市場調査レポート
商品コード
1297905

不正オンライン決済:市場予測・新たな脅威・セグメント分析 (2023~2028年)

Online Payment Fraud: Market Forecasts, Emerging Threats & Segment Analysis 2023-2028

出版日: | 発行: Juniper Research Ltd | ページ情報: 英文 | 納期: 即日から翌営業日

価格
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本日の銀行送金レート: 1GBP=195.02円
不正オンライン決済:市場予測・新たな脅威・セグメント分析 (2023~2028年)
出版日: 2023年06月26日
発行: Juniper Research Ltd
ページ情報: 英文
納期: 即日から翌営業日
  • 全表示
  • 概要
  • 目次
概要

2023年から2028年にかけての不正オンライン決済による加盟店の損失額は3,620億米ドルを超え、2028年だけで910億ドルの規模に達しています。新興市場におけるEコマース取引の増加がこの拡大を後押ししています。新興市場の加盟店は、AIを利用した攻撃の増加など、新たな脅威に直面しています。

当レポートでは、不正オンライン決済の動向を調査し、不正による損失額の推移・予測、不正のタイプによる詳細分析、不正検知および防止ソリューションの市場規模の推移・予測、主要ベンダーの競合情勢、企業プロファイル、将来の展望、戦略的提言などをまとめています。

本調査パッケージの構成:

  • 市場動向・戦略(PDF)
  • 競合リーダーボード(PDF)
  • データおよび予測(PDF・Excel)
  • オンラインデータプラットフォームへの12ヶ月のアクセス

主要市場統計

加盟店損失額 (2023年) 380億米ドル
加盟店損失額 (2028年) 910億米ドル
加盟店損失総額 (2023~2028年) 3620億米ドル

目次

市場動向・戦略

第1章 重要ポイント・戦略的提言

第2章 市場情勢

  • 不正のタイプ
    • ファーストパーティによる不正
    • サードパーティーによる不正
    • フィッシング
    • その他
  • 物理的商品・デジタル商品
    • 物理的商品の遠隔購入
    • デジタル商品の遠隔購入
  • デジタル不正の主な動向
  • 代替決済の手法
    • 決済:ダイナミクスの変化とエコシステムの拡大
    • オンラインバンキング
    • BNPL
    • CBDC
    • 暗号通貨

第3章 市場力学

  • 今後の課題とオープンAPI
  • 方程式におけるフィンテック
  • 消費者行動とボット:詐欺師にとっては豊富な機会
  • リアルタイム決済
  • 不正と決済
  • デジタルIDと不正
  • 3DS 2.0とトランザクションの生体認証

第4章 セグメント分析

  • バンキング・送金
  • 物理的商品およびデジタル商品の遠隔購入
  • 航空会社

競合リーダーボード

第1章 Juniper Researchの競合のリーダーボード

第2章 企業プロファイル

  • 不正オンライン決済対策:ベンダープロファイル
    • Accertify
    • ACI Worldwide
    • Experian
    • Featurespace
    • Feeedzai
    • FICO
    • Fiserv
    • Fraudio
    • GBG
    • Kount
    • LexisNexis Risk Solutions
    • Microsoft
    • NuData Security
    • Riskified
    • RSA Security
    • SAS
    • Signifyd
    • TransUnion
    • Vesta
    • Visa Acceptance Solutions
    • Worldpay
  • Juniper Research Leaderboard:評価手法

