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市場調査レポート

オンラインペイメント詐欺

Online Payment Fraud

発行 Juniper Research 商品コード 357197
出版日 ページ情報 英文
納期: 即日から翌営業日
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本日の銀行送金レート: 1GBP=144.22円で換算しております。
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オンラインペイメント詐欺 Online Payment Fraud
出版日: 2018年11月20日 ページ情報: 英文
概要

当レポートでは、世界のオンラインペイメント詐欺市場について取り上げ、詐欺師のアプローチおよびサービスプロバイダーの戦略といった双方の側面から、市場情勢の発展動向について包括的に分析しており、IoT、マシンラーニング、および規制の変化といった主な課題の分析とそれらが産業の発展に及ぼす影響などについて、まとめています。

第1章 オンラインペイメント詐欺:市場概要

  • イントロダクション
  • 詐欺の種類
  • オンラインペイメント情勢の概要
  • 不正活動の発展
  • 主な動向、促進因子、および抑制因子

第2章 オンラインペイメント詐欺のダイナミクス

  • モノのインターネットの影響
    • ケーススタディ - Uncle Sam:Fraud as a Service
    • カーディング
    • カードクラッキング
    • インジケータ―
    • 主な課題 & 機会
  • マルウェア & ID侵害
    • ケーススタディ:Scylex - バンキングマルウェア
    • ケーススタディ:Kaspersky - バンキング詐欺の防止
    • SIMスワップ詐欺
    • 認証
  • 詐欺師の攻撃の進化:要点 & 提言
    • 「スリーパー」攻撃
    • モバイルボット
    • アプリケーション改ざん
    • アカウント乗っ取り & 偽アカウント詐欺
  • FDPサービスプロバイダーの発展
    • 3Dセキュア2.0
    • マシンラーニング
    • レイヤードアプローチ
  • ビジネスモデル
  • 規制の影響
    • 規制 & 規格のコンプライアンス
    • PSD2
  • 詐欺のコスト
    • ROIの計算

第3章 FDPベンダーの分析

  • イントロダクション
  • Juniper Leaderboard
  • Leaderboard のスコア結果
  • オンラインペイメント詐欺の実力者
  • 企業プロファイル
    • Experian
    • Accertity (American Express)
    • ACI Worldwide
    • CyberSource (Visa)
    • FICO
    • iovation
    • Fiserv
    • Gemalto
    • NuData Security (Mastercard)
    • RSA Security
    • SAS
    • ThreatMetrix

詳細データ & 予測

第1章 オンラインペイメント詐欺:市場概要

  • イントロダクション
  • 詐欺の種類
  • オンラインペイメント情勢の概要
  • 不正活動の発展

第2章 オンラインペイメント詐欺市場のサマリー

  • イントロダクション
  • 調査手法 & 前提条件
  • 予測サマリー

第3章 航空eチケット詐欺:市場予測

  • イントロダクション
  • 詐欺取引額
  • オンライン vs. モバイル
  • 詐欺の比率

第4章 リモートデジタル商品購入詐欺:市場予測

第5章 リモート物理商品購入詐欺:市場予測

第6章 送金詐欺:市場予測

第7章 デジタルバンキング詐欺:市場予測

第8章 不正検出 & 防止ソリューション:市場予測

  • イントロダクション
  • ビジネスモデル
    • SaaS型ホスティング
    • 認可オンプレミスソフトウェアソリューション
    • ハイブリッドモデル
  • 市場規模
目次

Overview

Juniper's latest ‘Online Payment Fraud’ research provides a deep-dive analysis of how the changing digital payments landscape, alongside key shifts in fraudster behaviour, is creating new challenges and opportunities for fraud prevention service providers.

