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加盟店詐欺防止的全球市場:2024-2029年
市場調查報告書
商品編碼
1566487

加盟店詐欺防止的全球市場:2024-2029年

Global Merchant Fraud Prevention Market: 2024-2029
出版日期: | 出版商: Juniper Research Ltd | 英文 | 商品交期: 最快1-2個工作天內

價格
簡介目錄
主要統計
全球電子商務詐騙受害額 (2024年): 440億美元
全球電子商務詐騙受害額 (2029年): 1,090億美元
電子商務詐騙受害額的成長率 (2024～2029年): 141%
預測期間: 2024-2029年

"電子商務詐騙受害額預計2029年超越1,070億美元"

本報告調查了全球商家詐欺防制市場，概述了詐欺的主要類型、用於檢測和預防詐欺的解決方案和關鍵技術、各個類別的損害程度，並總結了以下趨勢和預測： FDP 軟體支出，以及使用瞻博網路研究競爭排行榜對競爭格局的分析。

主要功能

  • 市場動態：深入了解市場中的主要詐欺趨勢以及市場擴張的挑戰。討論電子商務詐欺的演變、技術進步、實施新的商家詐欺解決方案的障礙，包括識別可疑和真實的活動，以及分析發生詐欺的多個用例。它還對電子商務商家造成的欺詐損害的未來進行了展望。
  • 主要要點和策略建議：對主要市場發展機會和調查結果的詳細分析提供了有關商家如何應對欺詐交易和欺詐行為增加的信息，並提供了欺詐檢測和預防的策略建議。
  • 基準產業預測：此預測包括有關航空公司電子票券、遠端數位產品和遠端實體產品詐欺程度的數據。這些行業按線上和行動交易進行細分。這些數據還包括電子商務企業對反詐欺解決方案的採用和支出水準。
  • 瞻博網路研究競賽排行榜：分析商家詐欺偵測和預防產業的領導企業，並評估 16 家商家詐欺偵測和預防供應商的能力和能力。

樣品view

市場資料·預測報告:

市場趨勢·策略報告:

市場數據/預測報告

商家詐欺偵測和預防市場研究套件包括對由 55 個表格和 25,000 多個資料點組成的完整預測資料集的存取。此調查套件包括以下指標：

  • 電子商務商家的交易總數
  • 每年因 CNP（無卡）電子商務商家詐欺造成的總損失
  • 採用 FDP（詐欺偵測與預防）解決方案的電子商務商家總數
  • 電子商務商家在 FDP 解決方案上花費的總金額
  • 電子商務商家詐欺交易的百分比（基於金額）

這些指標由以下主要市場提供：

  • 航空公司電子機票
    • 航空公司電子機票
    • 航空機票
  • 遠端數位產品
    • 遠端線上數位產品
    • 遠端行動數位產品
  • 遠端實體產品
    • 遠端線上實體產品
    • 遠端行動實體產品
  • FDP 軟體
  • 詐欺率

Juniper Research 互動式預測 Excel 有以下功能：

  • 統計分析：您可以搜尋資料期間所有地區/國家顯示的特定指標。可以輕鬆修改圖表並將其匯出到剪貼簿。
  • 國家/地區資料工具：此工具可讓您查看預測期間內的所有區域和國家指標。使用者可以縮小搜尋欄中顯示的指標範圍。
  • 國家比較工具：使用者可以選擇國家/地區並針對特定國家/地區進行比較。該工具包括匯出圖表的功能。
  • 假設分析：讓使用者將預測指標與自己的假設進行比較。

目錄

市場趨勢·策略

第1章 重要點和策略性推薦事項

  • 重要點
  • 策略性推薦事項

第2章 市場形勢

  • 定義和範圍
  • 會員店詐欺的類型
    • 第一方欺詐
      • 退款欺詐
      • 友善的騙局
      • 濫用政策
    • ATO 詐欺
    • 其他
      • 乾淨的詐欺行為
      • 聯盟行銷詐欺
      • 殭屍網絡
      • 三角測量欺詐
      • 合成身分詐欺
  • 用於偵測和防止商家詐欺的解決方案
    • 商家詐欺偵測與預防工具
      • 生物辨識認證
      • 行為分析
      • 標記化
      • API
      • 3D安全認證
  • 實體產品與數位產品

