Internet fraud is a menace in our various financial institutes, and many fintech companies have been victims of this fraud game. Detection of these attacks comes in two ways: through inconsistent traditional methods or using ever-growing artificial intelligence mechanisms. Traditional methods, such as the rule-based method, are still widely used by most fintech companies in contrast to AI. At the same time, some are adjusting to leverage machine learning and artificial intelligence, improving ways to detect fraud. How have AI and machine learning improved fraud detection in the fintech industry?

Identity theft is often a multi-layered process. Once a thief gets one bit of your information, they try to use it to get more. The hackers behind the 2015 data breach of the US Internal Revenue Service (IRS), for example, used personal information they'd previously stolen from thousands of Americans to answer security questions on the IRS website, and in turn get access to their tax returns. The security questions asked about personal details, like, "On which of the following streets have you lived?" and, "What is your total scheduled monthly mortgage payment?" The hackers in the IRS case successfully got through that security measure, but what if the agency had a system in place that could detect whether the person answering the questions really was who they claimed to be?