Sift
shift
Fraud prevention and machine learning platform
Online fraud prevention solution that protects against online fraud with machine learning
Machine learning instantly identifies whether a transaction partner is a trustworthy user on EC sites and the Internet. Online fraud can be prevented by real-time detection of users engaging in account hijacking, impersonation, use of stolen credit cards, creation of fake accounts, and distribution of spam and illegal content.
Strength ①: Highly accurate risk determination
Machine learning automatically learns fraud trends and calculates risk values. Fraud trends are determined by combining Sift's global knowledge with what is learned for each service, making it possible to make highly accurate judgments that are appropriate for each service.
Sift holds over 16,000 features based on data accumulated worldwide. Risk judgments are made by combining the necessary features on the data that is actually sent, such as the characteristics of the accessing device, the entered email address, member information, and shipping address. (Score link from 0 to 100)
It also combines several types of machine learning models and performs calculations with the optimal engine. The learning model is optimized not only based on globally shared features but also based on the customer's environment.
Strength ②: Simple anti-fraud operation that does not require rule tuning
Automatically learns fraud trends by providing feedback based on actual data. Feedback can be provided with a single click or can be automated, making it possible to implement fraud countermeasures while reducing operational burden.
Strength 3: Flexible implementation of business logic
You can build business logic based on various conditions from the Sift management screen. There is no need to implement complex business logic on the EC system side.
[Product 1] Payment Protection
Anti-fraud solution. Efficiently detect fraudulent payments by machine learning trends in fraudulent payments on EC sites.
[Product ②] Account Abuse
It is a countermeasure solution for fraudulent account use. Machine learning of fraud trends to detect fraudulent use of applications.
[Product ③] Account Defense
An account takeover solution. High-precision fraud detection prevents account takeovers without compromising the convenience of legitimate users.
Case study
Sift - User case - istyle Co., Ltd.
Combined use of Sift and EMV 3D Secure (3D Secure 2) realizes anti-fraud measures that avoid the risk of cart abandonment
Sift - User Case Study - Company A, a major cosmetics brand
Sift protects EC site sales floors in combination with EMV 3D Secure (3D Secure 2.0), a card fraud countermeasure that can be introduced in linked payments
Sift - User case -freee K.K
Freee Co., Ltd. (hereafter, freee) supports the back-office operations of small and medium-sized enterprises with SaaS, with the mission of “making small businesses the leading players in the world”. What the company, which has achieved rapid growth with a freemium strategy, could not overlook was the behavior of suspicious users who abused free accounts.
Seminar
これから開催 開催終了 {{ item.title }}
{{ item.read }}
{{ item.seminar_start_date__display_1 }} {{ item.seminar_place__display_1 }}
video on demand
company description
| company name | Sift, Inc. |
|---|---|
| Established | 2011 |
| location | United States San Francisco |
| representative | Jason Tan |
| website | https://sift.com/ |
Inquiry/Document request
In charge of Macnica Sift Co., Ltd.
- TEL：045-476-2010
- E-mail：sift-sales@macnica.co.jp
Weekdays: 9:00-17:00