データおよび予測

第1章 市場概要

  • 不正のタイプ
  • 調査手法・前提
  • 予測:サマリー
    • CNP不正
    • 不正取引総額

第2章 航空会社の電子チケット不正:市場予測

  • 航空会社の電子チケット不正取引額
  • 航空会社の電子チケット不正:オンライン vs モバイル
  • 航空会社の電子チケットの不正行為率

第3章 デジタル商品の不正遠隔購入:市場予測

  • デジタル商品の不正遠隔取引額
  • デジタル商品の不正遠隔購入:オンライン vs モバイル
  • デジタル商品の遠隔購入の不正率

第4章 物理的商品の不正遠隔購入:市場予測

  • 物理的商品の不正遠隔取引額
  • 商品の不正遠隔購入:オンライン vs モバイル
  • 遠隔購入の不正率

第5章 不正送金:市場予測

  • 不正送金取引額
  • 不正送金:オンライン vs モバイル
  • 不正送金の割合

第6章 不正デジタルバンキング:市場予測

  • デジタルバンキング不正取引額
  • デジタルバンキング不正:オンラインvsモバイル
  • デジタルバンキング不正率

第7章 不正行為の検出および防止ソリューション

  • 不正検知および防止ソリューションの市場規模
目次

REPORT OVERVIEW

Juniper Research's “Online Payment Fraud ” research report provides a detailed evaluation of the market, including different fraud types, the impact of the increase in alternative payment types, the future challenges within Open Banking APIs, and differing types of fraud in a variety of segments including banking, remote digital and physical goods and airlines. In addition, this report covers market opportunities; providing strategic insights into the development of new technologies, such as AI and machine learning, and key steps that are important for both vendors and merchants to take to manage increasing fraud risks.

The report also positions 21 online payment fraud detection and prevention vendors in our Juniper Research Competitor Leaderboard; providing an invaluable resource for stakeholders seeking to understand the competitive landscape in the market.

The research suite also contains a detailed dataset; providing forecasts for 60 countries across a wide range of different metrics, including fraud value for remote digital and physical goods purchases and online transactions, money transfer fraud rate and digital banking fraud rate, the splits for fraud rate by vertical, as well as including the split for mobile and online transactions.

This research suite comprises of:

  • Market Trends & Strategies (PDF)
  • Competitor Leaderboard (PDF)
  • Data & Forecasts (PDF & Excel)
  • 12 Months' Access to harvest Online Data Platform

Key Market Statistics

Merchant Losses in 2023:$38 billion
Merchant Losses in 2028:$91 billion
Total Merchant Losses 2023-2028:$362 billion

KEY FEATURES

  • Market Dynamics: A strategic analysis of the major drivers, challenges, and innovations shaping the adoption and development of fraud detection and prevention solutions:
    • COVID-19 pandemic's impact on the online payment fraud market and the ongoing influence from it.
    • Future strategic direction and market outlook for fraud detection and prevention vendors.
    • Key drivers for fraud detection and prevention vendors, including an increase in data provided from merchants, the implementation of AI and ML and their impact on fraud detection and prevention, and the growing needs of SMEs to access sophisticated fraud prevention systems.
  • Segment Analysis: A segment analysis of banking and money transfer, remote physical and digital goods and airlines, looking into key challenges, key trends and future outlook. This gives readers insight into the markets with a growing need for improvement within fraud detection and prevention.
  • Benchmark Industry Forecasts: Includes forecasts for the total fraudulent transaction value for airline eTicketing, remote digital goods, remote physical goods, money transfer and digital banking, as well as the total spend on fraud detection and prevention. This data is split by online and mobile transactions, and for our 8 key forecast regions and 60 countries:
  • North America:
    • Canada, US.
  • Latin America:
    • Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Peru, Uruguay.
  • West Europe:
    • Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, UK.
  • Central & East Europe:
    • Croatia, Czech Republic, Hungary, Poland, Romania, Russia, Turkey, Ukraine.
  • Far East & China:
    • China, Hong Kong, Japan, South Korea.
  • Indian Subcontinent:
    • Bangladesh, India, Nepal, Pakistan.
  • Rest of Asia Pacific:
    • Australia, Indonesia, Malaysia, New Zealand, Philippines, Singapore, Thailand, Vietnam.
  • Africa & Middle East:
    • Algeria, Egypt, Israel, Kenya, Kuwait, Nigeria, Qatar, Saudi Arabia, South Africa, United Arab Emirates.
  • Juniper Research Competitor Leaderboard: Key player capability and capacity assessment for 21 online payment fraud detection and prevention vendors:
    • Accertify
    • ACI Worldwide
    • Experian
    • Featurespace
    • Feedzai
    • FICO
    • Fiserv
    • Fraudio
    • GBG
    • Kount
    • LexisNexis
    • Microsoft
    • NuData Security
    • Riskified
    • RSA Security
    • SAS
    • Signifyd
    • TransUnion
    • Vesta
    • Visa
    • Worldpay

