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市場調查報告書

線上付款詐騙

Online Payment Fraud

出版商 Juniper Research 商品編碼 357197
出版日期 內容資訊 英文
商品交期: 最快1-2個工作天內
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線上付款詐騙 Online Payment Fraud
出版日期: 2016年05月03日 內容資訊: 英文
簡介

本報告提供全球數位貿易市場上線上FDP (詐欺檢測 & 防止) ,尤其以網路銀行、CNP (非見面交易) 為焦點的分析,連網型設備相關的安全風險,主要的垂直部門的線上付款交易的詐欺檢測、管理,12家主要供應商所提供的解決方案分析、評估所採用的即時FDP解決方案的重要要件的策略性見解,主要企業的採訪,詐欺偵測、管理軟體的規模、成長的預測,及非法貿易規模的預測等彙整資料。

第1章 詐欺檢測 & 防止:預測手法 & 摘要

  • 簡介
    • 扣款 & 信用卡付款詐騙
    • 網路銀行詐騙
    • 匯款詐騙
  • 調查手法
  • 預測摘要
    • CNP (非見面交易) 詐騙的擴大:各垂直部門
    • CNP詐騙的擴大:各地區
    • CNP詐騙比率的變動:以金額為準
    • 網路銀行詐騙
    • 匯款詐騙

第2章 卡片詐騙的理解

  • 卡片詐騙的類型
    • 偽造卡片詐騙
    • 遺失 & 盜竊卡片詐騙
    • 卡ID盜竊
    • 遠隔購買詐騙
    • CNP (非見面交易) 詐騙
    • 同謀商人詐騙
  • 全球的規模的卡片付款 & 詐騙
    • 全世界的EMV卡的發展
    • 詐騙的EMV的影響

第3章 物理商品詐騙:預測 & 要點

  • 簡介
  • 市場規模
  • 物理商品詐騙的預測

第4章 數位商品詐騙:預測 & 要點

  • 簡介
  • 市場規模
  • 數位商品詐騙的預測
    • 詐騙的金額
    • 詐騙的比率

第5章 機票詐騙:預測 & 要點

  • 簡介
  • 市場規模
  • 詐騙的預測

第6章 網路銀行詐騙:預測 & 要點

  • 簡介
    • 語音釣魚
    • 惡意程式
    • 錢騾
  • 市場規模
  • 詐騙的預測

第7章 匯款詐騙:預測 & 要點

  • 簡介
  • 市場規模
  • 詐騙的預測

第8章 詐欺偵測 & 防止的收益

  • 簡介
  • 經營模式
    • SaaS型託管
    • 附授權內部部署型軟體解決方案
    • 混合模式
  • 商機
目錄

Overview

Juniper's market leading Commerce & Fintech research provides the most comprehensive and progressive analysis of the digital commerce market.

Our Online Payment Fraud strategies research provides a global analysis of online FDP (fraud detection & prevention) in the digital commerce sector, with a particular focus on online banking and card not present purchases.

The analysis covers key sectors including:

  • Online Banking
  • Physical Goods
  • Digital Goods
  • Money Transfer
  • Air Ticketing

Key Features

  • Provides an in-depth assessment of established and new fraud detection and authentication tools, with a particular focus on the specific security risks associated with connected devices.
  • Analysis of fraud detection and management for online payment transactions across key verticals:
    • Online Banking
    • Physical Goods
    • Digital Goods
    • Money Transfer
    • Air Ticketing
  • Strategic review of key requirements of real time FDP solutions which are used to analyse and evaluate the solutions offered by 12 leading vendors in the industry.
  • Interviews with leading players across the banking value chain, including:
    • ACI Worldwide
    • AMEX (Accertify)
    • Easy Solutions
    • Experian (41st Parameter)
    • FICO
    • Fiserv
    • Gemalto
    • IBM Trusteer
    • RSA Security
    • ThreatMetrix
    • Visa (CyberSource)
  • Key player capability and capacity assessment, together with our vendor market positioning matrix.
  • Benchmark industry forecasts for the size and growth of fraud detection and management software, alongside scale of growth in fraud transaction value.
  • Key industry executives identified in our ‘Movers & Shakers' section.

