Internationally, fraud costs the global economy trillions of dollars per year. Unsurprisingly, financial companies are highly invested in catching fraudulent activity detected from unusual consumer behavior to lower costs due to fraud and provide a more secure experience for their customers.
Fraudulent transactions are caught by using behavioral machine learning algorithms to establish normal behavior so that when an unusual transaction occurs it can be flagged as possible fraud. Often, financial companies will reach out to customers when possible fraudulent activity occurred to verify if the transaction was actually fraudulent.
An example of unusual behavior that may indicate fraud is if a consumer buys coffee in Los Angeles and then 20 minutes later buys a donut in London. It would be impossible to travel that fast to make both purchases. Another example is if a consumer makes an expensive purchase that they have never made before in a location they have never been. For instance, if a consumer’s financial credentials are used to purchase fifty mattresses in Brazil when they live in Canada.
Behavioral Analytics Case Study - Financial Services Organization