KEY QUESTIONS ANSWERED

  • 1. What will the total online payment fraud value be in 2028?
  • 2. What impact will AI and machine learning have on fraud detection and prevention?
  • 3. Who are the leading vendors in the fraud detection and prevention market?
  • 4. What impact do emerging payments have on online fraud?
  • 5. What are the next steps for vendors in fraud detection and prevention?

COMPANIES REFERENCED

  • Interviewed: Feedzai, Fraudio, Kount, LexisNexis, Vesta, Visa.
  • Included in Juniper Research Competitor Leaderboard: Accertify, ACI Worldwide, Experian, Featurespace, Feedzai, FICO, Fiserv, Fraudio, GBG, Kount, LexisNexis Risk Solutions, Microsoft, NuData Security, Riskified, RSA Security, SAS, Signifyd, TransUnion, Vesta, Visa, Worldpay.
  • Mentioned: 4Stop, Accelitas, ACI Worldwide, Acuris, Adobe, AFS (Advanced Financial Solutions), Allianz, ALTO, American Express, Aspenware, AT&T, AU10TIX, Authorize.net, AWS, Axerve, Bailhang Credit, Bank of Singapore, Barclaycard Payments, BAV (Bank Account Validation), BehavioSec, BigCommerce, Black Opal, Blibli, BlueSnap, BNP Paribas, Boemska, Braintree, Bukalapak, Cashplus, Cayan, Centro, Chargebacks911, checkout.com, CISCO, Citrix, Cognizant, Confused.com, ConnexPay, CredoLab, Crypto.com, Cybersource, Dell, Deloitte, Delta Air Lines, Dentsu, Deutsche Bank, Diebold Nixdorf, eBay, Emailage, Entersekt, Entrée Capital, Entrust Datacard, Equifax, Esure, Etisalat, Eway, Fidelity Management & Research Company, Finxact, Fischer, GDS Link, General Atlantic, GeoComply, Global Payments, HSBC, IBM, ID R&D, IDology, Innovalor, Invation, Jack Henry & Associates, Inc, JPMorgan Chase, Kaidee, Kenbi, KPMG, Landsbankinn, Laurentian Bank, Lego, LeoVegas, Mastercard, Midigator, MySQL, NatWest, NewSuite, NorthRow, NortonLife Lock, NTT Data, Oracle, OTP Bank, P97, PassFort, Payoneer, Philippines Security Bank Corporation, Phonelink, PingIdentity, Pitango Venture Capital, Plaid, Playtech, Praxis, Primer, Provenir, PWC, Qumra Capital, Railsr, RedAbierta, Regily, RocketFuel Blockchain, Inc, Sage, Salesforce Commerce Cloud, Santander Bank, SAP, Sapiens, Scudetto, Sekura, SEON, Shopify, Shyft Network, Soldo, SPhonic, SumUp, Symphony Technology Group, Synectics Solutions, Temenos, Teradata, Thales, The Access Group, Threat Fabric, TruNarrative, Trunarrative, TSYS, UK Finance, Visualsoft, Viva Wallet, Vtex, WiPay, Worldline, Zilch.