Key Questions

  • 1. Who are the key vendors in the online payment fraud detection and prevention market, what do they offer and how are they positioned in the value chain?
  • 2. What are the market opportunities and threats faced by the key vendors?
  • 3. What are the major market trends and what is driving this market?
  • 4. What are the key and most relevant business models offered by vendors to their customers?
  • 5. What is the current scale of online payment fraud transaction value?

Companies Referenced

Interviewed: 41st Parameter (Experian), Accertify (Amex), ACI Worldwide, CyberSource (Visa), Easy Solutions, FICO, Fiserv, Gemalto, IBM Trusteer, RSA Security, ThreatMetrix.

Profiled: 41st Parameter (Experian), Accertify (Amex), ACI Worldwide, CyberSource (Visa), Easy Solutions, FICO, Fiserv, Gemalto, IBM Trusteer, RSA Security, SAS (Statistical Analysis System), ThreatMetrix.

Case Studied: easyJet, MasterCard, OT (Oberthur Technologies).

Mentioned: 1 800-Flowers.com, Above All Software, Aegean, Alaska Airlines, Alibaba, American Express, Apple, Authorize.Net, Autotrader, Azimo, Backcountry.com, Banggood, Bank of America, Bank of New Zealand, Barclays, Bazaarvoice, BioCatch, Britannica.com, CA Accord, CA Arcot, Callidus Software, Capital One, Cardinal Commerce, China Eastern, CIBC, Cinépolis, Computop, Crew Clothing, Cyota, Currency Cloud, Decisioning Solutions, Digital Resolve, Discover, Dunbar, eBay, eBureau, Emailage, EMC, Entersekt, Epicom, eServGlobal, Ethoca, Eurobank, Europay, FIDO (Fast Identity Online) Alliance, Financial Fraud Action UK, First Data, First Direct, Fundtech, Google, Guardian Analytics, Hitachi, HSBC, IATA, IDology, InAuth, ING Direct, Ingenico, Inquiry.com, Internet Security Systems, Iovation, IR, JetBlue Airways, John Lewis, Kount, LexisNexis, Marks & Spencer, Microsoft, Mitratech, Moneris, MoneyGram, National Retail Federation, Nationwide, NatWest, NCR, Netflix, Nice Actimize, NuData Security, Official Payments, Online Resources, Pacific Sunwear, PAY.ON, PayPal, peerTransfer, Prosa, Q2, Quova, ReD (Retail Decisions), Remitly, Ria, Royal Bank of Scotland, Ryanair, Safaricom, SafeNet, Samsung, Santander, SAS, SecureBuy, ShopDirect, Signifyd, Silicium Security, Silver Tail Systems, Societe Generale, Spectrum MoneyGuard, Sphonic, Standard Bank of South Africa, Sugar CRM, Symantec (VeriSign), TAM Airlines, Target, TEPAR, Texaco, Tickets.com, TransferWise, Trend Micro, TUI, Turkish Airlines, UK Cards Association, Ulster Bank, Unisys, Vertical Software, Visa Europe, Visa Inc, Vivid Seats, Volaris, Vormetric, Western Union, WhitepagesPRO, World Bank, WorldRemit, Xactly, Xoom, YubiKey.

Data & Interactive Forecast

The Online Payment Fraud forecast suite includes:

  • Data provided for 8 key geographical regions split by key verticals:
    • Online Banking
    • Physical Goods
    • Digital Goods
    • Money Transfer
    • Air Ticketing
  • What-If Analysis tool allowing user the ability to manipulate Juniper's data for 6 different metrics.
  • Access to the full set of forecast data of more than 30 tables and 2,000 datapoints.

Juniper Research's highly granular interactive excels enable clients to manipulate Juniper's forecast data and charts to test their own assumptions, perform what-if analysis; and compare select markets side by side in customised charts and tables. IFxls greatly increase a clients' ability to both understand a particular market and to integrate their own views into the model.

