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市場調查報告書

電子商務詐騙偵測解決方案:市場概要

E-Commerce Fraud Detection Solutions: Market Overview

出版商 Mercator Advisory Group, Inc. 商品編碼 926563
出版日期 內容資訊 英文 19 Pages
商品交期: 最快1-2個工作天內
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電子商務詐騙偵測解決方案:市場概要 E-Commerce Fraud Detection Solutions: Market Overview
出版日期: 2020年02月25日內容資訊: 英文 19 Pages
簡介

機器學習工具大幅改變了偵測詐騙的方法。機器學習技術快速發展,而詐騙偵測平台模型亦大幅演進。這些模型現在可以監控和學習運用同一平台的多個網站活動、或是從支付網路直接獲得的數據。

本報告研究電子商務詐騙偵測解決方案,針對55個詐騙平台供應商的解決方案分類進行評估。

研究報告重點

  • 辨別整體支付價值鏈的詐騙總成本、或是特定角色的成本分配是相當困難的。造成這些困難的原因包含多種詐騙媒介的複雜性、以及缺乏一致的方法計算詐騙損失以分配商家、支付網路、發卡者之間的責任歸屬。
  • 若考慮爭議交易和退款、手續費、商品補貨、人工和調查、法律起訴、IT/軟體安全相關費用,詐騙造成之每1美元的損失成本,從2016的2.40美元於2019年增加為3.13美元。
  • 網路化的機器學習模式,尤其是那些透過蒐集來自多個商家、支付網路、發卡者情報訓練而來的模型,正改變著詐騙偵測市場動態。
  • 線上訂購商店取貨已成為重大的新詐騙媒介,其需要新的診測和組織模式。
目錄

Market overview of technology solutions to identify fraud across the entire e-commerce process.

Mercator Advisory Group releases new research that categorizes 55 fraud platform suppliers from initial online contact through purchase and dispute management.

Machine learning tools have significantly changed the way fraud is detected. Even as machine learning technology advances at a dizzying rate, so do the models that fraud detection platforms deploy to recognize fraud. These models can now monitor and learn from activity across multiple sites operating the same platform or even from data received directly from the payment networks. This ability to model and detect fraud activity across multiple merchants, multiple geographies, and from the payment networks enables improved detection and inoculation from new types of fraud attack as soon as they are discovered. What is more important is that this technology starts to connect identity, authentication, behavior, and payments in ways never possible before.

Mercator Advisory Group's latest research report, ‘E-Commerce Fraud Detection Solutions: Market Overview’, provides a foundational framework for evaluating fraud detection technologies in two categories. The first category includes 18 suppliers that have been identified by Mercator as implementing more traditional systems that monitor e-commerce websites and payments, evaluating shopping, purchasing, shipping, payments, and disputes to detect fraud. The second category includes 37 service providers that Mercator has identified as specializing in identity and authentication often utilizing biometrics as well as behavioral biometric data collected across multiple websites to establish risk scores and to detect account takeover attempts and bots. Note, however, that companies in both of these categories are adopting new technologies and their solutions are undergoing rapid change.

“E-commerce fraud rates continue to increase at a rapid rate, with synthetic fraud growing faster than other fraud types. It is time for merchants to reevaluate the tools they currently deploy to prevent fraud,” commented Steve Murphy, Director, Commercial and Enterprise Payments Advisory Service, co-author of the report.

This report is 19 pages long and has 7 exhibits.

Companies and other organizations mentioned in this report include: Accertify (Amex), ACI ReD Shield, Authenteq, BAE Systems, BioCatch, Bolt, Bottomline Technologies, Brighterion (Mastercard), CA Risk Analytics Network, Cybersource (Visa), Cyxtera (Easy Solutions), Datavisor, Demisto, Distilled Identity, Ethoca (Mastercard), Experian, Featurespace, Feedzai, FICO, Forter, FraudLabs, Gemalto, Guardian Analytics, ID Analytics, Idology, Illumio, InAuth (Amex), Jumio, Kount, LexisNexis, Mitek, NeuStar, Nice Actimize, NoFraud, Nuance, NuData (Mastercard), OnFido, PayFone, PayPal Order Filters, Plus Technologies & Innovations, Radial, Ravelin, Riskified, RSA, SAS, Shape Security (F5), Sift (Sift Science), Signifyd, Simility (PayPal), Socure, Stripe Radar, ThreatMetrix (LexisNexis Risk Solutions), Trulioo, and Verifi (Visa).

One of the exhibits included in this report:

Highlights of the report include:

  • Identifying the total cost of fraud or assigning costs to a specific role in the overall payments value chain is nearly impossible. The difficulty is a result of the complexity of multiple fraud vectors combined with the lack of a consistent methodology for counting fraud loss and assigning liability among merchants, payment networks, and card issuers.
  • When expenses related to chargebacks, fees, merchandise restocking, labor and investigation, legal prosecution, and IT/software security are taken into account, the cost for each dollar lost to fraud has increased from $2.40 in 2016 to $3.13 in 2019.
  • Networked machine learning models, especially those trained by information gleaned from multiple merchants, payment networks, and issuers, are changing the dynamics of the fraud detection market.
  • Online order for store pickup has become a significant new fraud vector, and it requires new models to detect and thwart.