By T.R Newcomb, VP of Strategy & Corporate Development at Riskified
The past few years have turned shopping trends on their head as consumers and merchants were forced to move largely online. This subsequently led to e-commerce sales soaring and retailers having to rapidly expand and digitally transform their online businesses to meet the new growing demand.
The rapid growth was predominantly set in the pandemic world against the backdrop of many lockdowns across the globe. And despite a slowdown in many e-commerce markets due to inflation and supply chain issues, many experts expect e-commerce shopping to keep rising. For example, Statista forecasts a 50 percent growth worldwide over the next four years in retail e-commerce sales. What’s more, consumers will without doubt be looking for the best deals there are. However, simultaneously with the soaring rise of online sales, online fraud and the risk of it rises too.
The importance of modern-day online fraud prevention and detection
Online fraud has always been an issue and it can take on many forms and anyone can be a victim – from savvy online shoppers to the elderly who perhaps don’t browse online that often. Fraud is a global problem. However, it has evolved rapidly in the past five-to-ten years and fraudulent methods are now evolving faster than teams can adapt.
A recent report found that despite there being thousands of emerging fraud schemes, in the next five years physical goods’ purchases will account for 49% of all online payment fraud losses globally – a 110% increase from current figures. With this in mind, it is more important than ever that e-commerce businesses prioritise their fraud prevention and detection transformations.
The necessity of modern-day solutions could seem remarkably obvious. Yet, you might be surprised to know that many businesses continue to rely on outdated, traditional fraud prevention and methods.
Popular legacy tools that merchants are still using are scoring or rule-based solutions. These solutions look at select data points on orders rather than considering the order holistically. The rule-based solutions force merchants to expand resources to constantly tweak and adjust them since fraud patterns are always evolving. Whether it is entering a new market or selling new products from new categories (for example, expanding from accommodation to flights, or adding digital gift cards) the fraud patterns change. In practice, rule-based solutions are not effective in the long run. They are not flexible enough to keep up with the adaptability of the fraudsters.
Due to the lack of accuracy of rules-based fraud prevention systems, fraudulent orders may be approved, and legitimate orders might be declined. As a result, merchants remain vulnerable despite the measures taken – as do their customers. False declines and long waiting times can turn legitimate buyers away, resulting in high abandonment rates and consequently also a loss in sales. Activities that fall into a grey area must also be released manually. Whilst this can increase accuracy, it is not scalable. As a result, customers have to expect higher waiting times, which can also be reflected in a negative experience. In the worst case, buyers order once and advise others not to shop with the merchant.
Therefore, you are unlikely to be surprised to know that consumers are also not particularly confident in merchants’ ability to combat fraud, either. Too many consumers have been the victim of fraud in the past year and it is wearing down their confidence in merchants. As many as one in five global consumers experienced online shopping fraud in 2021 and up to 40% of them blame the merchant rather than the fraudster.
Whilst we cannot expect to completely eradicate fraud, businesses must look to implement essential safeguards and preventative measures. Such measures will allow customers to carry out safer and faster online transactions, whilst simplifying the transaction process overall. Not to mention, it will save businesses money and boost revenue.
Modern day technologies help where the problem lies
As e-commerce is ever-changing, fraudsters have become much more sophisticated. In fact, they’ve needed to, to try and keep up. For example, we have seen a rise in account takeovers over the past few years, as well as an increase in policy abuse as retailers relaxed their policies. During the pandemic, account takeovers were a golden opportunity for fraudsters to hide among returning customers but we are now witnessing consistent spikes around holiday seasons. For retailers without the right solutions in place, such fraudulent transactions are hard to detect and identify.
So, what is the right solution? In recent years machine learning has become the dominant new technological innovation to detect fraud. It is able to evaluate even the largest amounts of data in real time and to recognise even the smallest nuances. For example, for rules-based system, the location from which a user orders can be the deciding factor in evaluating an order as fraudulent and rejecting it accordingly. On the other hand, modern technologies are more able to recognise factors such as the device used, IP address and password entry behaviour. The different location may be due to the fact that the order is made from the holiday. But it is not fraudulent.
Especially for larger online shops that have to process a large number of orders every day, prevention solutions based on machine learning and that have a chargeback guarantee and a holistic approach at each step of the purchase funnel can make all the difference. Because they only charge for approved orders, they strive to give as many approvals as possible, minimising the risk of legitimate customers being turned away and having negative experiences. If there is a fraud-related chargeback, they are liable for it. Even if fraud can never be completely avoided, retailers have the chance to fend off attempts as far as possible without alienating loyal buyers.
By relying on the right set of tools, businesses can stay on top of fast-changing fraudulent risks. Implementing a modern-day solution will not only protect the business from falling victim to fraud but also work toward providing a more seamless transaction process for their customers.
Fraud prevention refers to the measures and strategies implemented to detect and deter fraudulent activities, particularly in financial transactions and eCommerce.
eCommerce, or electronic commerce, is the buying and selling of goods and services over the internet, which has grown significantly in recent years.
Machine learning is a subset of artificial intelligence that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention.
Traditional fraud prevention methods often include rule-based systems that analyze specific data points but may lack the adaptability needed to combat evolving fraud tactics.
Consumer confidence is the degree of optimism that consumers feel about the overall state of the economy and their personal financial situation, which can be affected by experiences with fraud.
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