split by 8 Key Regions, 2024-
- 2.5.2 Remote Digital Goods
- Figure 2.6: Total Value of Fraudulent Remote Digital Goods Purchases Globally ($m), Split by 8 Key Regions, 2024-
- 3. Emerging Merchant Fraud Prevention Market
- 3.1 Key Themes & Areas Involved
- 3.2 Key Trends & Current Market Drivers
- 3.2.1 Rapid Rise of eCommerce
- 3.2.2 Emerging Fraudulent Methods & Tactics
- i. Generative AI
- ii. FaaS
- 3.2.3 Customers are Poorly Educated on New Technologies
- 3.2.4 Deepfakes
- 3.2.5 Cutting Business Costs
- 3.3 BNPL
- i. BNPL Fraud Methods
- ii. BNPL Fraud Prevention Methods
- 3.4 Technologies
- 3.4.1 AI
- i. Benefits of AI in Merchant Fraud Prevention
- Figure 3.1: AI Benefits in Merchant Fraud Prevention
- ii. Drawbacks of AI in Merchant Fraud Prevention
- 3.4.2 ML
- i. Benefits of ML in Merchant Fraud Prevention
- ii. Drawbacks of ML in Merchant Fraud Prevention
- 3.4.3 Merchant Fraud Prevention APIs
- 3.5 PSD
- 3.5.1 How PSD2 Affects Merchants
- 3.6 3D S2 & Biometric Authorisation of Transactions
- 3.6.1 Methods of Authentication
- i. OTPs
- ii. Biometrics
- Figure 3.2: Types of Biometric Authentication
- 3.6.2 3D S2 Implications
- 4. Segment Analysis
- 4.1 Introduction
- 4.1.1 Different Merchants Who Are Affected by Fraud
- i. Generalist Retailers
- ii. Specialist Retailers
- iii. Streaming Services
- iv. Hospitality
- 4.2 Remote Digital & Physical Goods
- 4.2.1 Digital Goods
- Figure 4.1: Total Number of Transactions for Remote Digital Goods (m), Split by 8 Key Regions, 2024-
- i. Video Games
- ii. Music
- iii. Video
- iv. Ticketing
- 4.2.2 Physical Goods
- Figure 4.2: Total Value of Remote Physical Goods Transactions Globally ($m), Split by 8 Key Regions, 2024-
- i. Impact of the COVID-19 Pandemic
- 4.3 Key Challenges
- 4.3.1 Organised Fraud
- i. Noir’s Organisation
- ii. REKK
- iii. AI Music Streaming Scam
- 4.3.2 Lack of Physical Biometrics
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