Balancing Robust Fraud Prevention With Seamless Online Shopping

TL;DR

Merchants can balance strong fraud protection with a smooth checkout by adopting approaches like machine learning, multichannel coverage, real-time analysis, and data-driven strategy updates. Overly aggressive filters cause costly false declines, which cost merchants $118 billion in 2016, compared with about $9 billion lost to actual fraud. A multilayered solution that combines human analysis, big data, and AI prevents fraud while keeping checkout frictionless.

The latest numbers on e-commerce sales have merchants cheering: In 2016, 70% of U.S. merchants reported an increase in online and mobile sales over 2015.

However, fraud has also risen in these channels, with a whopping 60% of online and mobile merchants experiencing fraud, often due to stolen customer data.

As a result, both merchants and consumers have mixed emotions about e-commerce. While consumers enjoy the simplicity and convenience of ordering online, they know the personal data they submit is at risk of being stolen and used against them. And yet, they still want their online shopping to be as easy and smooth as possible.

To build customer confidence and increase transactional security, online retailers must add more vigorous and effective fraud protection measures. But this has merchants wondering whether they can balance a robust fraud protection solution while also facilitating a smooth customer experience.

How Merchants Are Changing Their Fraud Prevention Strategies

Data breaches exposed nearly 2 billion personal records (not including records from the Equifax breach) in just the first half of 2017, which means fraudsters don’t have to look far to acquire sensitive data. Merchants are therefore rightly concerned about being defrauded, which means they are often quick to decline any transaction that gets caught in their fraud filters. However, that can increase the risk of inadvertently rejecting legitimate sales. In fact, false declines cost merchants $118 billion in 2016, while losses due to actual fraud were closer to $9 billion.

No merchant wants to be part of these statistics. To keep pace with the evolution of fraud, online retailers are implementing new fraud protection approaches, like:

Incorporating Machine Learning

Manually reviewing every transaction would be expensive, time-consuming and subjective. But machine learning’s algorithms can quickly and objectively identify unique fraud patterns within a merchant’s transactions. This automated analysis is a useful first line of defense to identify questionable transactions and set them aside for further review by specialized staff.

Covering Multiple Channels

Customers use numerous devices — like PCs, tablets and smartphones — to do their browsing and shopping. An online merchant’s fraud protection should also span these e-commerce access points to ensure customers are protected regardless of the channel they come through.

Acting in Real Time

Immediate gratification is the name of the game for shoppers and fraudsters today, so merchants must employ fraud prevention strategies that can quickly evaluate and react to fraud threats.

Using Data to Tweak Prevention Strategies

Fraudsters’ strategies are constantly evolving; fraud protection strategies must, too. E-commerce merchants should evaluate all available analytics, like success rates and customer feedback, and use them to make informed transactional decisions and update their fraud prevention approach.

How These Approaches Can Impact the Customer Experience

Having a robust fraud protection plan can help merchants flag legitimately false transactions. But there’s a catch: Merchants may find that their new fraud prevention controls end up increasing customer friction (and revenue losses) more than they decrease the fraud risk.

While customers appreciate the need for increased security, they don’t want authentication steps to take too long or be too complicated — one-click retailers like Amazon have set the convenience bar high when it comes to quickly placing an order. So if frustrated shoppers find it’s too cumbersome to complete an online purchase, they’ll turn to other retailers to place the order or even to social media to voice their complaints.

But when an e-commerce retailer implements a comprehensive program, they can integrate powerful fraud protection with a seamless customer experience. Real-time authentication means no need for step-up challenges or tricky CAPTCHAs (a challenge-response test that differentiates between humans and robots) to discern between fraudsters and true customers. And that results in satisfied customers who are likely to come back and buy more.

How the Right Fraud Solution Can Protect and Delight

Customers continue to flock to the online marketplace, enjoying the convenience and immediate gratification it offers. But if customers don’t feel an e-commerce merchant can provide a seamless and secure shopping experience, they won’t hesitate to move on to someone who can.

E-commerce merchants who invest in the right fraud prevention solution can protect clients (and themselves) against both criminal activity and the friction that often comes with increased security. That right solution is one that employs “deep learning” — a multilayered approach that combines the best of human analysis, big data and artificial intelligence. It’s this unique combination that offers online retailers the chance to prevent fraud, slash false declines and still provide customers with effortless shopping.

Companies worldwide are using ClearSale’s approach to strike the delicate balance between fraud protection and customer friction. Download our “Online Credit Card Fraud Risk” eBook to learn how our approach can help you safeguard your profits, reputation and customer relationships.

Frequently Asked Questions

Can merchants have strong fraud prevention and a smooth customer experience at the same time?

Yes. A comprehensive, multilayered program can integrate powerful fraud protection with a seamless experience. Real-time authentication removes the need for step-up challenges or CAPTCHAs, so legitimate customers check out smoothly while fraud is still caught.

How much do false declines cost compared with actual fraud?

False declines cost merchants $118 billion in 2016, while losses from actual fraud were closer to $9 billion. Aggressive fraud filters that decline transactions too readily increase the risk of rejecting legitimate sales.

What fraud prevention approaches are merchants adopting?

Merchants are incorporating machine learning to flag questionable transactions, covering multiple channels such as PCs, tablets, and smartphones, acting in real time to evaluate threats, and using analytics like success rates and customer feedback to update their strategies as fraud evolves.

Why is machine learning useful in fraud prevention?

Manually reviewing every transaction would be expensive, slow, and subjective. Machine learning algorithms quickly and objectively identify unique fraud patterns within a merchant's transactions, serving as a first line of defense that flags questionable orders for further review.

What happens when fraud controls add too much friction?

Customers accept the need for security but dislike authentication that is slow or complicated, since one-click retailers have set a high convenience bar. If checkout becomes too cumbersome, frustrated shoppers move to other retailers or voice complaints on social media.

What kind of solution balances fraud protection and customer experience?

A solution that uses deep learning, a multilayered approach combining human analysis, big data, and artificial intelligence. This combination lets online retailers prevent fraud, reduce false declines, and still give customers an effortless shopping experience.