Table of Contents

1. Fraud Detection & Prevention: Forecast Methodology & Summary

  • 1.1. Introduction
    • 1.1.1. Debit & Credit Card Payment Fraud
    • 1.1.2. Online Banking Fraud
    • 1.1.3. Money Transfer Fraud
  • 1.2. Methodology
    • Figure 1.1: Fraud Detection & Prevention Forecast Methodology
  • 1.3. Forecast Summary
    • 1.3.1. Growth in CNP Fraud by Vertical
      • Figure& Table 1.2: Total Value of CNP Transaction Fraud ($m)Split by Vertical 2015-2020
    • 1.3.2. Growth in CNP Fraud by Region
      • Figure & Table 1.3: Total Value of CNP Transaction Fraud ($m) Split by 8 Key Regions 2015-2020
    • 1.3.3. Variation in CNP Fraud Rate by Value
      • Figure & Table 1.4: Fraud Rate as a Percentage of Transaction Value ($m) North America v Rest of World 2015-2020
    • 1.3.4. 0nline Banking Fraud
      • Figure & Table 1.5: Mobile & 0nline Banking Fraudulent Transaction Value ($m) by 8 Key Regions 2015-2020
    • 1.3.5. Money Transfer Fraud
      • Figure & Table 1.6: Mobile & 0nline Money Transfer Fraudulent Transaction Value ($m) Split by 8 Key Regions 2015-2020

2. Understanding Card Fraud

  • 2.1. Types of Card Fraud
    • 2.1.1. Counterfeit Card Fraud
    • 2.1.2. Lost & Stolen Card Fraud
    • 2.1.3. Card ID Theft
    • 2.1.4. Remote Purchase Fraud
    • 2.1.5. Card Non-Receipt Fraud
    • 2.1.6. Collusive Merchant Fraud
      • Figure 2.1: Card Fraud Losses Split by Type (as Percentage of Total Lossses)
  • 2.2. Card Payments & Fraud on a Global Scale
    • 2.2.1. Global Deployment of EMV Cards
      • Table 2.2: Global Adoption & Usage of EMV Cards (at end of Q4 2014)
    • 2.2.2. Impact of EMV on Fraud
      • Figure 2.3: eCommerce CNP fraud in Canada (CAD m)
      • Figure 2.4: Remote Purchase Fraud Losses on UK-issued Cards (include MO/TO) (£m) 2004-2014

3. Physical Goods Fraud: Forecasts & Key Takeaways

  • 3.1. Introduction
  • 3.2. Market Sizing
    • Figure & Table 3.1: Remote Purchases for Physical Goods Gross Transaction Values ($m) Split by 8 Key Regions 2015-2020
  • 3.3. Physical Goods Fraud Forecasts
    • 3.3.1. Fraud Value
      • Figure & Table 3.2: Physical Goods Sales, Fraudulent Transaction Values ($m) Split by 8 Key Regions 2015-2020
    • 3.3.2. Fraud Rates
      • Figure & Table 3.3: Remote Physical Goods Sales, Fraud Rate as a Percentage of Transaction Value (%) Split by 8 Key Regions 2015-2020
      • Figure & Table 3.4: Remote Physical Goods Sales, Number of Fraudulent Transactions (m) Split by 8 Key Regions 2015-2020
      • Figure & Table 3.5: Remote Physical Goods Sales, Fraud Rate as a Percentage of Transaction Volume (%)Split by 8 Key Regions 2015-2020

4. Digital Goods Fraud: Forecasts & Key Takeaways

  • 4.1. Introduction
  • 4.2. Market Sizing
  • 4.3. Digital Goods Fraud Forecasts
    • 4.2.1. Fraud Value
      • Figure & Table 4.1: Digital Goods Sales, Fraudulent Transaction Value ($m) by 8 Key Regions 2015-2020
    • 4.2.2. Fraud Rates
      • Figure & Table 4.2: Digital Goods Sales, Fraud Rate as a Percentage Transaction Value (%) Split by 8 Key Regions 2015-2020
      • Figure & Table 4.3: Digital Goods Sales, Number of Fraudulent Transactions ($m) Split by 8 Key Regions 2015-2020
      • Figure & Table 4.4: Digital Goods Sales, Fraud Rate as a Percentage of Transaction Volume (%) Split by 8 Key Regions 2015-2020

