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Global Merchant Fraud Prevention Market 2024-2029: Market Trends & Strategies

Publisher Juniper Research
Published Jul 10, 2024
Length 41 Pages
SKU # JNRH19878224

Description

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.

Please note: the online download version of this report is for a global site license.

Table of Contents

41 Pages
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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