第3章 新興加盟店詐欺防止市場

  • 主要主題和相關領域
  • 主要趨勢與當前市場推動因素
    • 電子商務快速崛起
    • 新的詐欺方法和策略
    • 客戶缺乏對新科技的了解
    • 深假
    • 降低業務成本
  • BNPL
  • 技術
    • AI
    • ML
    • 加盟店詐欺防止API
  • PSD2
  • 3DS2由於生物識別的交易的認證
    • 認證方法
    • 3DS2影響

第4章 市場區隔分析

  • 各種受到詐欺影響的商家
  • 遠端數位和實體產品
    • 數位商品
    • 物理性的商品
  • 主要課題
    • 有組織的詐欺
    • 缺乏生物特徵認證

競爭排行榜

第1章 Juniper Research的排行榜

第2章 業者簡介

  • 加盟店詐欺防止業者簡介
    • Accertify
    • ACI Worldwide
    • ClearSale
    • Discover Financial Services
    • Forter
    • Fraudio
    • Kount
    • Mastercard
    • Microsoft
    • Riskified
    • RSA Security
    • Signifyd
    • TransUnion
    • Vesta
    • Visa Acceptance Solutions
    • Worldpay
  • 評估手法

資料·預測

第1章 加盟店詐欺防止的預測:調查手法

第2章 機票的預測

  • 線上航空電子票
    • 線上航空電子票的年度發行總數
    • 詐欺線上航空公司電子票發行的總數
    • 詐欺線上航空公司電子票交易的總額
  • 行動航空m票
    • 行動航空m票的年度發行總數
    • 詐欺行動航空m票發行的總數
    • 詐欺行動航空m票交易的總額

第3章 遠隔數位商品的預測

  • 線上遠隔數位商品
    • 遠端線上購買數位產品的總交易金額（不包括機票）
    • 欺詐性遠端線上數位產品購買總數
    • 欺詐性遠端線上數位產品交易總額
  • 行動遠隔數位商品
    • 遠端行動數位產品購買交易總金額（不含機票）
    • 欺詐性遠端行動數位產品購買總數
    • 欺詐性遠端行動數位產品交易總額

第4章 遠隔物理商品的預測

  • 遠端線上實體產品
    • 遠端線上商品購買交易總數
    • 欺詐性遠端線上商品購買總數
    • 欺詐性遠端線上商品交易總額
  • 遠端行動實體產品
    • 遠端行動實體產品購買的交易總數
    • 欺詐性遠端行動產品購買總數
    • 欺詐性遠端行動產品交易總額

第5章 FDP軟體的預測

  • FDP支出
    • 在 FDP 上支出的電子商務商家數量
    • 包括航空公司在內的電子商務 CNP 交易總額
    • 電子商務商家的 FDP 支出總額
簡介目錄
KEY STATISTICS
eCommerce fraud value globally in 2024:$44 billion
eCommerce fraud value globally in 2029:$109 billion
Total eCommerce fraud value growth between 2024 & 2029:141%
Forecast period:2024-2029

'eCommerce Fraud to Exceed $107 Billion in 2029'

Overview

Our merchant fraud detection and prevention research suite provides detailed analysis of this rapidly changing market; allowing fraud prevention platform providers to gain an understanding of key fraud trends and challenges, potential growth opportunities, and the competitive environment.

Providing multiple options that can be purchased separately, the research suite includes access to data mapping the future growth of the merchant fraud detection and prevention market. The detailed study reveals the latest opportunities and trends within the market, and an insightful document containing an extensive analysis of 16 merchant fraud detection and prevention providers within the space. Aspects such as the use of artificial intelligence and machine learning by both providers and fraudsters, identity theft and synthetic identity use, and the challenges and new techniques for identifying legitimate customers are explored throughout the report. The coverage can also be purchased as a Full Research Suite, containing all of these elements, and includes a substantial discount.

Collectively, these elements provide an effective tool for understanding this constantly evolving market; allowing merchant fraud detection and prevention vendors and providers to set out their future strategies to tackle fraudulent activity among online purchases. Its unparalleled coverage makes this research suite an incredibly useful resource for gauging the future of this complex market.

