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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
價格
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線上付款詐騙 Online Payment Fraud
出版日期: 2017年06月20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

本網頁內容可能與最新版本有所差異。詳細情況請與我們聯繫。

目錄

Overview

Juniper's latest Online Payment Fraud research provides a comprehensive analysis of how the landscape is developing, both in terms of fraudster approaches as well as service provider strategies. It examines key issues, such as the IoT (Internet of Things), machine learning and regulatory changes and how these will impact the industry moving forward.

The analysis covers key industry sectors including:

  • Digital Banking
  • Remote Physical Goods Purchases
  • Remote Digital Goods Purchases
  • Digital Money Transfer
  • Air Ticketing
  • Fraud Detection & Prevention Solutions

This research suite comprises:

  • Market Trends & Competitive Landscape (PDF)
  • 5 Year Market Sizing & Forecasts (PDF & Excel)
  • Executive Summary & Core Findings (PDF)

Key Features

  • Sector Dynamics: Detailed analysis of the evolving market state and future outlook, examining:
    • Present & future impact of IoT
    • Evolution of malware & fraudster approaches
    • Evolution of fraud detection & prevention services and strategies
    • Regulatory landscape & future impact
    • Key market drivers, trends & constraints
  • Cost Analysis: In-depth examination of the cost of fraud:
    • Scenario analysis of chargeback, manual review, order declines and total fraud costs
    • Return on investment calculation for merchants
  • Interviews with leading players, including:
    • Accertify
    • CyberSource
    • Experian
    • InAuth
    • iovation
    • Fidor
    • Gemalto
    • Kaspersky
    • NuData Security
    • RSA Security
  • Juniper Leaderboard: Key player capability and capacity assessment for 12 major Fraud Detection & Prevention vendors.
  • Benchmark Industry Forecasts: Market segment forecasts key eCommerce segments, including:
    • Digital Banking
    • Remote Physical Goods Purchases
    • Remote Digital Goods Purchases
    • Digital Money Transfer
    • Air Ticketing
    • Fraud Detection & Prevention Solutions

Key Questions

  • 1. What is the anticipated size of transaction value lost to fraud?
  • 2. What is the market size for FDP solutions?
  • 3. What are the key market forces influencing fraudster & fraud prevention strategies?
  • 4. Who are the key disruptors in this space, and what strategies are vendors employing?
  • 5. How is the industry expected to develop towards 2022?

Companies Referenced

  • Interviewed: Experian, Accertify (American Express), CyberSource (Visa), Gemalto, InAuth, iovation, Kaspersky Labs, NuData Security (Mastercard), RSA Security,
  • Profiled: Experian, Accertify (American Express), ACI Worldwide, CyberSource (Visa), FICO, Fiserv, Gemalto, iovation, NuData Security (Mastercard), RSA Security, SAS, ThreatMetrix.
  • Case Studied: InAuth, Kaspersky Labs, Sedicii.
  • Mentioned: 41st Parameter, Adobe, Amazon, Apple, Arvato Financial Solutions, Authorize.Net, Azimo, Backcountry.com, Banggood, Bazaarvoice, British Airways, Callcredit, Callidus Software, Capital One, Cardinal Commerce, CARO (Computer AntiVirus Researchers' Organization), Carphone Warehouse, CBGroup, Cifas, Cinépolis, Currency Cloud, Cyota, DataVisor, Decisioning Solutions, Dell, Discover, Early Warning, Easy Solutions, EasyJet, EBA (European Banking Authority), eBay, eBureau, Elastica, Emailage, EMC, EMVCo, Equifax, eServGlobal, Ethoca, Eurobank, Europol, Fidor, First Data, Friss, GeCad, Greyhound, Groupon, HP, HSBC, IATA (International Air Transport Association), IBM, ID Analytics, IDology, Imperva, Ingenico, Inquiry.com, JetBlue, LaunchKey, LexisNexis, Maxmind, Microsoft, Mitratech, MoneyGram, NAORCA (National Anti-Organized Retail Crime Association), NCR, Network Associates, Neustar, Nintendo, NIST (National Institute for Standards and Technology), Official Payments, Online Resources, Oracle, Pacific Sunwear, PAY.ON, PayPal, PCI (Payment Card Industry), peerTransfer, PlaySpan, Quest, Quova, ReD (Retail Decisions), Red Hat, Remitly, Ria, SafeNet, Secure Networks, SecureBuy, Security Focus, ShopDirect, Silicium Security, Silver Tail Systems, SITA (Société Internationale de Télécommunications Aéronautiques), Sony, Southwest Airlines, Sphonic, StubHub, Syectics Solutions, Symantec, TAM Airlines, THQ, T-Mobile, Todos AB, TransferWise, Trusteer, TSYS, Turkish Airlines, UK Cards Association, Urban Outfitters, VeriskVocalink, Vormetric, Western Union, WhitepagesPRO, Wildlist Organization International, World Bank, WorldRemit, Xactly, Xoom, Zoot.

