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市場調査レポート
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加盟店不正防止の世界市場：2024-2029年

Global Merchant Fraud Prevention Market: 2024-2029

出版日
ページ情報
英文
納期
即日から翌営業日
価格
価格表記: GBPを日本円(税抜)に換算
本日の銀行送金レート: 1GBP=194.57円
加盟店不正防止の世界市場：2024-2029年
出版日: 2024年10月07日
発行: Juniper Research Ltd
ページ情報: 英文
納期: 即日から翌営業日
GIIご利用のメリット
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  • 概要
  • 目次
概要
主要統計
世界のeコマース詐欺被害額 (2024年)： 440億米ドル
世界のeコマース詐欺被害額 (2029年)： 1,090億米ドル
eコマース詐欺被害額の成長率 (2024～2029年)： 141%
予測期間： 2024-2029年

"eコマース詐欺被害額は2029年には1,070億米ドルを超える見通し”

当レポートでは、世界の加盟店不正防止の市場を調査し、主要な不正行為の種類と概要、不正行為の検出・防止に使用されるソリューションと主要技術、各種区分別の被害額およびFDPソフトウェア支出額の推移・予測、Juniper Research競合リーダーボードによる競合情勢の分析などをまとめています。

主な特徴

  • 市場力学：市場における主要な不正行為の動向と市場拡大の課題についての洞察。eコマース詐欺の進化がもたらす課題、技術の進歩 、不審な行為と真正な行為の識別、不正行為が発生した複数のユースケースの分析など、新たな加盟店不正ソリューションの導入における障壁について述べています。また、eコマース加盟店による詐欺被害の状況に関する将来の展望も示しています。
  • 主な要点と戦略的提言：市場の発展の主な機会と調査結果を詳細に分析し、不正取引や不正行為の増加に対処する方法について、加盟店の不正検知・防止プロバイダーに対する戦略的提言を提供します。
  • ベンチマーク業界予測：予測には、航空会社のeチケット、遠隔デジタル商品、遠隔フィジカル商品における不正行為のレベルに関するデータが含まれています。これらのセクターはオンラインおよびモバイルトランザクションで分割されています。このデータには、eコマース事業者による不正防止ソリューションの導入・支出レベルも含まれています。
  • Juniper Researchの競合リーダーボード：加盟店不正検知・防止業界の主要企業を分析し、加盟店不正検知・防止ベンダー16社の能力とキャパシティを評価します。

サンプルビュー

市場データ・予測レポート：

市場動向・戦略レポート：

市場データ・予測レポート

加盟店不正検知・防止市場の調査スイートには、55の表と25,000以上のデータポイントからなる予測データ一式へのアクセスが含まれています。調査スイートには以下の指標が含まれます：

  • eコマース加盟店の取引総数
  • CNP (カード非提示型) eコマース加盟店による不正行為の年間総被害額
  • FDP (不正検知・防止) ソリューションを採用しているeコマース加盟店の総数
  • eコマース加盟店によるFDPソリューションへの総支出額
  • eコマース加盟店による不正取引の割合 (金額ベース)

これらの指標は、以下の主要市場別に提供されています：

  • 航空eチケット
    • 航空eチケット
    • 航空mチケット
  • 遠隔デジタル商品
    • 遠隔オンラインデジタル商品
    • 遠隔モバイルデジタル商品
  • 遠隔フィジカル商品
    • 遠隔オンラインフィジカル商品
    • 遠隔モバイルフィジカル商品
  • FDPソフトウェア
  • 不正率

Juniper Research Interactive Forecast Excelには以下の機能があります：

  • 統計分析：データ期間中の全地域・国について表示される特定の指標を検索できます。グラフは簡単に変更でき、クリップボードへのエクスポートも可能です。
  • 国別データツール：このツールでは、予測期間中のすべての地域と国の指標を見ることができます。ユーザーは検索バーで表示される指標を絞り込むことができます。
  • 国別比較ツール：ユーザーは国を選択し、特定の国についてそれぞれを比較することができます。このツールには、グラフをエクスポートする機能が含まれています。
  • What-if分析：ユーザーは予測指標を独自の前提条件と比較することができます。

