Knowing New Customers – And How Shared Data Helps in Fighting Fraud

To maintain a good customer experience, it is vital for merchants to be able to support and protect consumers as they move across channels – and this requires the ability to accurately identify them and respond with an appropriate level of service.

Adapting to change as fulfilment options evolve

‘Click and collect’ is an interesting example. Since its early beginnings in the grocery segment, there has been demand for this facility across many sectors – and there’s every reason to believe this will continue.

But while click and collect delivers speed and convenience for customers, it also brings challenges for fraud prevention that merchants need to address. For example, as the ultimate in fast fulfilment options, merchants need to be able to quickly and accurately screen and approve customer orders while blocking fraudulent ones. The lack of shipping address information also means merchants are missing an important factor against which they can check for fraud.

The changes in shopping behavior and preferences, plus the growing volume of new customers, mean that merchants need to find new or alternative detection methods to supplement the more traditional ones.

Knowing your customer

Without clear visibility of customers, merchants run the risk of losing new business before a customer has even bought anything. And that loss can be very damaging – after all, consumers have plenty of choice.

If a new customer’s transaction is mistakenly declined, or if they abandon their purchase through frustration around extra security measures, it’s not just that one purchase that is lost – it’s the entire potential lifetime value of that customer.

But if a merchant can see beyond the confines of their own transactional data, this becomes a different story. If a merchant could see that a customer – who was brand new to them – had a good buying history with other merchants and all their data matched up, they could confidently accept that new customer order.

This is exactly where positive profiling – a combination of consortium intelligence and big data analytics – can help. By using detailed behavioral data, externally confirmed fraud intelligence and an extensive range of customer identifiers, merchants can build a more holistic picture of customers at the individual level. Rather than the traditional route of screening each transaction, this focuses fraud screening on the person behind that transaction.

This approach can give merchants a better understanding of their customers (both new and existing), offering the ability to tailor the customer experience, improve conversion rates and maximize revenue – while blocking fraud in the process.

Still no silver bullet

The opportunity to ID customers as well as fraudsters can be invaluable for online merchants, especially as volumes continue to increase and merchants look to expand their market presence. Positive profiling can help significantly with this identification process, supporting reduced friction and higher acceptance levels, as well as reduced fraud and chargebacks through highly accurate fraud decisioning.

However, it is important to remember that professional fraudsters work hard to predict the industry’s next move and to circumvent the controls or predictive measures we use to fight fraud. Whether it is rules, machine learning, or more traditional fraud detection measures, each approach has attracted the close attention of fraudsters who have then subverted them.

So, it makes ultimate sense to combine multiple approaches and to include both automated decisioning and the continued review of human experts who can tailor fraud strategies in line with changing market conditions. Positive profiling, as effective as it is, needs to be part of a layered approach to fraud prevention. The technique must work in sync with a full range of other intelligence-driven tools and strategies to ensure merchants can take advantages of opportunities for growth without increased exposure to risk.