As eCommerce continues to grow exponentially, so does fraud. In fact, consumer credit reporting agency, Experian,reports that there’s been a huge 56% increase in eCommerce breaches since 2016. The prevalence of digital payments and transactions, coupled with the number of consumers gravitating towards apps and mobile connectivity, has encouraged criminals to devise newer, more sophisticated methods of stealing money.

Fortunately, there is a solution. Artificial Intelligence (AI) and machine learning are increasingly alleviating the fears of merchants, PSPs and eCommerce companies who are being plagued by an array of cyberattacks. In fact, these technologies have become critical tools in the fight against fraud as the payments and transactions industry continues to evolve.

Most anti-fraud systems that flag suspicious behaviour (for example, unusual payments to remote suppliers, or credit card purchases that take place outside a customer’s country of residence) are ‘rules-based’.  This means that they detect fraud by measuring transactional activity against several pre-determined rules that humans have created by combining data about previous fraud with intuition about what constitutes ‘normal’ buyer behaviour. Although effective to some degree, this approach can be costly and slow, with high false-positive rates, and no reliable way of identifying new, emerging fraud patterns.

Machine learning, on the other hand, uses self-learning algorithms to integrate and analyse massive amounts of evolving, fast-moving and unstructured data.  These algorithms can detect fraud in real time, learn from trends, automate tedious tasks, and effectively identify new fraud patterns.

While AI and machine learning are important developments in the fight against fraud, the role of humans in securing the omnichannel eCcommerce space should not be underestimated. Machines can identify signs of fraudulent activity, but it’s up to analysts to act on them. This is especially important in today’s omnichannel retail environment, where chargebacks caused by fraudulent activity can have a negative impact on the touchpoints that connect buyers with sellers.

Today’s cyber-criminals know the ins and outs of payment processes and can easily locate vulnerabilities through distributed networks and the dark web.  They then employ multiple sophisticated tactics to exploit these vulnerabilities, including, but not limited to , identity theft, phishing and account takeover. According to Nielsen Report, fraudsters steal about 5.65 cents for every USD 100 spent!

As online fraud continues to evolve, machine learning is proving to be the most effective method that ecommerce eCommerce constituents can use to protect themselves. Robust and user -friendly, it secures vulnerabilities by monitoring real-time customer behaviour, and helps companies with achieve better and more effective decision-making. From identity verification and payment authorizsation, to checkout scoring and merchant underwriting, its applications are limitless. The underlying result is a significant reduction in fraud loss and chargebacks.