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Eコマース不正検出ソリューション:市場概要

E-Commerce Fraud Detection Solutions: Market Overview

出版日: | 発行: Mercator Advisory Group, Inc. | ページ情報: 英文 19 Pages | 納期: 即日から翌営業日

価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=105.60円
Eコマース不正検出ソリューション:市場概要
出版日: 2020年02月25日
発行: Mercator Advisory Group, Inc.
ページ情報: 英文 19 Pages
納期: 即日から翌営業日
  • 全表示
  • 概要
  • 目次
概要

機械学習ツールは、不正検出の方法を大きく変えました。機械学習技術が急速に進歩する中で、不正検出プラットフォームのモデルも大きく進化しています。これらのモデルは現在では、同一のプラットフォームを運用する複数サイトでのアクティビティ、あるいは決済ネットワークから直接得たデータの監視・学習を行うことができます。

当レポートでは、Eコマースの不正検出ソリューションを調査し、55の不正プラットフォームサプライヤーのソリューションをカテゴリー別に評価します。

調査内容のハイライト

  • 決済バリューチェーン全体の詐欺の総コスト費用の特定や特別の役割に対する費用の割当ては困難であり、複数の詐欺のベ??クトルの複雑性、詐欺の損失を計上手法に一貫した方法がないこと、加盟店・決済ネットワーク・カード発行者間の責任の割り当てなどの困難さがその原因となっています。
  • チャージバック・手数料・商品補充・労働力および調査・法的手続き、IT/ソフトウェアセキュリティに関連する費用を考慮すると、不正による1ドルの損失あたりのコストは2016年の2.40ドルから2019年には3.13ドルに増加しました。
  • ネットワーク化された機械学習モデル、特に複数の加盟店・決済ネットワーク・カード発行者から収集した情報によって訓練されたモデルは、不正検出市場のダイナミクスを変えています。
  • オンライン注文・店舗でのピックアップは、詐欺の深刻な新しいベクトルになっており、検出・阻止の新しいモデルが必要です。
目次

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.
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