Key Features

  • Market Dynamics: Insights into key fraud trends and market expansion challenges within the merchant fraud detection and prevention market. It addresses the challenges posed by the evolving nature of eCommerce fraud, technological advancements, barriers to adopting new merchant fraud solutions, including discerning between suspicious activity and genuine behaviour, and analysing multiple use cases where fraudulent activity occurred. The research also provides a future outlook on the landscape of eCommerce merchant fraud.
  • Key Takeaways & Strategic Recommendations: In-depth analysis of key development opportunities and findings within market, accompanied by key strategic recommendations for merchant fraud detection and prevention providers on how they can tackle the rise in fraudulent transactions and attempts.
  • Benchmark Industry Forecasts: The forecasts include data on fraud levels within airline eTickets, remote digital goods, and remote physical goods. These sectors are split by online and mobile transactions; allowing for splits by consumer eCommerce shopping preferences to be evaluated. The data also includes adoption and spend levels by eCommerce merchants on fraud prevention solutions.
  • Juniper Research Competitor Leaderboard: Key player capability and capacity assessment for 16 merchant fraud detection and prevention vendors, via the Juniper Research Competitor Leaderboard, featuring analysis around major players in the merchant fraud detection and prevention industry.

SAMPLE VIEW

Market Data & Forecasts Report:

The numbers tell you what's happening, but our written report details why, alongside the methodologies.

Market Trends & Strategies Report:

A comprehensive analysis of the current market landscape, alongside strategic recommendations.

Market Data & Forecasts Report

The market-leading research suite for the merchant fraud detection and prevention market includes access to the full set of forecast data, consisting of 55 tables and over 25,000 datapoints. Metrics in the research suite include:

  • Total Number of eCommerce Merchant Transactions.
  • Total Annual Transaction Value of CNP (Card-not-present) eCommerce Merchant Fraud.
  • Total Number of eCommerce Merchants Employing FDP (Fraud Detection & Prevention) Solutions.
  • Total Spend on FDP Solutions by eCommerce Merchants.
  • Proportion of eCommerce Merchant Transactions That Are Fraudulent, By Value.

These metrics are provided for the following key market verticals:

  • Airline eTickets
    • Airline eTickets
    • Airline mTickets
  • Remote Digital Goods
    • Remote Online Digital Goods
    • Remote Mobile Digital Goods
  • Remote Physical Goods
    • Remote Online Physical Goods
    • Remote Mobile Physical Goods
  • FDP Software
  • Fraud Rates

The Juniper Research Interactive Forecast Excel contains the following functionality:

  • Statistics Analysis: Users benefit from the ability to search for specific metrics, displayed for all regions and countries across the data period. Graphs are easily modified and can be exported to the clipboard.
  • Country Data Tool: This tool lets the user look at metrics for all regions and countries in the forecast period. Users can refine the metrics displayed via the search bar.
  • Country Comparison Tool: Users can select countries and compare each of them for specific countries. The ability to export graphs is included in this tool.
  • What-if Analysis: Here, users can compare forecast metrics against their own assumptions. 5 interactive scenarios.

Market Trends & Strategies Report

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.

Competitor Leaderboard Report

The Competitor Leaderboard report provides a detailed evaluation and market positioning for 16 leading vendors in the merchant fraud solution space. These vendors are positioned as an established leader, leading challenger, or disruptor and challenger based on capacity and capability assessments, including their use of technologies such as artificial intelligence, machine learning, and biometrics. The 16 merchant fraud detection and prevention vendors consist of:

  • Accertify
  • ACI Worldwide
  • ClearSale
  • Discover
  • Forter
  • Fraudio
  • Kount
  • Mastercard
  • Microsoft
  • Riskified
  • RSA Security
  • Signifyd
  • TransUnion
  • Vesta
  • Visa
  • Worldpay

This document is centred around the Juniper Research Competitor Leaderboard, a vendor positioning tool that provides an at a glance view of the competitive landscape in the merchant fraud detection and prevention market, backed by a robust methodology.

Table of Contents

Market Trends & Strategies

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 ($m), Split by Segment, 2024-2029
    • 2.5.1. Remote Physical Goods
      • Figure 2.5: Total Value of Fraudulent Remote Physical Goods Purchases Globally ($m), Split by 8 Key Regions, 2024-2029
    • 2.5.2. Remote Digital Goods
      • Figure 2.6: Total Value of Fraudulent Remote Digital Goods Purchases Globally ($m), Split by 8 Key Regions, 2024-2029

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. PSD2
    • 3.5.1. How PSD2 Affects Merchants
  • 3.6. 3DS2 & Biometric Authorisation of Transactions
    • 3.6.1. Methods of Authentication
      • i. OTPs
      • ii. Biometrics
        • Figure 3.2: Types of Biometric Authentication
    • 3.6.2. 3DS2 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, 2024-2029
      • 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, 2024-2029
      • 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