DATA & INTERACTIVE FORECAST

Juniper Research's “Online Payment Fraud ” forecast suite includes splits for:

  • Airline eTicket
  • Remote Physical Goods
  • Remote Digital Goods
  • Money Transfer
  • Banking

Metrics include the total card not present fraud transaction value split by online and mobile, total spend on fraud detection and prevention, fraud rate by value.

Geographical splits: 60 countries

Number of tables: Over 67 tables

Number of datapoints:Over 30,000 datapoints

harvest:Our online data platform, harvest, contains the very latest market data and is updated throughout the year. This is a fully featured platform, enabling clients to better understand key data trends and manipulate charts and tables; overlaying different forecasts within the one chart - using the comparison tool. Empower your business with our market intelligence centre, and receive alerts whenever your data is updated.

Interactive Excels (IFxl): Our IFxl tool enables clients to manipulate both forecast data and charts, within an Excel environment, to test their own assumptions using the interactive scenario tool and compare selected markets side by side in customised charts and tables. IFxls greatly increase a client's ability to both understand a particular market and to integrate their own views into the model.

FORECAST SUMMARY

  • Merchant losses from online payment fraud will exceed $362 billion globally between 2023 to 2028, with losses of $91 billion alone in 2028. A rise in eCommerce transactions in emerging markets is driving this growth. Merchants there are facing new threats, such as an increased use of AI for attacks. Online payment fraud is where cybercriminals conduct false or illegal transactions online, using a number of different fraud strategies, such as phishing, business email compromise or account takeover.
  • Underpinned by a robust scoring methodology, the new Competitor Leaderboard ranked the top 21 fraud detection and prevention vendors, using criteria such as the relative size of their customer base, completeness of their solutions and their future business prospects.
  • The top 5 vendors for 2023:
    • LexisNexis Risk Solutions
    • Experian
    • ACI Worldwide
    • Visa
    • FICO
  • Leading players scored well based on the breadth of their anti-fraud orchestration capabilities, as well as their use of AI for analysing trends in fraudster behaviour. In order to stay ahead of the competition, vendors must utilise data collected throughout the whole eCommerce process to further develop their fraud detection and prevention solutions through training and advancing AI models.
  • eCommerce payment vendors need to offer dashboards and data visualisations to their smaller SME customers. At present, SMEs lack access to good customer analytics, and this data could highlight consumer purchasing behaviours, as well as providing insights into payment method popularity and fraud. By offering additional services to SMEs, eCommerce payment vendors can differentiate their portfolios in an increasingly competitive and commoditised market.

Table of Contents

Market Trends & Strategies

1. Key Takeaways and Strategic Recommendations

  • 1.1. Key Takeaways
  • 1.2. Strategic Recommendations

2. Market Landscape

  • 2.1. Introduction
  • 2.2. Types of Fraud
    • 2.2.1. First-party Fraud
      • i. Fronting
      • ii. Address Fronting
      • iii. Friendly Fraud
      • iv. Chargeback Fraud
    • 2.2.2. Third-party Fraud
      • i. Identity Fraud and KYC (Synthetic Identity)
      • ii. Account Takeover
      • iii. Silent Fraud
    • 2.2.3. Phishing
      • i. Phishing
      • ii. Whaling
      • iii. Pharming
    • 2.2.4. Other Types of Fraud
      • i. Clean Fraud
      • ii. Affiliate Fraud
      • iii. Re-shipping
      • iv. Botnets
      • v. Triangulation
      • vi. Pagejacking
  • 2.3. Physical and Digital Goods
    • Figure 2.1: Total Value of Fraudulent Transactions, Split by Segment, Global 2023-2028
    • 2.3.1. Remote Physical Goods
      • Figure 2.2: Graph Showing Total Remote Physical Goods Fraud ($m), Split by 8 Key Regions, 2023-2028
    • 2.3.2. Remote Digital Goods
      • Figure 2.3: Graph Showing Total Remote Digital Goods Fraud ($m), Split by 8 Key Regions, 2023-2028
  • 2.4. Key Trends in Digital Fraud
  • 2.5. Alternative Payment Methods
    • 2.5.1. Payments: Changing Dynamics & Expanding Ecosystem
    • 2.5.2. Online Banking
      • i. Online Banking Fraud
      • ii. Online Banking Fraud Prevention Solutions
    • 2.5.3. BNPL
      • i. BNPL Fraud
      • ii. BNPL Fraud Prevention Solutions
    • 2.5.4. CBDCs
      • i. CBDC Fraud
      • ii. CBDC Fraud Prevention
    • 2.5.5. Cryptocurrencies
      • i. Cryptocurrency Fraud
      • ii. Cryptocurrency Fraud Prevention

