Sift Science uses large data to guard against cyber fraud

Existing network payment and network transaction fraud prevention system is too complex, also can not effectively eliminate network fraud, Sift science, such as start-up companies began to use machine-learning based on large data analysis to prevent network fraud.

For large data analysis, network security is a potential application field. With large data analysis, the attacker's pattern can be screened to be more proactive in defending against network attacks, rather than a known attack pattern that can only be prevented as a traditional security defense. This article active Defense: Large data-induced information security technology revolution, has mentioned IBM and RSA, some based on large data analysis products. And startups like Cylance, with big data technology, have done their financing.

Recently, another company, Sift Science, uses large data analysis based on machine learning to provide services to protect against cyber fraud. Sift Science recently got the first round of 4 million dollars of investment led by the VC union Square Ventures. After counting the 1.5 million-dollar seed fund, Sift science has already melted 5.5 million dollars.

Sift Science's services are mainly for the Internet trading market, Electronic payment network and E-commerce sites, which is the most rampant network fraud. These sites only need to integrate a section of JavaScript sift science into the Web page to enjoy SIFT science services.

Brandon Ballinger, co-founder of Sift Science, who worked in Google for four years, has been working to guard against a large number of fraudulent ads. Of the engineers in Sift Science, 5 were from Google, two from the search department, and three with Brandon Ballinger, who had worked on a team that had been guarding against fraudulent advertising.

"We realize that every site on the internet will have some ' bad ' users, that is, some users who commit fraud," he said. We created SIFT science to create a fraud monitoring system. "Brandon Ballinger said.

Brandon Ballinger founded Sift Science with his college roommate, Jason Tan, in June 2011. Initially, they financed the 2011 Summer project of Y Combinator. When they communicate with potential customers, the customer's initial response is that there has been a lot of fraud-proof systems already in place, and it seems that the problem with fraud has been solved.

"However, when we really have in-depth communication with our clients, we find that the problem is far from resolved," he said. Many websites buy fraud-proof systems, and almost no one really uses them. "Brandon Ballinger said.

He pointed out that the existing fraud prevention system is still too complex:

They're not as easy to use as Google Analytics or Mixpanel. Moreover, in order to use the fraud prevention system, you need to take a long sales process, need to have installation costs, need to have a minimum fee and so on. And the API is too complex. Existing anti-fraud systems use SOAP APIs, which often take months to integrate into existing systems. Sift Science provides a rest API for this.

Moreover, one of the big problems with existing systems is that they adopt fixed rules. For example, they filter over a certain amount of trading, or a deal from Nigeria. However, the network fraud is not according to the fixed rules of the card, they change, you can easily through the change of behavior to cheat the fraud prevention system.

Therefore, Sift Science uses machine learning algorithms to deal with cyber fraudsters. In the database of Sift science, there are more than 1 million behavioral patterns of network fraud, and it is still being added through machine learning algorithms. For example, some URL browsing order, from the Tor node IP address, from late night transaction information, etc., may be added to the network fraud behavior patterns for analysis.

For each user, the site using SIFT science can obtain the user's fraud-proof score through the API. Site can also be based on the machine learning model feedback, so that the fraud-proof model more suitable for the needs of the site.

This system can be used to detect fraudulent behavior in network transactions, or to help sites find spammers created by network fraudsters. According to SIFT science, their system is able to identify more than 90% of the network fraud behavior of the client site.

Sift Science's products have previously been tested by 20 customers, including Airbnb,uber and Listia. In addition, there are some top E-commerce sites and network payment platform.

The pricing of Sift science products is based on the number of user ratings that the site expects each month. Users below 5000 per month for free, more than 5000 users per month for 10 cents a user.

(Responsible editor: Fumingli)