Last year might end up being remembered as the year that mobile commerce came of age with mobile devices becoming a suitable way to shop online. According to IBM, mobile traffic during the 2013 holiday season made up 39.7 percent of all online traffic, an increase of 34 percent from the prior year, while mobile sales made up 21 percent of overall online sales, a 43 percent improvement from 2012.*
While mobile shopping is an increased convenience for customers, it can lead to greater fraud risk for online retailers as the proliferation of mobile devices can also make it easier for fraudsters to commit crimes. Fraudsters target online retailers because some have access to customer credit card information that fraudsters seek; mobile consumers sometimes store this information in online retail accounts, making it easier for fraudsters to steal.
As a result of this mobile device threat, online retailers are faced with the task of using advanced methods to minimize risk without hindering the customer experience. While some companies have focused on developing rules-based tools to fight fraud, it is only one piece to solving the fraud puzzle. In order to more effectively fight fraud, online retailers must look for other ways to authenticate consumers.
An approach to authenticating consumers is to combine device authentication with the relevant personal identifiable information (name, phone number, IP address, email). Applying predictive analytics to this information could enable merchants to generate a greater level of insight into each customer. Device data can be collected during the online experience without the consumer having to enter information. Personally identifiable information can be gathered through the checkout process. Armed with these two approaches, the merchant can create a safer, well-lit, friction-free online store. With the holistic approach at the point of a transaction, online retailers possess comprehensive information for each transaction and can receive real-time insights into transaction risk.
What can an online merchant do with these insights? It can take three courses of action: 1) if it is considered low risk, it can automatically clear the transaction; 2) if it is considered somewhat risky, the merchant can flag a transaction for a manual review; or 3) if it is considered high risk, it can either manually review, contact the customer, or simply shut down the transaction.
With these predictive risk assessments, online retailers have more accurate, real-time information on transactions to help them more effectively determine fraud risk. They can also able to maximize sales conversion by reducing false positives generated by standard models. Best of all, this can all be accomplished while providing a mobile customer experience with less friction.
Aaron Kline is the director of eCommerce at ID Analytics, a leader in consumer risk management with patented analytics, proven expertise and real-time insight into consumer behavior.
* IBM 2013 Holiday Benchmark Reports: http://www-01.ibm.com/software/marketing-solutions/benchmark-reports/black-friday-2013.html