The research report covers major payment service dynamics, such as PSD2 (the EU's revised payment services directive), API-driven banking, 3-D Secure 2.0 and Instant Payment schemes alongside a comprehensive analysis of digital payment segment trends and fraud outlook in terms of:

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

This research suite comprises:

  • Deep Dive Strategy & Competition (PDF)
  • 5 Year Deep Dive Data & Forecasting (PDF & Excel)
  • Executive Summary & Core Findings (PDF)
  • 12 months' access to harvest online platform

Key Features

  • Market Dynamics: Detailed analysis of the evolving market state and future outlook, examining:
    • PSD2
    • 3-D Secure 2.0
    • Instant Payments
    • Key Fraud Trends
    • Strategies & Recommendations
  • Segment Analysis: In-depth examination of sector-specific challenges, strategies and future outlook in the context of fraud for:
    • Banking and money transfer
    • Remote goods purchases
    • Airline ticketing
  • ROI Cost Analysis: New, in-depth cost model to uncover fraud prevention service ROI (Return on Investment):
    • Scenario analysis of chargeback, manual review, order declines and fraud prevention spend as a proportion of revenue
    • Total costs to businesses through fraud prevention, cost of fraud, and revenue losses
    • ROI calculation for merchants
  • Interviews with leading players, including:
    • Accertify
    • ACI Worldwide
    • Adyen
    • CyberSource
    • Experian
    • 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 and 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 2023?

Companies Referenced

  • Interviewed: Accertify, ACI Worldwide, CyberSource, Experian, NuData, RSA Security.
  • Profiled: Accertify, ACI Worldwide, CyberSource, Experian, FICO, Fiserv, iovation, NICE Actemize, NuData, RSA Security, SAS, ThreatMetrix.
  • Mentioned: 41st Parameter, ACCC (Australian Competition and Consumer Commission), Accenture, Accuity, Acuant, Adidas, ADP, Adyen, Aegean, Air Canada, Allianz, Amazon, American Express, Arvato Financial Solutions, Auchan, Authorize.Net, Backcountry.com, Bain, Barclays, Bazaarvoice, BBC, BioCatch, Booz & Company, Boston Consulting Group, British Airways, Capital One, Cardinal Commerce, Carte Bancaire, Cathay Pacific, CBGroup, China Eastern Airlines, Cinépolis, Cisco, Citrus Pay, Clifford Chance, Cognizant, ConvergeOne, CyberSource, Cyota, Danal, Daon, Dealflo, Deloitte, Delta, Discover, Early Warning, EasyJet, EBA (European Banking Authority), eBay, eBureau, EC (European Commission), Elastica, Emailage, EMVCo, Entrust Datacard, Equifax, Ethoca, European Payments Council, Evo Payments, Experian, Falabella, FedEx, First Data, Forbes, Friss, FTC (Federal Trade Commission), Fuze, Groupon, HSBC, IATA, IBM, IDology, InAuth, Infosys, IPC, Jack Henry & Associates, Jason's Deli, JCB, JP Morgan, Kareo, Klarna, KPMG, Landsbankinn, Laurentian Bank, LaunchKey, LexisNexis Risk Solutions, Mastercard, Maxmind, Mitek, Motorola, NAORCA (National Anti-Organized Retail Crime Association), NBC, Neustar, Newegg, Nintendo, NPCI (National Payments Corporation of India), NSPK (National System of Payment Cards), OBIE (Open Banking Implementation Entity), Orbitz, OTP Bank, Panera Bread, PASA (Payment Association of South Africa), PAY.ON, PayPal, Paytm, PlaySpan, Playtech, Positive Technologies, PWC, Quest, Rackspace, Rail Europe, RELX Group, RingCentral, RSA, Sagepay, Salesforce.com, Scudetto, SecureBuy, Servion, Shape's, ShopDirect, Silicium Security, ShopDirect, Silver Tail Systems, Sony, Southwest Airlines, Sphonic, Syectics Solutions, Symantec, TAM Airlines, TaskRabbit, Tata Consulting Services, Tencent, THQ, Ticketfly, Ticketmaster, TransUnion, Trulioo, trunarrative, TrustDefender, TSYS, UBS, UnionPay, Under Armour, Urban Outfitters and Greyhound, US Data and Marketing Association, US Department of Justice, Venmo, VeriFone, Verizon, Visa, Vocalink, WeChat, Wells Fargo, Wendy's, Western Union, Which?, Whitepages Pro.