3. Market Dynamics

  • 3.1. Introduction
  • 3.2. Future Challenges and Open APIs
    • 3.2.1. Open Banking APIs
    • 3.2.2. The API in the Machine
    • 3.2.3. FAPI (Financial-grade API)
    • 3.2.4. Open Banking, CIBA (Client-initiated Back Channel Authorisation) and Premium APIs
    • 3.2.5. PSD2. Overview
    • 3.2.6. PSD2. State of the Nations
      • Figure 3.1: Ravelin 3DS2 Statistics
    • 3.2.7. RTS (Regulatory Technical Standards) Implications for Payment Service Providers
      • i. Fraud Detection
      • ii. Merger of Home Working, Personal Devices and Corporate Access
      • iii. Exemptions from SCA
      • iv. Implications
      • v. Regulation Differences
  • 3.3. The Fintech in the Equation
    • 3.3.1. Establishing Resilient Fintech Products
      • Figure 3.2: Visualisation Displaying the 10 Step Framework Process for Establishing Resilient Fintech Products
  • 3.4. Consumer Behaviour and Bots, a Wealth of Opportunities for Fraudsters
    • 3.4.1. Type of API Attacks
      • i. Unauthorised API Requests
      • ii. Unauthorised Modification of Request Or Token Responses
      • iii. Unauthorised Token Use
      • iv. Exposure and Modification of API Response Data
    • 3.4.2. API Authentication Security
    • 3.4.3. Avoiding Logic Abuse
  • 3.5. Real-time Payments
    • Table 3.3: Global Instant Payments Market Status
  • 3.6. Fraud and Payments
  • 3.7. Digital Identity & Fraud
    • 3.7.1. Decentralised Identity Wallets
      • Figure 3.4: Visualisation of Decentralised Identity Wallets
      • i. Benefits of Decentralised Identity Wallets
  • 3.8. 3DS 2.0 & Biometric Authorisation of Transactions
    • 3.8.1. Authentication Mechanisms
      • i. OTP (One-time Passwords)
      • ii. Biometrics
      • iii. SIM Swap Fraud
    • 3.8.2. Further 3DS Implications
      • i. 3D Secure Authentication Failure
    • 3.8.3. Next Steps & Regional Outlook

4. Segment Analysis

  • 4.1. Introduction
  • 4.2. Banking and Money Transfer
    • 4.2.1. Key Challenge: Advanced Persistent Threats
      • Table 4.1: Phases of Threats
    • 4.2.2. Key Challenge: Open Banking & Multi-part Attacks
    • 4.2.3. Key Trends & Outlook in the Financial Sector
  • 4.3. Remote Physical and Digital Goods
    • 4.3.1. Physical Goods
      • Table 4.2: Revenue & Growth of Selected eCommerce Players
      • i. Impact of COVID-19
      • ii. Impact of Inflation and Cost of Living Crisis
        • Figure 4.3: Total Transaction Volume Physical Goods (m), Split by 8 Key Regions, 2022-2027
    • 4.3.2. Digital Goods
      • i. Impact of Inflation and Cost of Living Crisis
      • ii. Gaming
      • iii. Music
      • iv. Video
        • Figure 4.4: Share of Netflix Subscribers Worldwide, 2023
      • v. eBooks
      • vi. Adult Content
      • vii. Ticketing
    • 4.3.3. Key Challenge: Synthetic Identity
      • i. Detection
      • ii. Activity History
    • 4.3.4. Key Challenge: Deepfakes
    • 4.3.5. Account Takeover
      • Figure 4.5: Visualisations Displaying Process of ATO (Account Takeover)
    • 4.3.6. Omnichannel Fraud
    • 4.3.7. Key Trends & Outlook in eRetail
    • 4.3.8. Machine Learning
      • Figure 4.6: Goal Revenue Optimisation Fraud Management
  • 4.4. Airlines
    • 4.4.1. Key Challenge: Security
    • 4.4.2. Key Challenge: Third-party Attacks
    • 4.4.3. Key Challenge: Chargebacks
    • 4.4.4. Key Trends & Future Outlook in the Airline Sector

