About
Online fraud accounts for a large part of the $600-billion toll cybercriminal activities take on the global economy every year. detecting and flagging fraudulent transactions revolved around determining static rules such as geolocations, IP addresses, the difference between billing and shipping addresses, the amount of purchase, and the type of items. AI-based fraud mitigation technologies take many more data points into account. These data points can give insight into not only the probability that a transaction is fraudulent, but can also give an indication that a user account may have been compromised for use in fraud—or that a new ‘fake’ account was created specifically to carry out fraud.
Analysis of the additional data points can help lower the frequency of false positive alerts for legitimate transactions and increase the rate of legitimate alerts for suspicious activity. fraud, such as account takeover (ATO), where a bad actor tries to gain access to another person’s online account. looks at various session, device, and behavioral biometrics and builds a profile for what constitutes “normal” user login behavior; if an anomaly is spotted, it can act to prevent the action.
An AI system that makes a judgment about a customer needs to be able to explain itself. AI system could one day analyze large datasets and provide an employee with a specific judgment about whether the charge was indeed fraudulent, based on the customer’s past purchase behaviors and other data.