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. Physical & Digital Goods
      • Figure 1.1: Remote Physical & Digital Goods Purchases per Annum (m) 2018-2023
    • 1.3.2. eCommerce Value
      • Figure 1.2: Total eCommerce Market Value ($m), Split by Segment 2018-2023
    • 1.3.3. The Rise of Alternative Payments
      • Figure 1.3: Global Physical Goods eRetail Transactions 2017, By Payment Mechanism (57.8 billion)
      • Figure 1.4: iDEAL Transactions Per Annum (m), 2012-2017
  • 1.4. Development of Fraudulent Activity
    • Figure 1.5: eCommerce & Fraud Attempt Growth (%), November-December 2014-2017
    • Figure 1.6: Merchant EMV Card Acceptance, US, 2015-2018
  • 1.5. Key Trends in Digital Fraud
    • 1.5.1. Darknet Activity
      • i. From Darknet to Clearnet
        • Table 1.7: Average Dark Market List Price ($), Various Tools, Products & Guides used by Fraudsters 2018
    • 1.5.2. Identity Theft
      • i. Data Breaches
        • Figure 1.8: Total Number of Data Records per Annum Exposed through Cybercrime (m), Split by 8 Key Regions 2018-2023
        • Table 1.9: Selected Major Data Breaches Reported January-June 2018
        • Table 1.10: FTC Reported Identity Theft Cases 2017 vs 2016
      • ii. Cybercriminal Targeting Shifts
      • iii. Key Takeaways

2. Online Payment Fraud Market Dynamics

  • 2.1. Introduction
  • 2.2. PSD2(Revised Payment Services Directive)
    • 2.2.1. RTS Status
    • 2.2.2. RTS Implications for Payment Service Providers
      • i. Fraud Detection
      • ii. Exemptions from SCA
        • Table 2.1: CNP Fraud Rate Thresholds for SCA Exemption
      • iii. API Authentication Security
      • iv. Avoiding Logic Abuse
  • 2.3. Real-time Payments
    • Table 2.2: Global Instant Payments Market Status
    • Table 2.3: Global Instant Payments Market Status (Continued)
    • 2.3.1. Real-time Payments Impact on Fraud
      • i. Problems Inherent in Infrastructure
      • ii. Further Protections Required
  • 2.4. 3DS 2.0 (3-D Secure 2.0)
    • 2.4.1. Authentication Mechanisms
      • i. OTP (One-Time Passwords)
      • ii. KBA (Knowledge-Based Authentication)
      • iii. Biometric
    • 2.4.2. Further 3DS Implications
    • 2.4.3. Next Steps & Regional Outlook
      • i. North America
      • ii. Latin America
      • iii. West Europe
      • iv. Central & East Europe
      • v. Far East & China
      • vi. Indian Subcontinent
      • vii. Rest of Asia Pacific
      • viii. Africa & Middle East

3. Online Payment Fraud - Segment Analysis

  • 3.1. Banking & Money Transfer
    • Figure 3.1: Major Losses from Banking Malware
    • 3.1.1. Key Challenge: Advanced Persistent Threats
      • Table 3.2: Carbanak Attack Execution Phases
    • 3.1.2. Key Challenge: Digital Transformation
    • 3.1.3. Key Trends & Outlook in the Financial Sector
  • 3.2. Remote Goods Purchases
    • 3.2.1. Key Challenge: Synthetic Identity
      • i. Detection
    • 3.2.2. Key Challenge: Malicious JavaScript
    • 3.2.3. Key Trends & Future Outlook in eRetail
      • i. Modelling the Impact of Manual Reviews & False Positives
        • Table 3.3: Fraud Mitigation ROI Analysis
        • Table 3.4: Fraud Mitigation ROI Analysis (Continued)
        • Figure 3.5: Cost & Lost Revenue ($m), Split by FDP Spend as a Proportion (%) of Revenue
      • ii. Machine Learning
  • 3.3. Airlines
    • Figure 3.4: Impact of Crude Oil Price ($) on Aviation Operating Costs (%) 2004-2018
    • 3.3.1. Key Challenge: Security
    • 3.3.2. Key Challenge: Chargebacks
    • 3.3.3. Key Trends & Future Outlook in the Airline Sector