Competitor Leaderboard

1. Juniper Research Competitor Leaderboard

  • 1.1. Why Read This Report
    • Table 1.1: Juniper Research Competitor Leaderboard Vendors: Online Payment Fraud
    • Figure 1.2: Juniper Research Competitor Leaderboard - Online Payment Fraud
    • Table 1.3: Juniper Research Competitor Leaderboard: Online Payment Fraud Vendor Ranking
    • Table 1.4: Juniper Research Competitor Leaderboard Online Payment Fraud - Heatmap
    • Table 1.5: Juniper Research Competitor Leaderboard Online Payment Fraud - Heatmap - Continued

2. Company Profiles

  • 2.1. Online Payment Fraud Vendor Profiles
    • 2.1.1. Accertify
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.2. ACI Worldwide
      • i. Corporate
        • Table 2.1: ACI Worldwide's Revenue ($bn), 2016-2022
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-Level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.3. Experian
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.4. Featurespace
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.5. Feeedzai
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • i. High-level View of Offering
      • ii. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.6. FICO
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.7. Fiserv
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.8. Fraudio
      • 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
    • 2.1.9. GBG
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.10. Kount
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.11. LexisNexis Risk Solutions
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.12. Microsoft
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.13. NuData Security
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.14. Riskified
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.15. RSA Security
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.16. SAS
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.17. Signifyd
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
        • Figure 2.2: Visualisation Displaying Signifyd's Commerce Protection Platform
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.18. TransUnion
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.19. Vesta
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
        • Figure 2.: Visualisation Displaying Vestas Payment Guarantee
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.20. Visa Acceptance Solutions
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
    • 2.1.21. Worldpay
      • i. Corporate
      • ii. Geographical Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Offering
      • v. Juniper Research's View: Key Strengths & Strategic Opportunities
  • 2.2. Juniper Research Leaderboard Assessment Methodology
    • 2.2.1. Limitations & Interpretations
      • Table 2.: Juniper Research Competitor Leaderboard Scoring Criteria

Data & Forecasting

1. Market Overview

  • 1.1. Introduction
  • 1.2. Types of Fraud
    • 1.2.1. First-party Fraud
      • i. Fronting
      • ii. Address Fronting
      • iii. Friendly Fraud
      • iv. Chargeback Fraud
    • 1.2.2. Third-party Fraud
      • i. Identity Fraud and KYC (Know Your Customer) Synthetic Identity
      • ii. Account Takeover
      • iii. Silent Fraud
    • 1.2.3. Phishing
      • i. Phishing
      • ii. Whaling
      • iii. Pharming
    • 1.2.4. Other Types of Fraud
      • i. Clean Fraud
      • ii. Affiliate Fraud
      • iii. Re-shipping
      • iv. Botnets
      • v. Triangulation
      • vi. Pagejacking
  • 1.3. Introduction
  • 1.4. Methodology & Assumptions
    • Figure 1.1: FDP Forecast Methodology
  • 1.5. Forecast Summary
    • 1.5.1. CNP Fraud
      • Figure & Table 1.2: Total CNP Fraud Value ($m), Remote Goods Purchases, Airline Tickets, Split by Goods Type 2023-2028
    • 1.5.2. Total Value of Fraudulent Transactions
      • Figure 1.3: Total Value of Fraudulent Transactions ($m), Split by eCommerce Segment, 2023-2028

