Fraud is a costly problem for merchants, and it’s growing. The cost of fraud to retailers is up 6.6% this year over last, according to LexisNexis. And merchants may have been extra concerned about fraud during the holiday shopping season, because the crush of holiday orders and unusual shopper behavior (binge-buying, shipping to multiple addresses) can make it hard to detect fraudsters online and at curbside for BOPIS pickups.
To avoid a slew of holiday fraud that can raise their chargeback ratio in January, merchants may decide to automatically reject any order that doesn’t meet strict fraud screening criteria. Some of those merchants will get through the holiday season with no chargebacks at all — but they may have created a worse problem.
We can all agree that stopping fraud is important, because every dollar of fraud now costs online retailers $3.73 in related costs. Eliminating fraud from your store is a worthy goal, but only if you can do so in a way that doesn’t cost you more in the long run.
Unfortunately, many merchants who think they have fraudsters beat don’t have all the information they need to see the whole picture. If their fraud program automatically rejects every suspicious order without further review, even for a simple AVS (Address Verification Service) mismatch, then all those rejected orders may get recorded as fraud attempts — and none of those presumed fraud attempts succeeded.
However, industry research indicates that anywhere from 30% to 65% of orders that are classified as attempted fraud are good orders that could have been approved with manual review.
Turning away that many good orders can decrease your profit, and it can cause other costly problems, too.
ClearSale commissioned a consumer attitudes survey from Sapio Research in March 2020, among 1,000 online shoppers each in the U.S., UK, Canada, Mexico and Australia. Perhaps the most striking finding was that in each of the five countries in the survey, consumers said they were less forgiving of false declines than fraud.
On average across all the countries, 39% of online shoppers said they’d never shop again with a merchant that turned away their order. But only 14% said they’d stop shopping with an online merchant after a fraud experience in their store.
Here’s where the math can get ominous. If 30% of the orders your fraud program rejects are good, and 39% of those shoppers are never coming back, you can see that you’re creating a problem with your customer lifetime value. If your false decline rate is at the high end of the range, 65%, the problem is much worse.
Losing good customers because they were mistaken for fraudsters is bad enough. But the damage to merchants doesn’t stop there. Our survey found that one-quarter of online shoppers were likely to say something negative on social media if their order was rejected in error.
What happens if your brand is tagged by a shopper who’s frustrated that their holiday gift-giving plans were derailed by your store? With every negative comment, your reputation among prospective customers decreases. Convincing them to give you a chance gets harder and more expensive as time goes on.
There’s one more issue with strict automatic-reject fraud rules, too.
If your fraud screening app uses AI and machine learning, in theory it should get better over time at separating fraud attempts from legitimate orders. However, even the smartest algorithms need accurate information to shape their decisions. If they never get feedback that some of the orders they marked as fraud were good, they’re going to keep flagging and rejecting good orders. That’s going to keep costing you profit and customers.
There are two ways to figure out how many good orders you may be turning away. The first is a back-of-the-envelope estimate. You can multiply a figure in the industry estimate range — again, anywhere from 30% to 65% of your rejected orders — to get a good guess.
The other way is more precise but more labor-intensive. If you have access to your rejected holiday-season orders and to some fraud review analysts, you can take a few random batches of those “bad” orders and check them all to see which ones were fraud attempts and which were false declines. Doing this takes some time, but it can give you the most accurate data on your false decline rate during the holidays.
You can take the analysis a step further by comparing batches from the holiday shopping season to batches from other times of the year, to see if you’re rejecting more good orders during holiday sales peaks. If you’re turning away good customers regularly, especially during the holidays, it’s time to take steps to correct the problem.
As you plan your fraud rules for next holiday season, find a way to review flagged orders instead of rejecting them automatically. If you don’t have the in-house resources to do manual reviews, consider working with a third party, at least during your peak sales times, to do the reviews for you.
If you can put manual review in place permanently, you can avoid losing good customers year-round, which can mean more customers coming back to your store for next year’s holiday shopping. That’s a gift that keeps on giving to your business.
Original article published here: https://retailtouchpoints.com/topics/security/payment-security/if-your-store-had-no-ecommerce-fraud-this-holiday-season-you-may-have-a-larger-problem