Using AI to Prevent Ecommerce Fraud and Secure Payments

As e-commerce continues to grow exponentially, fraud within these channels is also on the rise. In fact, credit reporting agency Experian reports that there’s been a whopping 56% increase in ecommerce breaches since 2016. The prevalence of digital payments and transactions, coupled with the number of consumers gravitating to apps and mobile connectivity, has led to criminals devising 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 cyber attacks. In fact, these technologies have become critical tools in the fight against fraud in the continuously evolving payments and transactions industry.

Most anti-fraud systems that flag suspicious behavior (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 behavior. Although effective to some degree, this approach can be costly and slow, with high false positive rates and no way to identify new, emerging fraud patterns.

Machine learning, on the other hand, uses self-learning algorithms to integrate and analyze 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 ecommerce 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 constituents can use to protect themselves. Robust and user-friendly, it secures vulnerabilities by monitoring real-time customer behavior, and helps companies with better and more effective decision-making. From identity verification and payment authorization to checkout scoring and merchant underwriting, its applications are limitless. The underlining result is a significant reduction in fraud loss and chargebacks.

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About Fiona Brown

Fiona Brown, SVP of Commercial Risk and Underwriting at Credorax, has operated within the field of Risk Management for in excess of 20 years. She has always worked within Payments and has experience both within Acquiring and PSPs having held senior roles at both First Data Merchant Services and Pay360 (formerly PayPoint Online). She joined Credorax in April 2017 where she assumed responsibility for the Fraud, Risk, Legal, Underwriting and Compliance teams to ensure that Fraud and Chargeback levels are effectively managed and, importantly, ensuring that our clients have access to any support they need with regards to managing Fraud.
Connect with her: LinkedIn