Data & Interactive Forecast

Juniper's Online Payment Fraud forecast suite includes:

  • Forecasts for 8 Key Regions as well as 11 countries including:
    • Canada
    • China
    • Denmark
    • Germany
    • Japan
    • Norway
    • Portugal
    • Spain
    • Sweden
    • UK
    • USA
  • Fraud Transaction Value, Split by Device:
    • Mobile
    • Online
  • Fraud Transaction Value, Split by eCommerce Segment:
    • Digital Banking
    • Airline Ticketing
    • Remote Digital Goods Purchases
    • Remote Physical Goods Purchases
    • Digital Money Transfers
  • Fraud Detection & Prevention Service Revenue
  • Interactive Scenario Tool allowing user the ability to manipulate Juniper's data for 10 different metrics.
  • Access to the full set of forecast data of 69 tables and over 9,000 datapoints.

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

Table of Contents

Deep Dive Strategy & Competition

1. Online Payment Fraud: Market Overview

  • 1.1. Introduction
  • 1.2. Types of Fraud
  • 1.3. Online Payments Landscape Overview
    • 1.3.1. Goods and Digital Services Purchases
      • Figure 1.1:Annual Remote Physical & Digital Goods Transactions (m) 2016-2021
    • 1.3.2. eCommerce Value
      • Figure 1.2: Total eCommerce Market Value ($m), Split by Segment 2016-2021
  • 1.4. Development of Fraudulent Activity
    • Figure 1.3: eCommerce and Fraud Attempt Growth (%) 2015-2016 (November-December)
    • 1.4.1. Fraud Chargebacks
      • Figure 1.4: Country Level Fraud Chargeback Rates (%) 2015 & 2016- Large Corporations
    • 1.4.2. Vertical Analysis
      • Figure 1.5: Top Merchants Affected by Fraud Transactions
  • 1.5. Key Trends, Drivers & Constraints
    • Figure 1.6: Online Payment Fraud Key Trends, Drivers & Constraints
    • 1.5.1. Drivers
      • i. Fraudsters
      • ii. Consumers
      • iii. Service Provider
    • 1.5.2. Trends
      • i. Fraudsters
      • ii. Case Study: Carbanak - Spear Phishing Evolved
      • iii. Consumers
      • iv. Service Providers
    • 1.5.3. Constraints
      • i. Fraudsters
      • ii. Consumer
      • iii. Service Provider

2. Online Payment Fraud Dynamics

  • 2.1. The Impact of the Internet of Things
    • Figure 2.1:ConsumerIoT Units (m), Split by 8 Key Regions 2016-2021
    • 2.1.1. Case Study - Uncle Sam: Fraud as a Service
      • Figure 2.2:'Fraud as a Service' - Uncle Sam Carding Merchant
    • 2.1.2. Carding
    • 2.1.3. Card Cracking
    • 2.1.4. Indicators
      • Figure 2.3: Low Cost Anti-carding & Card Cracking Techniques
    • 2.1.5. Key Challenges & Opportunities
  • 2.2. Malware & Identity Compromise
    • 2.2.1. Case Study: Scylex - Banking Malware
    • 2.2.2. Case Study: Kaspersky - Banking Fraud Preventional
    • 2.2.3. SIM Swap Fraud
      • i. Challenges & Opportunities
      • ii. Case Study: Sedicii - Preventing SIM Swap Fraud
    • 2.2.4. Authentication
      • i. Case Study: SS7 Exploited
  • 2.3. Evolution of Fraudster Attacks - Key Takeaways & Recommendations
    • 2.3.1. ‘Sleeper' Attacks
    • 2.3.2. Mobile Bots
      • i. Case Study: InAuth
    • 2.3.3. App Tampering
    • 2.3.4. Account Takeover & Synthetic Account Fraud
  • 2.4. FDP Service Provider Evolution
    • 2.4.1. 3D Secure 2.0
      • Figure 2.4: 3DSI.x vs 2.0
      • i. PSD2
    • 2.4.2. Machine Learning
      • i. Drivers
      • ii. Components
    • 2.4.3. Layered Approaches
  • 2.5. Business Models
  • 2.6. Regulatory Impacts
    • 2.6.1. Compliance with Regulations & Standards
    • 2.6.2. PSD2
      • i. Open Banking APIs
  • 2.7. Cost of Fraud
    • 2.7.1. Calculating ROI (Return on Investment)
      • Table 2.5: Limited FDP Deployment - Daily Cost Analysis per Gross Margin
      • Table 2.6: Full FDP Deployment - Daily Cost Analysis per Gross Margin %
      • i. Conclusion
        • Figure 2.7: Annual ROI ($) Analysis of FDP by Gross Margin %