4. Online Fraud Detection Service Provider Analysis

  • 4.1. Introduction
  • 4.2. Juniper Leaderboard
    • Table 4.1: FDP Vendor Capability Assessment Criteria
  • 4.3. Leaderboard Scoring Results
    • Table 4.2: Juniper Leaderboard: FDP Vendors
    • Figure 4.3: Juniper Leaderboard: FDP Vendors
    • 4.3.1. Stakeholder Groupings
      • i. Established Leaders
      • ii. Leading Challengers
      • iii. Disruptors & Emulators
    • 4.3.2. Limitations & Interpretations
  • 4.4. Online Payment Fraud Movers & Shakers
  • 4.5. Company Profiles
    • 4.5.1. Accertify
      • i. Corporate Profile
        • Table 4.4: American Express Financial Snapshot 2015-2017
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: Accertify Key Strengths & Strategic Development
    • 4.5.2. ACI Worldwide
      • i. Corporate Profile
        • Table 4.5: ACI Worldwide Financial Snapshot ($m) 2015-2017
      • ii. Geographic Spread
      • iii. Key Clients & Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: ACI Key Strengths & Strategic Development Opportunities
    • 4.5.3. CyberSource, a Visa Solution
      • i. Corporate Profile
        • Table 4.6: Visa Financial Snapshot ($m) 2015-2017
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: CyberSource Key Strengths & Strategic Development Opportunities
    • 4.5.4. Experian
      • i. Corporate Profile
        • Table 4.7: Experian Financial Snapshot ($m) FY 2016-2018
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: Experian Key Strengths & Strategic Development Opportunities
    • 4.5.5. FICO
      • i. Corporate
        • Table 4.8: FICO Financial Snapshot ($m) 2015-2017
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: FICO Key Strengths & Strategic Development Opportunities
    • 4.5.6. Fiserv
      • i. Corporate Profile
        • Table 4.9: Fiserv Financial Snapshot ($m) 2015-2017
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: Fiserv Key Strengths & Strategic Development Opportunities
    • 4.5.7. iovation
      • i. Corporate Profile
        • Table 4.10: TransUnion Financial Snapshot ($m) 2015-2017
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: iovation Key Strengths & Strategic Development Opportunities
    • 4.5.8. NICE Actemize
      • i. Corporate Profile
        • Table 4.11: NICE Financial Snapshot ($m) 2015-2017
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: NICE Actemize Key Strengths & Strategic Development Opportunities
    • 4.5.9. NuData Security (Mastercard)
      • i. Corporate Profile
        • Table 4.12: Mastercard Financial Snapshot ($m) 2015-2017
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: NuData Key Strengths & Strategic Development Opportunities
    • 4.5.10. 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
    • 4.5.11. SAS
      • i. Corporate
        • Table 4.13: SAS Financial Snapshot ($m) 2015-2017
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High-level View of Products
      • v. Juniper's View: SAS Key Strengths & Strategic Development Opportunities
    • 4.5.12. ThreatMetrix
      • i. Corporate Profile
        • Table 4.14: RELX Group Financial Snapshot (£m/$m) 2015-2017
      • 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. Online Payment Fraud: Market Overview

  • 1.1. Introduction
  • 1.2. Types of Fraud
  • 1.3. Online Payments Landscape Overview
    • 1.3.1. Physical & Digital Goods
      • Figure 1.1: Remote Physical & Digital Goods Purchases per Annum (m) 2018-2023
    • 1.3.2. eCommerce Value
      • Figure 1.2: Total eCommerce Market Value ($m), Split by Segment 2018-2023
    • 1.3.3. The Rise of Alternative Payments
      • Figure 1.11: Global Physical Goods eRetail Transactions 2017, By Payment Mechanism (57.8 billion)
      • Figure 1.12: iDEAL Transactions Per Annum (m), 2012-2017
  • 1.4. Development of Fraudulent Activity
    • Figure 1.3: eCommerce & Fraud Attempt Growth (%), November-December 2014-2017
    • Figure 1.4: Merchant EMV Card Acceptance, US, 2015-2018

