A Retailer’s Guide to Combating Online Fraud this Festive Season

by Ido Lustig, VP of Product, Risk and Identity at Checkout.com

Online commerce has grown to become a critical component of the retail industry: in August 2022, the value of internet sales as a percentage of total retail sales in the UK was 24.2%. As a result, retailers’ strategies are increasingly centred around the growing number of digitally native shoppers. And with the ‘golden quarter’ of festive retail activity upon us, that emphasis will come to be even more important over the coming weeks. 

However, as consumers gear up for Cyber Monday and Black Friday – albeit in a macro context which means we’re likely to see some changes in spending patterns – fraudsters are also circling. Yes, fraud is growing in volume – but perhaps more important is its increasing complexity. Fraudsters are deploying ever more innovative attacks, using tactics that side step measures usually considered sufficient like Strong Customer Authentication (SCA).

This scam evolution is unfolding at a quicker pace than many businesses are adapting their countermeasures. Many existing online payment fraud solutions are too rigid, not tailored enough to counter specific merchant requirements. Aside from the fraud incidents themselves, this leads to inaccuracies in detection, the blocking of legitimate payments and unnecessary friction for consumers that are seeking a seamless checkout experience.

Understanding the evolving fraud landscape

Far from being a fringe threat, ecommerce payment fraud is a major issue for retailers, that if left unchecked could cost merchants in excess of $48bn globally in 2023 and $206bn by 2025.

One of the major vectors is stolen financials fraud which occurs when fraudsters use stolen or hacked credit card information that was used to make legitimate purchases, then file a chargeback with their issuing bank to gain ‘stolen refunds’. A 2021 United Nations report found that up to 86% of chargebacks could be fraudulent. This type of fraud disproportionately impacts merchants, as banks tend to side with the customer, costing the merchant more than the price of the lost commerce as chargeback fees are also charged by the bank to the merchant. Combating stolen financials fraud requires retailers to balance the need to protect shoppers with avoiding unnecessary verification steps or blocking legitimate claims.

On online marketplaces, such as Etsy or eBay, sellers are threatened with targeted attacks from fake accounts and fake buyer/seller closed-loops. They are deploying increasingly sophisticated methods to bypass authentication measures like OTP (one time password), including interception bots which intercept and steal the additional authentication factors. This is a particular problem given how important marketplaces are becoming in the retail landscape: up to 70% of ecommerce is expected to take place on marketplaces by 2025. 

New emerging industries that are pioneering the use of alternative payment methods also present fresh opportunities to fraudsters and pose another threat to consumers and retailers. For instance, given the delayed nature of buy-now-pay-later (BNPL) payments, fraudulent payments can be made in advance before they are identified and retrospective action taken. 

Deploying the right technology

For consumers, this year’s retail season looks set to be dominated by soaring inflation, leading many to be more exacting about where they make their purchases. And for retailers, that means that they cannot afford to compromise on either customer experience or security – Checkout.com research has found that 34% of people have been permanently put off a shopping site by a declined payment. So how can retailers prepare?

One answer is taking a risk-based segmentation approach. In practice, that means separating high-risk versus low-risk payment profiles and applying different rules for each. For example, a returning customer using a familiar IP and payment method can usually be treated with a different anti-fraud strategy than a new customer from an unfamiliar or suspicious IP address. The same logic can be applied to geographies, payment methods or even by product code if certain lines are targeted more than others. The overall effect of this approach will be more fraudulent activity blocked – but less friction for transactions likely to be legitimate. 

On top of this, retailers should adopt a tailored approach to anti-fraud. Applying a rigid solution will still leave holes in any fraud prevention strategy for fraudsters to exploit. Each business has a unique risk profile and as such employing a tailored approach to fraud prevention, combats emerging threats more effectively. Hoping that higher sales volumes outperform the losses from fraudulent attacks isn’t a strategy. Businesses must prioritise fighting fraud and not accept that legitimate customers and transactions will get blocked as fair collateral. 

Another approach is the use of advanced machine learning (ML), which can be trained on existing data points from real fraud incidents – from across multiple geographies and sectors. This leaves it primed to spot potentially fraudulent patterns in real-time, alerting businesses so they can block attacks as they happen, rather than reacting retrospectively. ML benefits from a networked ‘immune response’ too: once one business has been targeted by an attempted fraud, the data points for that attack can be shared so other businesses’ defences can spot the warning signs more easily in future too.  

Conclusion

The festive retail season will be critical for a retail sector facing an uncertain near-term future. As scams evolve at a faster pace than ever, getting anti-fraud right is a critical question for businesses. 

And while this type of investment comes with a cost, businesses should not see strengthening fraud solutions as a sunk cost or a short term strategy only for the retail season. Instead, fraud prevention is a business enabler that will set apart the winners of the retail season through additional brand capital, consumer trust, revenue and growth.