Competitor Leaderboard

1. Juniper Research Competitor Leaderboard

  • 1.1. Why Read This Report?
    • Table 1.1: Juniper Research Competitor Leaderboard Vendors: Merchant Fraud Detection & Prevention
    • Figure 1.2: Juniper Research Competitor Leaderboard - Merchant Fraud Detection & Prevention
    • Table 1.3: Juniper Research Competitor Leaderboard: Merchant Fraud Detection & Prevention Vendor Ranking
    • Table 1.4: Juniper Research Competitor Leaderboard Merchant Fraud Detection & Prevention - Heatmap

2. Vendor Profiles

  • 2.1. Merchant Fraud Prevention Vendor Profiles
    • 2.1.1. Accertify
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offerings
        • Figure 2.1: Accertify's Four Key Areas for Chargeback Management
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.2. ACI Worldwide
      • i. Corporate
        • Table 2.2: ACI Worldwide Revenue ($m), 2022-2023
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.3. ClearSale
      • i. Corporate
        • Table 2.3: ClearSale Revenue ($m), 2022-2023
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.4. Discover Financial Services
      • i. Corporate
        • Table 2.4: Discover Financial Services Revenue ($m), 2022-2023
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.5. Forter
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.6. Fraudio
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offerings
        • Figure 2.5: Fraudio's Centralised ML AI Brain
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.7. Kount
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.8. Mastercard
      • i. Corporate
        • Table 2.6: Mastercard Revenue ($m), 2022-2023
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.9. Microsoft
      • i. Corporate
        • Table 2.7: Microsoft Dynamics 365 Revenue ($m), 2022-2023
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.10. Riskified
      • i. Corporate
        • Table 2.8: Riskified Revenue ($m), 2022-2023
      • ii. Geographical Spread
      • iii. Key Clients and Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.11. RSA Security
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients and Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.12. Signifyd
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients and Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.13. TransUnion
      • i. Corporate
        • Table 2.9: TransUnion Revenue ($m), 2022-2023
      • ii. Geographical Spread
      • iii. Key Clients and Strategic Opportunities
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.14. Vesta
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients and Strategic Partnerships
      • iv. High-level View of Offerings
        • Figure 2.10: Visualisation Displaying Vestas Payment Guarantee
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.15. Visa Acceptance Solutions
      • i. Corporate
        • Table 2.11: Visa Revenue ($m), 2022-2023
      • ii. Geographical Spread
      • iii. Key Clients and Strategic Partnerships
      • iv. High-level View of Offerings
        • Figure 2.12: How Visa Acceptance Solutions' Payer Authentication Works
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.16. Worldpay
      • i. Corporate
        • Table 2.13: Worldpay Revenue ($m), 2022-2023
      • ii. Geographical Spread
      • iii. Key Clients and Strategic Partnerships
      • iv. High-level View of Offerings
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
  • 2.2. Juniper Research Leaderboard Assessment Methodology
    • 2.2.1. Limitations & Interpretations
      • Table 2.14: Juniper Research Merchant Fraud Prevention Assessment Criteria

Data & Forecasting

1. Merchant Fraud Prevention Forecast Methodology

  • 1.1. Methodology & Assumptions
    • Figure 1.1: Airline Tickets Forecast Methodology
    • Figure 1.2: Remote Digital Goods Forecast Methodology
    • Figure 1.3: Remote Physical Goods Forecast Methodology
    • Figure 1.4: FDP Software Forecast Methodology

2. Airline Tickets Forecasts

  • 2.1. Online Airline eTickets
    • 2.1.1. Total Number of Online Airline eTickets Issued per Annum
      • Figure & Table 2.1: Total Number of Online Airline eTickets Issued Globally per annum (m), Split by 8 Key Regions, 2024-2029
    • 2.1.2. Total Number of Fraudulent Online Airline eTickets Issued
      • Figure & Table 2.2: Total Number of Fraudulent Online Airline eTickets Issued Globally (m), Split by 8 Key Regions, 2024-2029
    • 2.1.3. Total Value of Fraudulent Online Airline eTicket Transactions
      • Figure & Table 2.3: Total Value of Fraudulent Online Airline eTicket Transactions Globally ($m), Split by 8 Key Regions, 2024-2029
  • 2.2. Mobile Airline mTickets
    • 2.2.1. Total Number of Mobile Airline mTickets Issued per Annum
      • Figure & Table 2.4: Total Number of Mobile Airline mTickets Issued per Annum Globally (m), Split by 8 Key Regions, 2024-2029
    • 2.2.2. Total Number of Fraudulent Mobile Airline mTickets Issued
      • Figure & Table 2.5: Total Number of Fraudulent Mobile Airline mTickets Issued Globally (m), Split by 8 Key Regions, 2024-2029
    • 2.2.3. Total Value of Fraudulent Mobile Airline mTicket Transactions
      • Figure & Table 2.6: Total Value of Fraudulent Mobile Airline mTicket Transactions Globally ($m), Split b 8 Key Regions, 2024-2029