2. Online Payment Fraud Market Summary

  • 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 ($m): Remote Goods Purchases, Airline Tickets, Split by 8 Key Regions 2018-2023
    • 2.3.2. Total Value of Fraudulent Transactions
      • Figure & Table 2.3: Total Value of Fraudulent Transactions ($m), Split by eCommerce Segment 2018-2023

3. Airline eTicketing Fraud: Market Forecasts

  • 3.1. Introduction
  • 3.2. Fraud Transaction Value

4. Remote Digital Goods Purchases Fraud: Market Forecasts

  • Figure & Table 3.1: Total Airline eTicket Fraudulent Value ($m), Split by 8 Key Regions 2018-2023
  • 3.3. Online vs Mobile
    • Figure & Table 3.2: Total Airline eTicket Fraudulent Value ($m), Split by Sales Channel 2018-2023
  • 3.4. Fraud Rates
    • Figure & Table 3.3: Total Airline Ticket Fraud Rate by Value (%), Split by 8 Key Regions 2018-2023
  • 4.1. Introduction
  • 4.2. Fraud Transaction Value
    • Figure & Table 4.1: Total Remote Digital Goods Fraudulent Value ($m), Split by 8 Key Regions 2018-2023
  • 4.3. Online vs Mobile
    • Figure & Table 4.2: Total Remote Digital Goods Fraudulent Value ($m), Split by Sales Channel 2018-2023
  • 4.4. Fraud Rates

5. Remote Physical Goods Purchases Fraud: Market Forecasts

  • Figure & Table 4.3: Total Remote Digital Goods Fraud Rate by Value (%), Split by 8 Key Regions 2018-2023
  • 5.1. Introduction
  • 5.2. Fraud Transaction Value
    • Figure & Table 5.1: Total Remote Physical Goods Fraudulent Value ($m), Split by 8 Key Regions 2018-2023
  • 5.3. Online vs Mobile
    • Figure & Table 5.2: Total Remote Physical Goods Fraudulent Value ($m), Split by Sales Channel 2018-2023
  • 5.4. Fraud Rates
    • Figure & Table 5.3: Total Remote Physical Goods Fraud Rate by Value (%), Split by 8 Key Regions 2018-2023

6. Money Transfer Fraud: Market Forecasts

  • 6.1. Introduction
  • 6.2. Fraud Transaction Value
    • Figure & Table 6.1: Total Money Transfer Fraudulent Value ($m), Split by 8 Key Regions 2018-2023
  • 6.3. Online vs Mobile
    • Figure & Table 6.2: Total Money Transfer Fraudulent Value ($m), Split by Sales Channel 2018-2023
  • 6.4. Fraud Rates
    • Figure & Table 6.3: Total Money Transfer Fraud Rate by Value (%), Split by 8 Key Regions 2018-2023

7. Digital Banking Fraud: Market Forecasts

  • 7.1. Introduction
  • 7.2. Fraud Transaction Value

8. Fraud Detection & Prevention Solutions: Market Forecasts

  • Figure & Table 7.1: Total Digital Banking Fraudulent Value ($m), Split by 8 Key Regions 2018-2023
  • 7.3. Online vs Mobile
    • Figure & Table 7.2: Total Digital Banking Fraudulent Value ($m), Split by Sales Channel 2018-2023
  • 7.4. Fraud Rates
    • Figure & Table 7.3: Total Digital Banking Fraud Rate by Value (%), Split by 8 Key Regions 2018-2023
  • 8.1. Introduction
  • 8.2. Business Models
    • 8.2.1. SaaS (Software as a Service)-based Hosting
    • 8.2.2. Licenced On-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 2018-2023
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