Technology has changed how business is conducted across all sectors. More than ever, huge amounts of data is gathered to gain insight across functions. This shift is particularly relevant for small and medium-sized businesses (SMBs) in the e-commerce space, which need to take advantage of every tool available to stay competitive.
Despite tremendous progress, technology has still not been able to eliminate payments fraud – a looming problem for the industry, and one that cuts significantly into the profits of SMBs and online retailers. Merchants, along with their customers, can become easy targets throughout the payments lifecycle. And as businesses become increasingly data reliant, they are finding themselves more susceptible to fraudsters, who continue to come up with new and ever more sophisticated methods of attack.
Payment fraud is a unique challenge because perpetrators are not looking to steal money outright. Instead, they use stolen information such as credit card numbers and other personal data to make a host of fraudulent charges. In this scenario there are two victims – the individual whose payment information was used, and the retailer. The difference is that for the individual, they can dispute the charges and generally are not liable for them. Not so for the merchant, who typically loses the product they sold and on top of that, is hit with hefty chargeback fees from their acquiring bank.
According to LexisNexis in its 2018 True Cost of Fraud Report, merchants are saddled with $2.94 for every dollar of fraud, and the number of fraudulent transactions keeps growing every year. Depending on the size of the merchant, fraud costs could be even greater. This expense adds to a myriad of existing challenges of running an SMB. Merchants must juggle fraud prevention with numerous other priorities like sales and customer experience, and all of these factors can significantly damper profits.
So, what can SMB online retailers do to counter this issue as efficiently as possible? Leveraging artificial intelligence (AI) and machine learning-based tools is a good place to start. Companies often employ analysts to sift through data and see if there are any anomalies. But to deeply examine and understand all of it requires precious time and effort that cannot rely on manpower alone. It is unrealistic for people to process all this information, no matter how big the team. Trying to identify patterns at this scale can only be accomplished with the assistance of technology.
With machine learning, a system combs through large volumes of transactions and can accurately predict which of them may be fraudulent, based on its own model of observations. Furthermore, because the system actively learns as it interacts with data, it can continuously adapt and eventually even catch patterns that would otherwise be missed by people. By letting computers take care of this very technical and intricate job, workers can shift their focus to other valuable business tasks, a crucial shift in manpower, especially for SMBs trying to compete against fully-tech driven behemoths.
SMBs and e-commerce retailers will need to evaluate their options. Reducing fraud is difficult, but necessary to provide a secure experience. Once organizations take the proactive approach of incorporating AI and machine learning into their operations strategies, they will truly be able to realize their full growth potential.