Sometimes, it can feel like there are as many flavors of fraud as there are of artisanal ice cream (though fraud – admittedly –  is much, much less enjoyable on a lazy summer’s day). Every day, we talk to all sorts of businesses who are fighting fraud, and we get to hear about what tactics they used before discovering Sift Science.

Based on our customers’ experiences, here are the top three missteps made by companies combating fraud:

1. Relying on humans alone

Your people are smart. Your team members are savvy. No one’s disputing that. In fact, your in-house fraud detectors are likely to be experts in their field and serious bada**es at cross-referencing incoming data with external sources of info to block bad transactions.

However, people are also moving at the speed of – well, people. Fraud, however, pummels fast and furious. Where human-based fraud detection tends to fail is around both speed and scale, particularly if you’re dealing with real-time commerce or on-demand businesses.

2. Relying on a rules-based system alone

At first, this approach makes tons of sense. If you’ve noticed that orders placed during a certain time period like 3 a.m. tend to be fraudulent, creating an “if x, then y” logic to block all orders for that time period where the shipping and billing addresses don’t match is obvious.

But rules run the risk of penalizing your most precious asset: your legitimate customers. Following the example above, can you imagine that a real – albeit nightowl – buyer with real money could possibly have different shipping and billing addresses? Of course you can. How about someone who just moved across timezones? Or a college student? It happens.

That’s one of the main reasons rules-based fraud detection just doesn’t cut it. Machine learning, on the other hand, uses a huge number of potential signals to paint a more nuanced and accurate picture of fraud on your site.

3. Relying on machines alone

So, what powers the incredible accuracy of machine learning fraud solutions?  Well, it all starts with people. Sift Science already has an unparalleled advantage (sorry, we just gotta say it) compared to other solutions: a global network of thousands of potential fraud signals.

However, the real magic begins when you marry that power with human insight by labeling users as “bad” and “not bad” to help train the system and tailor its intelligence to your particular business and industry. Machine learning is accurate, but to make it a custom-fit for your needs, you need to teach it how to work for you. Not only will you stop more bad guys, you’ll also smooth the way for your good customers to have the speediest, easiest experience using your site.

The takeaway

Every business is different, but we’ve found that the recipe for successful fraud fighting shares some common elements. Try looking for a flexible solution that incorporates the real-time responsiveness of machine learning, supports automation (if it makes sense for you), and frees up your in-house fraud team to focus on the small subset of orders that need their attention. You have bigger and better things to worry about.