目次

市場動向・戦略

第1章 重要ポイントと戦略的推奨事項

  • 重要ポイント
  • 戦略的推奨事項

第2章 市場情勢

  • 定義と範囲
  • 加盟店不正の種類
    • ファーストパーティ詐欺
      • チャージバック詐欺
      • フレンドリー詐欺
      • ポリシーの濫用
    • ATO詐欺
    • その他
      • クリーン詐欺
      • アフィリエイト詐欺
      • ボットネット
      • 三角測量詐欺
      • 合成ID詐欺
  • 加盟店の不正行為の検出と防止に使用されるソリューション
    • 加盟店不正検出および防止ツール
      • 生体認証
      • 行動分析
      • トークン化
      • API
      • 3Dセキュア認証
  • フィジカル商品とデジタル商品

第3章 新興の加盟店不正防止市場

  • 主なテーマと関連する分野
  • 主な動向と現在の市場促進要因
    • eコマースの急速な台頭
    • 新たな詐欺手法と戦術
    • 顧客の新しい技術についての知識の不足
    • ディープフェイク
    • 事業コストの削減
  • BNPL
  • 技術
    • AI
    • ML
    • 加盟店不正防止API
  • PSD2
  • 3DS2と生体認証による取引の承認
    • 認証方法
    • 3DS2の影響

第4章 セグメント分析

  • 詐欺の影響を受けるさまざまな加盟店
  • 遠隔デジタル＆フィジカル商品
    • デジタル商品
    • フィジカル的な商品
  • 主な課題
    • 組織的詐欺
    • 生体認証の欠如

競合リーダーボード

第1章 Juniper Researchのリーダーボード

第2章 ベンダープロファイル

  • 加盟店不正防止ベンダープロファイル
    • Accertify
    • ACI Worldwide
    • ClearSale
    • Discover Financial Services
    • Forter
    • Fraudio
    • Kount
    • Mastercard
    • Microsoft
    • Riskified
    • RSA Security
    • Signifyd
    • TransUnion
    • Vesta
    • Visa Acceptance Solutions
    • Worldpay
  • 評価手法

データ・予測

第1章 加盟店不正防止の予測：調査手法

第2章 航空券の予測

  • オンライン航空eチケット
    • オンライン航空eチケットの年間発行総数
    • 不正オンライン航空会社eチケット発行の総数
    • 不正オンライン航空会社eチケット取引の総額
  • モバイル航空mチケット
    • モバイル航空mチケットの年間発行総数
    • 不正モバイル航空mチケット発行の総数
    • 不正モバイル航空mチケット取引の総額

第3章 遠隔デジタルグッズの予測

  • オンライン遠隔デジタルグッズ
    • 航空券を除く遠隔オンラインデジタル商品購入の総取引額
    • 不正遠隔オンラインデジタル商品購入の総数
    • 不正遠隔オンラインデジタル商品取引の総額
  • モバイル遠隔デジタルグッズ
    • 航空券を除く遠隔モバイルデジタル商品購入の総取引額
    • 不正遠隔モバイルデジタル商品購入の総数
    • 不正遠隔モバイルデジタル商品取引の総額

第4章 遠隔フィジカル商品の予測

  • 遠隔オンラインフィジカル商品
    • 遠隔オンライン物品購入の総取引数
    • 不正遠隔オンライン物品購入の総数
    • 不正遠隔オンライン物品取引の総額
  • 遠隔モバイルフィジカル商品
    • 遠隔モバイルフィジカル商品購入の総取引数
    • 不正遠隔モバイル商品購入の総数
    • 不正遠隔モバイル商品取引の総額

第5章 FDPソフトウェアの予測

  • FDP支出
    • FDPに支出するeコマース加盟店の数
    • 航空会社を含むeコマースCNP取引総額
    • eコマース加盟店によるFDP支出総額
目次
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