3. FDP Vendor Analysis

  • 3.1. Introduction
  • 3.2. Juniper Leaderboard
    • Table 3.1: FDP Vendor Capability Assessment Criteria
  • 3.3. Leaderboard Scoring Results
    • Table 3.2: Juniper Leaderboard: FDP Vendors
    • Figure 3.3: Juniper Leaderboard: FDP Vendors
    • 3.3.1. Stakeholder Groupings
      • i. Established Leaders
      • ii. Leading Challengers
      • iii. Disruptors & Emulators
    • 3.3.2. Limitations & Interpretations
  • 3.4. 0nline Payment Fraud Movers & Shakers
  • 3.5. Company Profiles
    • 3.5.1. Experian
      • i. Corporate Profile
        • Table 3.4: Experian Financial Snapshot ($m) 2015-20
        • ii. Geographic Spread
        • iii. Key Clients & Strategic Partnerships
        • iv. High-level View of Products
        • v. Juniper's View: Experian Key Strengths & Strategic Development Opportunities
    • 3.5.2. Accertity (American Express)
      • i. Corporate Profile
        • Table 3.5: American Express Financial Snapshot ($m) 2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: Accertify Key Strengths & Strategic Development Opportunities
    • 3.5.3. ACI Worldwide
      • i. Corporate
        • Table 3.6: ACI Worldwide Financial Snapshot ($m) 2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: ACI Key Strengths & Strategic Development Opportunities
    • 3.5.4. CyberSource (Visa)
      • i. Corporate Profile
        • Table 3.7: Visa Financial Snapshot ($m) 2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: CyberSource Key Strengths & Strategic Development Opportunities
    • 3.5.5. FICO
      • i. Corporate:
        • Table 3.8: FICO Financial Snapshot ($m) 2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: FICO Key Strengths & Strategic Development Opportunities
    • 3.5.6. iovation
      • i. Corporate Profile
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: iovation Key Strengths & Strategic Development Opportunities
    • 3.5.7. Fiserv
      • i. Corporate Profile
        • Table 3.9: Fiserv Financial Snapshot ($m) 2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: Fiserv Key Strengths & Strategic Development Opportunities
    • 3.5.8. Gemalto
      • i. Corporate:
        • Table 3.10: Gemalto Financial Snapshot (Em) 2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: Gemalto Key Strengths & Strategic Development Opportunities
    • 3.5.9. NuData Security (Mastercard)
      • i. Corporate Profile
        • Table 3.11: Mastercard Financial Snapshot ($m) 2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: NuData Key Strengths & Opportunities
    • 3.5.10. RSA Security
      • i. Corporate Profile
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: RSA Key Strengths & Strategic Development Opportunities
    • 3.5.11. SAS
      • i. Corporate
        • Table 3.12: SAS Financial Snapshot ($m) 2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: SAS Key Strengths & Strategic Development Opportunities
    • 3.5.12. ThreatMetrix
      • i. Corporate Profile
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: ThreatMetrix Key Strengths & Strategic Development Opportunities

