F5, Inc (Shape Security)
F-Five (Shape Security)
Shape AI Fraud Engine (SAFE)
Accurately discriminate between legitimate users and unauthorized users who commit online fraud
There has been an increase in the number of fraudulent acts that misuse user environment information and digital fingerprint information to pretend to be the real person, and there are more cases where it is not possible to distinguish between normal users and unauthorized users due to the strengthening of the authentication level. SAFE catches signs of unauthorized access from actions and operations by "humans" on web/mobile applications. An online fraud detection service that uses machine learning modeling rather than rule-based to detect online fraud.
Protect and optimize the entire customer journey by detecting unauthorized access not only at login, but also after login
An example of a SAFE use case
- Prevent unauthorized acquisition of credit cards, etc.
- Two-factor authentication is not enforced each time when authenticating or purchasing a product, but only when transactions are considered suspicious.
- Prevent account hijacking by using login information illegally stolen by phishing, smishing, etc.
- Detecting the use of fraudulently obtained credit card numbers
Differences between SAFE and risk-based authentication
Risk-based authentication uses the application usage history of authorized users to generate a profile of the authorized user "the person". If there is access, we will compare it with the profile of the authorized user and generate a score such as "personality" to judge the risk.
SAFE, on the other hand, picks up the "habits and signs" of the unauthorized accesser's operations, finds out the access from the unauthorized accessor, and judges the risk. Create custom machine learning models for each customer to detect fraud patterns unique to that site.
Inquiry/Document request
In charge of Macnica F5 Co., Ltd.
- TEL：045-476-2010
- E-mail：shape-sales@macnica.co.jp
Mon-Fri 8:45-17:30