Modern electronic payment fraud detection cannot continue to rely on the traditional method of data analysis paired with human participation, which is why financial companies are turning to machine learning and cloud computing to deal with a flood of big data from a multitude of transactions, writes Pennsylvania State University professor Jungwoo Ryoo.
He says a machine-learning fraud detection algorithm requires training by first feeding it the normal payment data of many cardholders, and then running transactions through it--preferably in real time--to yield a probability number. The algorithm weighs numerous variables to qualify a transaction as fraudulent, such as vendor trustworthiness and a cardholder's purchasing behavior, including time, location, and IP addresses. The more information the algorithm has, the greater its accuracy in determining whether a payment is legitimate or not.
Ryoo notes such systems are making heavy human interventions less necessary. Nevertheless, people can still contribute, either when validating a fraud or following up with a rejected payment.
Meanwhile, cloud computing is being deployed to relieve organizations' computing infrastructure from the burden of sifting through vast volumes of transaction data. Cloud computing furnished by off-site computing resources is scalable, and is not restrained by the company's own computational limits.
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