By Jason Tan of Sift Science
Fraud is an unfortunate fact of life for many online businesses. And when you’re a small, growing company, solving that problem can feel overwhelming – especially when you’re getting your first taste of fraudsters masquerading as good customers.
(Photo courtesy of Jason Tan)
However, as with many complex issues, breaking your fraud-fighting game plan into smaller tasks can help it feel more achievable. Here’s a three-step plan:
Step 1: Do Your Research (From Reputable Sources)
Google much? You’re not alone. If you’re like 74% of businesses, you’re using the internet as your primary source for learning about how to solve your issue – and probably also gathering more info on the problem itself.
The Merchant Risk Council offers unbiased information aimed at businesses of all sizes who are navigating a fraud problem. And some fraud-prevention vendors offer 101-level introductions to everything from the basics of e-commerce fraud to how to prevent it. Your e-commerce platform may also have tips aimed specifically at businesses using their technology.
Step 2: Determine Your Needs
Once you’ve got the lay of the land, it’s time to ask yourself some key questions that will help determine your fraud-fighting approach. Here’s a handy checklist of questions to consider:
How much risk are you comfortable with?
Fighting fraud involves a delicate balance between creating friction for bad users (which could slow down the checkout process for good ones and even lose sales) and reducing friction for good users (which could inadvertently let some bad ones in). You’ll need to find the right balance to meet your company’s current goals.
How much of a risk is fraud to your business?
In other words, if fraudsters were to go nuts tomorrow and take advantage of your website, how much damage could they do? For some businesses, fraud is merely a nuisance. For others, it’s a serious threat to their financial wellbeing.
What do you want your customers to experience?
Introducing hurdles could lower fraud, but legitimate users may get fed up and start looking to your competitors for their shopping needs.
How much fraud are you experiencing?
If you’ve got a small fraud problem, maybe an existing team – like customer success – could take on the responsibility of managing it. If your fraud problem is growing rapidly, you may want to consider an in-house fraud team, plus a machine learning-based fraud solution to automate decision-making.
How rapidly do you need to react to fraud?
Businesses that rely on speed and instant delivery – like on-demand, travel, and digital goods – are wise to look at machine learning-based fraud solutions that learn and adapt in real time.
When will you be making decisions?
Is it after the payment’s collected, after you ship a package, after you’ve issued a gift card, or at some other time? Some vendors deliver risk scores at different points in the purchase flow.
Your answers to those questions will help you determine important pieces of your fraud approach, like whether or not to use a third-party tool – and what type of solution would fit your needs best.
Step 3: Gather Social Proof
When it comes to choosing a tool to augment your internal efforts at spotting and stopping fraud, it can be hard to directly compare your options. Fraud-prevention solutions vary significantly in terms of the features they offer – not to mention their pricing model, ease of integration, and level of support.
Of course, every business is different and has unique needs, but the key is to find companies that share similar challenges – perhaps they rely on fast delivery or specialize in big-ticket items, like you do – and find out who they’d recommend. Talk to your colleagues and friends, and to their colleagues and friends. Join local meetups and online discussion groups for your industry, and find out who’s using what approaches and tools.
One easy way to assess whether a company is reputable or not is by looking at the logos listed on their website, and then seeking out case studies. Are they working with well-known brands, with similar business models to your company? Do their case studies list concrete results for their customers, like how much they lowered chargebacks, what ROI they delivered, or how many manual review hours were saved? If you don’t see hard results listed on their website, reach out to the sales team. Companies that aren’t able to substantiate their claims should be approached with caution.
Jason Tan (@jasontan) is the Co-Founder and CEO of Sift Science, a San Francisco technology company that fights online fraud with large-scale machine learning. He previously served as CTO of BuzzLabs, a machine learning startup acquired by InterActiveCorp. Prior to that, he was an early engineer at two Seattle startups, Zillow and Optify. Jason graduated magna cum laude from the University of Washington in 2006 with a Computer Engineering degree.
The views, opinions and positions expressed within this guest post are those of the authors alone and do not represent those of CBS Small Business Pulse or the CBS Corporation. The accuracy, completeness and validity of any statements made within this article are verified solely by the authors.