2. Airline eTicketing Fraud: Market Forecasts

  • 2.1. Airline eTicketing Fraud Transaction Value
    • Figure & Table 2.1: Total Airline eTicket Fraudulent Value ($m), Split by 8 Key Regions, 2023-2028
  • 2.2. Airline eTicketing Fraud: Online VS Mobile
    • Figure & Table 2.2: Total Airline eTicket Fraudulent Value ($m), Split by Sales Channel, 2023-2028
  • 2.3. Airline eTicketing Fraud Rates
    • Figure & Table 2.3: Total Airline eTicketing Fraud Rate by Value (%), Split by 8 Key Regions, 2023-2028

3. Remote Digital Goods Purchases Fraud: Market Forecasts

  • 3.1. Remote Digital Goods Fraud Transaction Value
    • Figure 3.1: Total Remote Digital Goods Fraudulent Value ($m), Split by 8 Key Regions, 2023-2028
  • 3.2. Remote Digital Goods Fraud: Online vs Mobile
    • Figure & Table 3.2: Total Remote Digital Goods Fraudulent Value ($m), Split by Sales Channel, 2023-2028
  • 3.3. Remote Digital Goods Fraud Rates
    • Figure & Table 3.3: Total Remote Digital Goods Fraud Rates by Value (%), Split by 8 Key Regions, 2023-2028

4. Remote Physical Goods Purchases Fraud: Market Forecasts

  • 4.1. Remote Physical Fraud Transaction Value
    • Figure & Table 4.1: Total Remote Physical Goods Fraudulent Value ($m), Split by 8 Key Regions, 2023-2028
  • 4.2. Remote Physical Goods Fraud Online vs Mobile
    • Figure & Table 4.2: Total Remote Physical Goods Fraudulent Value ($m), Split by Sales Channel, 2023-2028
  • 4.3. Remote Physical Fraud Rates
    • Figure & Table 4.3: Total Remote Physical Goods Fraud Rate by Value (%), Split by 8 Key Regions, 2023-2028

5. Money Transfer Fraud: Market Forecasts

  • 5.1. Money Transfer Fraud Transaction Value
    • Figure & Table 5.1: Total Money Transfer Value ($m), Split by 8 KeyRegions, 2023-2028
    • 5.1.1. Money Transfer Fraud: Online vs Mobile
      • Figure & Table 5.2: Total Money Transfer Fraudulent Value ($m), Split by Sales Channel, 2023-2028
  • 5.2. Money Transfer Fraud Rates
    • Figure & Table 5.3: Total Money Transfer Fraud Rate by Value (%), Split by 8 Key Regions, 2023-2028

6. Digital Banking Fraud: Market Forecasts

  • 6.1. Digital Banking Fraud Transaction Value
    • Figure & Table 6.1: Total Digital Banking Fraudulent Value ($m), Split by 8 Key Regions, 2023-2028
  • 6.2. Digital Banking Fraud: Online vs Mobile
    • Figure & Table 6.2: Total Digital Banking Transaction Value ($m), Split by Sales Channel, 2023-2028
  • 6.3. Digital Banking Fraud Rates
    • Figure & Table 6.3: Total Digital Banking Fraud Rate by Value (%), Split by 8 Key Regions, 2023-2028

7. Fraud Detection and Prevention Solutions

  • 7.1. Fraud Detection and Prevention Solutions Market Size
    • Figure & Table 7.1: Total Annual FDP Spend ($m), Split by 8 Key Regions 2023-2028