Deep Dive Data & Forecasting

1.0nline Payment Fraud: Market Overview

  • 1.1. Introduction
  • 1.2. Types of Fraud
  • 1.3. Online Payments Landscape Overview
    • 1.3.1. Goods and Digital Services Purchases
      • Figure 1.1:Annual Remote Physical & Digital Goods Transactions (m) 2016-2021
    • 1.3.2. eCommerce Value
      • Figure 1.2: Total eCommerce Market Value ($m), Split by Segment 2016-2021
  • 1.4. Development of Fraudulent Activity
    • Figure 1.3: eCommerce and Fraud Attempt Growth (%) 2015-2016 (November-December)
    • 1.4..1 Fraud Chargebacks
      • Figure 1.4: Country Level Fraud Chargeback Rates (%) 2015 & 2016 - Large Corporations
    • 1.4.2. Vertical Analysis
      • Figure 1.5: Top Merchants Affected by Fraud Transactions

2. OnlinePaymentFraudMarketSummary

  • 2.1. Introduction
  • 2.2. Methodology & Assumptions
    • Figure 2.1: FDP Forecast Methodology
  • 2.3. Forecast Summary
    • 2.3.1. CNP Fraud
      • Figure & Table 2.2: Total CNP Fraud Value ($m): Remote Goods Purchases, Airline Tickets, Split by 8 Key Regions 2017-2022
    • 2.3.2. Total Value of Fraudulent Transactions
      • Figure & Table 2.3: Total Value of Fraudulent Transactions ($m), Split by eCommerce Segment 2017-2022

3. Airline eTicketing Fraud: Market Forecasts

  • 3.1. Introduction
  • 3.2. Fraud Transaction Value
    • Figure & Table 3.1: Total Airline eTicket Fraudulent Value ($m), Split by 8 Key Regions 2017-2022
  • 3.3. Online vs Mobile
    • Figure & Table 3.2: Total Airline eTicket Fraudulent Value ($m), Split by Sales Channel 2017-2022
  • 3.4. Fraud Rates
    • Figure & Table 3.3: Total Airline Ticket Fraud Rate by Value (%), Split by 8 Key Regions 2017-2022

4. Remote Digital Goods Purchases Fraud: Market Forecasts

  • 4.1. Introduction
  • 4.2. Fraud Transaction Value
    • Figure & Table 4.1: Total Remote Digital Goods Fraudulent Value ($m), Split by 8 Key Regions 2017-2022
  • 4.3. 0nline vs Mobile
    • Figure & Table 4.2: Total Remote Digital Goods Fraudulent Value ($m), Split by Sales Channel 2017-2022
  • 4.4. Fraud Rates
    • Figure & Table 4.3: Total Remote Digital Goods Fraud Rate by Value (%), Split by 8 Key Regions 2017-2022

5. Remote Physical Goods Purchases Fraud: Market Forecasts

  • 5.1. Introduction
  • 5.2. Fraud Transaction Value
    • Figure & Table 5.1: Total Remote Physical Goods Fraudulent Value ($m), Split by 8 Key Regions 2017-2022
  • 5.3. 0nline vs Mobile
    • Figure & Table 5.2: Total Remote Physical Goods Fraudulent Value ($m), Split by Sales Channel 2017-2022
  • 5.4. Fraud Rates
    • Figure & Table 5.3: Total Remote Physical Goods Fraud Rate by Value by 8 Key Regions 2017-2022

6. Money Transfer Fraud: Market Forecasts

  • 6.1. Introduction
  • 6.2. Fraud Transaction Value
    • Figure & Table 6.1: Total Money Transfer Fraudulent Value ($m), Split by 8 Key Regions 2017-2022
  • 6.3. 0nline vs Mobile
    • Figure & Table 6.2: Total Money Transfer Fraudulent Value ($m), Split by Channel 2017-2022
  • 6.4. Fraud Rates
    • Figure & Table 6.3: Total Money Transfer Fraud Rate by Value (%), Split by 8 Key Regions 2017-2022

7. Digital Banking Fraud: Market Forecasts

  • 7.1. Introduction
  • 7.2. Fraud Transaction Value
    • Figure & Table 7.1: Total Digital Banking Fraudulent Value ($m), Split by 8 Key Regions 2017-2022
  • 7.3. 0nline vs Mobile
    • Figure & Table 7.2: Total Digital Banking Fraudulent Value ($m), Split by Channel 2017-2022
  • 7.4. Fraud Rates
    • Figure & Table 7.3: Total Digital Banking Fraud Rate by Value (%), Split by 8 Key Regions 2017-2022

8. Fraud Detection & Prevention Solutions: Market Forecasts

  • 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. Market Size
    • Figure & Table 8.1: Total Annual FDP Spend ($m), Split by 8 Key Regions 2017-2022
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