The Value Of Fighting Fraud

Protecting themselves against fraud — merchants understand this as a valuable notion.

What some might not realize, however, is that that they can actually affix a dollar amount to what implementing one fraud prevention solution in particular — Forter’s Decision as a Service — can earn them in increased business.

Having recently released the results of a study conducted with Forrester Research on the ROI of his company’s solution, Bill Zielke, CMO of Forter, spoke with PYMNTS about that and the technology behind the solution, as well as plans currently in the works to lift the veil on the identities of several different fraudsters.

Zielke describes Decision as a Service as a “fully automated, real-time fraud prevention solution.” In operating it, Forter looks at every single transaction and gives retailers a yes or no answer in real time, eliminating the need for manual reviews.

“We charge retailers only for what is approved, which aligns our value with theirs,” the CMO adds, “and best of all, we back all of our decisions up with a 100 percent chargeback guarantee.”

Forter recently completed a study with Forrester Research called the Total Economic Impact (TEI), which provides retailers with a fully quantified ROI from their solution with Forter. (The company will be hosting a webinar on those results on Oct. 20.)

The study found that Forter lifted sales approximately 15 percent in year one, contributing to over 150 percent ROI.

Zielke highlights “three key areas” in which the solution drove value: increasing the number of approved transactions, lowering false positives by almost 80 percent and reducing the number of declined transactions.

“Through those areas,” he remarks, “we were able to generate that over 150 percent ROI — really, almost $4.5 million in net present value on a three-year basis.”

In addition to that 15 percent sales increase, the Forrester Research study also showed an order increase of 8 percent through the use of Decision as a Service. A key contributing factor to that statistic, according to Zielke, is automation.

“A lot of the retailers that are live today are using legacy systems that are based on rules or scores, and that’s predominately how they’re fighting fraud on their eCommerce channel — both Web and mobile,” Zielke explains. “What we’ve found at Forter is that those rules and scores produce a tremendous amount of manual reviews.”

He cites a recent LexisNexis study showing that nearly half of all orders (48 percent) are flagged for potential fraud, and about half of those (46 percent) go out for manual review — many of which produce friction during the checkout process, with retailers asking consumers for more information or stepping them up through additional programs.

“At Forter, we believe that if you are able to offer an automated, real-time fraud prevention system, you can eliminate that friction during checkout — and that’s really where you’re going to get your ROI,” says Zielke. “One reason is that less friction during checkout reduces card abandonment and cancelled orders. More importantly, you’re able to reduce the false positives because of the increased accuracy that you’re getting with a real-time system over those that rely on manual review — which often involve customer service reps that may not have the experience, expertise and/or training to make the right decisions on those types of transactions.”

As far as the cost of the solution, Forter charges retailers on a percentage of transactions based on two dimensions: their size (how much volume they’re processing), as well as the risk category. Zielke says that, on average, Forter charges 40 basis points up to “about 1 percent, depending on the dimensions of risk and size.”

Central to the effectiveness of Decision as a Service is Forter’s ability to recognize fraudsters, a task that relies on three layers of technology: behavioral analysis, cyberintelligence and elastic identity.

Zielke describes the behavioral analysis layers as “a very small JavaScript that persists on each of the retailer’s pages, and it helps Forter and its engine look at things like where the user is shopping, where their mouse may be going, how long they spend on pages — even things like the browser language. These are important details because about 80 percent of the time we can determine whether we’ve got a fraudster just based on how they’re shopping and how they’re behaving.”

In the cyberintelligence layer, Forter looks at elements like the user’s IP address and email address in conjunction with their bill-to/ship-to addresses and their AVS (Address Verification System) match. “Combining that with the behavioral layer,” says Zielke, “we’re looking for trends that make up the overall story of the transaction. That often helps us make an informed decision, all in real time and based on machine learning.”

To build the elastic identity layer, Forter brings in “thousands of data elements” — according to Zielke — some from external sources such as LinkedIn, Facebook and Twitter, which are used to establish the identity of the user. “We’re looking to determine if the owner of the card is the one who’s shopping online, as well as the reverse,” he comments.

“With those three layers of technology,” Zielke goes on to explain, “we’re able to really make very accurate decisions in less than a second: 300 to 500 milliseconds is the approximate time it takes for us to make a decision, all without the need for a manual review.”

Forter is currently in the process of profiling fraudsters — “not unlike how a marketer would profile consumers,” notes Zielke — to understand their tactics: how they are defrauding merchants on their websites, as well as how much they’re stealing, what goods they prefer, their method of choice, et al.

Zielke tells PYMNTS that Forter has so far identified six different profiles of the more common fraudsters — “some sophisticated in their tactics, others not so much.”

“We’ll be releasing that study in just about a month,” he adds, “so that retailers can get an idea of the different profiles.”