Discussing Forter with CTO Iftah Gideoni

This conversation covers:

• The value that Forter provides, and the types of companies that they work with. Iftah also explains what makes Forter so unique. 
• The underlying technology that Forter is using, and how they quickly process hundreds of complex backend workflows. Iftah also talks about some of the tools that they are using, including AWS and Apache Storm.
• How Forter approaches the cloud, and how it’s helping them concentrate on the business of detecting fraud. In addition, talks about the types of cloud services that Forter is using.
• Forter’s ability to scale — including how they responded to increased customer demand during COVID-19.
• Forter’s biggest technical challenge that they are currently working through.
• Iftah’s thoughts on the security- speed tradeoff.

Links:

• Forter
• Forter on Twitter
• Connect with Iftah on LinkedIn
• Iftah’s email: [email protected]

Transcript:
Emily: Hi everyone. I’m Emily Omier, your host, and my day job is helping companies position themselves in the cloud-native ecosystem so that their product’s value is obvious to end-users. I started this podcast because organizations embark on the cloud naive journey for business reasons, but in general, the industry doesn’t talk about them. Instead, we talk a lot about technical reasons. I’m hoping that with this podcast, we focus more on the business goals and business motivations that lead organizations to adopt cloud-native and Kubernetes. I hope you’ll join me.

Emily: Welcome to The Business of Cloud Native. I'm Emily Omier, your host, and today I'm chatting with Iftah Gideoni. Iftah is the CTO at Forter. Iftah, first of all, thank you so much for joining me.

Iftah: Very glad to be here.

Emily: So, I wanted to have you start by introducing yourself and what you do, and then also what Forter does.

Iftah: Hi, I'm Iftah. I’m a physicist of education, and in the last 20 years, a CTO of several companies, mostly [00:01:11 unintelligible] governmental companies, and companies that I founded. In the last six and a half years, I'm with Forter. And what Forter started to do from 2014 is to provide what was, at the time, very bold vision of fully automated, fully cloud-based decisions about whether to allow or decline e-commerce transactions. 

Now, from that time we actually implemented and executed that, we decide very many more than 3 million transactions every day, today, all in real-time without a human in the loop. And we expanded into being a fully-fledged trust engine that gives decisions not only about transactions, but about many other points of interaction with the consumer, for example, in their login time, and in other points where trust decision is needed.

Emily: So, just because I think it might be interesting to listeners, give me some examples of, like, when somebody might interact with Forter or have some sort of action approved or declined by Forter.

Iftah: Right. The prime customers of Forter are the big e-commerce enterprises. Think about the [00:02:42 Sephoras], the Nordstroms, the Home Depots, and this kind of companies. And whenever you press the button of requesting to committing to the purchase and you see this small things rounding on the screen, then it is sent to Forter and Forter within, usually, half a second returns a decision. 

Now, Forter does not act as an additional data point, or input, or score into some system of the merchant. It actually answer whether to approve or decline the transaction. In very many—and most of the revenue of Forter comes from a covered transaction that, if this transaction was fraud, it’s on Forter. Forter will guarantee it. And we were pioneering this model to putting our mouth where our money is.

Emily: Tell me just a little bit about why this is so difficult. What makes what Forter does unique?

Iftah: What Forter does is unique because it tells the human story, and takes it all the way to the decision itself. For example, it's very easy to approve the fourth transaction of a person that is sitting at home, browsing from home, making the purchase on the same desktop they made at previous times, and sending the shipment to the same home. That's very easy. But we want to be able to approve the traveler, the person that is sending a gift to a third party, or a person that is sending a gift to another state while not browsing from home and not from his common device. 

We want to be able to approve those transactions that are checking out as guests from a new device and that's the first time this person ever appeared on our radar. And the ability to do that and to take the calculated risks and to look at the behavior, the cyber clues, and still be able to tell that this is indeed a new person and not someone that visited before and is trying now to hide. That's what makes what we do very difficult and complex.

Emily: So, tell me a bit about the technology story. What technology do you use to accomplish this, and how does it work? What does your stack look like?

Iftah: When I came to—from 2014, I looked at the system and what is actually needed in order to cater to such a complex story? And I thought to myself—and we'll talk about maybe a bit later about how all this is excellently suited for the Cloud, but what I found that throughput and big data is not the problem. First, it’s more or less solved, but it is the e-commerce business; it's not Facebook scale throughput. And on the other hand, it's not hardcore real-time, right? We're talking about tens of milliseconds, not the microseconds domain. 

What is extreme about what we do is the complexity of the flow. We have hundreds of processes that are needed to be ran within that half a second in order to test, and check, and infer, and decide on many aspects of this transaction and of this person. So, first, we started from Amazon Web Services, and we started with, actually, Apache Storm. And why we decided that because we wanted to have something that enables first, a lot of parallelism—doing many things in parallel—with smart joins, that is with processes that takes information from other processes that executed in parallel, and can decide whether what they have so far from these processes is enough. Because we are very high availability, we didn't lose more than 10 seconds straight in the last four years. We are very high availability, but a lot of our sub-processes are not. 

So, you need such a machine that will be able to infer about whether the information at hand is good enough and to move forward and still give, after half a second, the answer. We also wanted to have within this high availability system, we wanted to have the domain experts, the analysts, and the fraud researchers, we wanted to give them a very direct access to the code and each insight that they get, in close to real-time, maybe in 10 or 15 minutes from the time that they understood that there is a new wave of attacks or a new fraudster in action in a particular store or across stores. We wanted all these insights to be manifested in the sys...