5. Airline Ticketing Fraud: Forecasts & Key Takeaways

  • 5.1. Introduction
    • Figure 5.1: Airline Fraud by CNP Channel at Visa Europe (2013)
  • 5.2. Market Sizing
  • 5.3. Fraud Forecasts
    • 5.2.1. Fraud Value
      • Figure & Table 5.2: Airline eTicket Sales, Fraudulent Transaction Value ($m) Split by 8 Key Regions 2015-2020
    • 5.2.2. Fraud Rates
      • Figure & Table 5.3: Airline eTicket Sales, Fraud Rate as a Percentage of Transaction Value (%) Split by 8 Key Regions 2015-2020
      • Figure & Table 5.4: Airline eTicket Sales, Number of Fraudulent Transaction Split by 8 Key Regions 2015-2020
      • Figure & Table 5.5: Airline eTicket Sales, Fraud Rate as a Percentage of Transaction Volume (%) Split by 8 Key Regions 2015-2020

6. Online Banking Fraud: Forecasts & Key Takeaways

  • 6.1. Introduction
    • 6.1.1. Vishing
    • 6.1.2. Malware
      • Figure 6.1:Phishing Web Sites Targeting UK Banks & Building Societies 2005-2014
    • 6.1.3. Money Mules
      • Figure 6.2: Online Banking Fraud Losses in the UK (pre-& post- EMV Adoption)
  • 6.2. Market Sizing
  • 6.3. Fraud Forecasts
    • 6.3.1. Fraud Value
      • Figure & Table 6.3: Mobile & 0nline Banking, Fraudulent Transaction Values ($m) Split by 8 Key Regions 2015-2020
    • 6.3.2. Fraud Rates
      • Figure & Table 6.4: Mobile & Online Banking, Fraud Rate as a Percentage of Transaction Value (%) Split by 8 Key Regions 2015-2020
      • Figure & Table 6.5: Mobile & Online Banking, Number of Fraudulent Transaction (m) Split by 8 Key Regions 2015-2020
      • Figure & Table 6.6: Mobile &Online Banking, Fraud Rate as a Percentage of Transaction Volume (%) Split by 8 Key Regions 2015-2020

7. Money Transfer Fraud: Forecasts & Key Takeaways

  • 7.1. Introduction
    • Figure 7.1: Mobile/Online Money Revenues, Selected Players, 2015 ($ millions)
  • 7.2. Market Sizing
  • 7.3. Fraud Forecasts
    • 7.3.1. Fraud Value
      • Figure & Table 7.2: Mobile & Online Money Transfer, Fraudulent Transaction Values ($m) Split by 8 Key Regions 2015-2020
    • 7.3.2. Fraud Rates
      • Figure & Table 7.3: Mobile &Online Money Transfer, Fraud Rate as a Percentage of Transaction Val e (%) Split by 8 Key Regions 2015-2020
      • Figure & Table 7.4: Mobile & Online Money Transfer, Number of Fraudulent Transactions (m) Split by 8 Key Regions 2015-2020
      • Figure 7.5: Mobile & Online Money Transfer, Fraud Rate as a Percentage Transaction Volume (%) Split by 8 Key Regions 2015-2020

8. Fraud Detection & Prevention Revenues

  • 8.1. Introduction
  • 8.2. Business Models
    • 8.2.1. SaaS (Software as a Service)-based Hosting
    • 8.2.2. Licenced 0n-premises Software Solution
    • 8.2.3. Hybrid Model
  • 8.3. Revenue Opportunities
    • Figure & Table 8.1: Annual Revenues from FDP Products & Services ($m) Split by 8 Key Regions 2015-2020
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