by Angela Guess
Michael Liberty recently wrote in TheNextWeb, “‘No battleplan ever survives contact with the enemy,’ is one of history’s most telling statements on military strategy. It’s also the philosophy that data scientists, fighting an increasingly sophisticated class of fraudsters, must live by. We’re in the midst of an escalating battle against online fraud. In its True Cost of Fraud Study, LexisNexis found that annual e-commerce fraud costs alone reached $32 billion in 2014, representing a 38 percent increase over the year before. For every dollar of revenue, merchants are losing more to fraud despite spending more money on fraud countermeasures. Simply put, fraudsters are getting better. Accessibility, anonymity, and automation allow them to ply their trade at industrial proportions. Machine learning has emerged as the best tool to fight fraud at scale, and merchants with the right instincts are increasingly turning to it for solutions.”
Liberty goes on, “However, too many merchants are looking to machine learning as a panacea for fraud, and some vendors are irresponsibly fueling that belief, advocating a total replacement of seasoned fraud experts in favor of the machine. The truth is, when machine learning is naively and dogmatically applied, it will not only fall short of its potential, but it’s also likely to perform much worse than traditional fraud prevention techniques.”
He continues, “Machine learning, in simple terms, is the practice of using algorithms that learn a “model” from past data and using it to make predictions on future events. It implicitly assumes that the patterns of the past will be repeated in the future. A common application is purchase recommendations, like those seen on Amazon, which are provided by models that have learned to predict what customers might purchase based on what similar buyers have purchased. The effectiveness of machine learning in this and other contexts, such as web search, provides strong evidence that it can be a valuable tool in the data-rich field of fraud prevention. However, unlike the customers that browse Amazon’s purchase recommendations, fraudsters actively avoid being predictable.”
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