September 13, 2019
Macnica Networks Corp.

- Machine learning security suitable for implementing mandatory countermeasures against credit card fraud at e-commerce member stores -

Macnica Networks Corp., which provides network devices and security solutions (headquarters: 1-5-5 Shin-Yokohama, Kohoku-ku, Yokohama, Japan; President: Jun Ikeda; hereinafter "Macnica Networks") is pleased to announce its conclusion of a distributor agreement with Sift Science, Inc. (Headquarters: USA; CEO: Jason Tan (Co-founder); hereinafter "Sift").

In recent years, various initiatives have been promoted for achieving a cashless society, and the reduction of cash payments is expected to reduce the workload of operations and bring down costs. On the other hand, losses due to credit card fraud in Japan from January to December 2018 totaled 23.5 billion yen, and the amount has been growing from year to year. *1 Most of this loss is due to cases of identity theft involving fraudulent use of information from stolen credit cards. Many such incidents occurring at e-commerce member stores.*2

Under these circumstances, on March 1st, 2019, the Credit Transaction Security Council released the “Action Plan 2019 for the Consolidation of Security Measures for Credit Card Transactions” (hereinafter “Action Plan 2019”). The Action Plan 2019 is the practice guideline for the implementation of security measures at member stores, as mandated by the Installment Sales Act. And businesses are expected to strengthen their measures against credit card fraud further.

Sift’s Digital Trust and Safety Platform (SaaS), uses machine learning technology to determine if someone making a transaction online is a trustworthy user. By identifying in real-time those users who engage in activities such as account takeover, identity theft, use of stolen credit cards, creation of fake accounts, or the spreading of spam or illegal contents, the Sift platform can prevent internet fraud.

Sift’s platform manages attributes, behavior analysis, and delivery that address the fraud countermeasures described in Action Plan 2019. For cases of credit card payments in non-face-to-face transactions, the Sift platform performs a real-time risk assessment of the purchase based on data available to the e-commerce member store, such as the consumer’s attributes, activities, and postal address, and can instantly detect and reject any fraudulent use. The Sift Digital Trust and Safety Platform allow e-commerce member stores to request further personal authentication in cases judged to be high-risk. Lower risk transactions are processed, avoiding the potential loss of sales opportunities from customers abandoning their shopping carts.

Macnica Networks is distributing the Sift Digital Trust and Safety Platform to help prevent losses from internet fraud in Japan and is contributing to the a safe and reliable cashless society.

*1: Japan Consumer Credit Association: Occurrence of Losses Due to Credit Card Fraud, June 2019
https://www.j-credit.or.jp/information/statistics/download/toukei_03_g_190628.pdf

*2: Japan Consumer Credit Association: Action Plan 2019 for the Consolidation of Security Measures for Credit Card Transactions
https://www.j-credit.or.jp/security/pdf/plan_2019.pdf

Features of Sift

• Machine learning capable of identifying behavior and making predictions beyond the ability of rule-based learning
  The Sift Platform through machine learning and identifying patterns of fraud can quickly and accurately identify users engaging in fraudulent activities (i.e., account takeover, identity theft, credit card fraud, creation of fake accounts, or spreading of illegal contents)
• An AI engine employing a global network protecting over 34,000 websites and mobile apps
  There are over 34,000 websites and mobile apps using Sift, including names such as Airbnb, Twitter, and Yelp. Sift provides all clients in its global network with fraud-related information gathered from businesses since 2011, and the results learned by the AI engine are shared.
• Automation through machine learning
  By extracting the characteristics of fraudulent behavior, an effective countermeasure to internet fraud goes through an automated workflow. Fraudulent transactions can automatically be blocked or submitted for manual review.

Sift: Company Outline

Sift’s Digital Trust and Safety Platform used by global businesses such as Airbnb, Fareportal, Western Union, Yelp, Jet, and Twitter, also have investments from such organizations as Insight Venture Partner, Union Square Ventures and Spark Capital, worth 100,000,700 USD. Other investors include Max Levchin, former CTO of PayPal, and Marc Benioff, founder of Salesforce.

*The company names and product names used herein are registered trademarks or trademarks of the respective company.