3. Remote Digital Goods Forecasts

  • 3.1. Online Remote Digital Goods
    • 3.1.1. Total Transactions for Remote Online Digital Goods Purchases, Less Airline Tickets
      • Figure & Table 3.1: Total Number of Transactions for Remote Online Digital Goods Purchases, Less Airline Tickets, Globally (m), Split by 8 Key Regions, 2024-2029
    • 3.1.2. Total Number of Fraudulent Remote Online Digital Goods Purchases
      • Figure & Table 3.2: Total Number of Fraudulent Remote Digital Goods Purchases Globally (m), Split by 8 Key Regions, 2024-2029
    • 3.1.3. Total Value of Fraudulent Remote Online Digital Goods Transactions
      • Figure & Table 3.3: Total Value of Fraudulent Remote Online Digital Goods Transactions Globally ($m), Split by 8 Key Regions, 2024-2029
  • 3.2. Mobile Remote Digital Goods
    • 3.2.1. Total Transactions for Remote Mobile Digital Goods Purchases, Less Airline Tickets
      • Figure & Table 3.4: Total Number of Transactions for Remote Mobile Digital Goods Purchases, Less Airline Tickets, Globally (m), Split by 8 Key Regions, 2024-2029
    • 3.2.2. Total Number of Fraudulent Remote Mobile Digital Goods Purchases
      • Figure & Table 3.5: Total Number of Fraudulent Remote Mobile Digital Goods Purchased Globally (m), Split by 8 Key Regions, 2024-2029
    • 3.2.3. Total Value of Fraudulent Remote Mobile Digital Goods Transactions
      • Figure & Table 3.6: Total Value of Fraudulent Remote Mobile Digital Goods Transactions Globally ($m), Split by 8 Key Regions, 2024-2029

4. Remote Physical Goods Forecasts

  • 4.1. Remote Online Physical Goods
    • 4.1.1. Total Transactions for Remote Online Physical Goods Purchases
      • Figure & Table 4.1: Total Transactions for Remote Online Physical Goods Purchases Globally (m), Split by 8 Key Regions, 2024-2029
    • 4.1.2. Total Number of Fraudulent Remote Online Physical Goods Purchases
      • Figure & Table 4.2: Total Number of Fraudulent Remote Physical Goods Purchases Globally (m), Split by 8 Key Regions, 2024-2029
    • 4.1.3. Total Value of Fraudulent Remote Online Physical Goods Transactions
      • Figure & Table 4.3: Total Value of Fraudulent Remote Online Physical Goods Transactions Globally ($m), Split by 8 Key Regions, 2024-2029
  • 4.2. Remote Mobile Physical Goods
    • 4.2.1. Total Transactions for Remote Mobile Physical Goods Purchases
      • Figure & Table 4.4: Total Transactions for Remote Mobile Physical Goods Purchases Globally (m), Split by 8 Key Regions, 2024-2029
    • 4.2.2. Total Number of Fraudulent Remote Mobile Physical Goods Purchases
      • Figure & Table 4.5: Total Number of Fraudulent Remote Mobile Physical Goods Purchases (m), Split by 8 Key Regions, 2024-2029
    • 4.2.3. Total Value of Fraudulent Remote Mobile Physical Goods Transactions
      • Figure & Table 4.6: Total Value of Fraudulent Remote Mobile Physical Goods Transactions Globally ($m), Split by 8 Key Regions, 2024-2029

5. FDP Software Forecasts

  • 5.1. FDP Spend
    • 5.1.1. Number of eCommerce Merchants That Spend on FDP
      • Figure & Table 5.1: Number of eCommerce Merchants That Spend on FDP Globally (m), Split by 8 Key Regions, 2024-2029
    • 5.1.2. Total Value of eCommerce CNP Transactions, Including Airlines
      • Figure & Table 5.2: Total Value of eCommerce CNP Transactions, Including Airline, Globally ($m), Split by 8 Key Regions, 2024-2029
    • 5.1.3. Total FDP Spend by eCommerce Merchants
      • Figure 5.3: Total FDP Spend by eCommerce Merchants Globally ($m), Split by 8 Key Regions, 2024-2029