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市场调查报告书

电子商务诈骗侦测解决方案:市场概要

E-Commerce Fraud Detection Solutions: Market Overview

出版商 Mercator Advisory Group, Inc. 商品编码 926563
出版日期 内容资讯 英文 19 Pages
商品交期: 最快1-2个工作天内
价格
电子商务诈骗侦测解决方案:市场概要 E-Commerce Fraud Detection Solutions: Market Overview
出版日期: 2020年02月25日内容资讯: 英文 19 Pages
简介

机器学习工具大幅改变了侦测诈骗的方法。机器学习技术快速发展,而诈骗侦测平台模型亦大幅演进。这些模型现在可以监控和学习运用同一平台的多个网站活动、或是从支付网路直接获得的数据。

本报告研究电子商务诈骗侦测解决方案,针对55个诈骗平台供应商的解决方案分类进行评估。

研究报告重点

  • 辨别整体支付价值链的诈骗总成本、或是特定角色的成本分配是相当困难的。造成这些困难的原因包含多种诈骗媒介的复杂性、以及缺乏一致的方法计算诈骗损失以分配商家、支付网路、发卡者之间的责任归属。
  • 若考虑争议交易和退款、手续费、商品补货、人工和调查、法律起诉、IT/软体安全相关费用,诈骗造成之每1美元的损失成本,从2016的2.40美元于2019年增加为3.13美元。
  • 网路化的机器学习模式,尤其是那些透过搜集来自多个商家、支付网路、发卡者情报训练而来的模型,正改变著诈骗侦测市场动态。
  • 线上订购商店取货已成为重大的新诈骗媒介,其需要新的诊测和组织模式。
目录

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.