Monica Eaton, Chargebacks911 CEO and one of the world’s leading experts in fraud and chargeback prevention, was asked to share her thoughts in a new feature for Electronic Payments International.
Electronic Payments International is one of a network of 30+ proprietary B2B websites, with an unrivalled global audience of active decision makers, influencers, and opinion leaders across the world with a combined readership of 55 million industry professionals each year. Their expertise spans more than 80 markets globally and driven by 800+ award-winning journalists, researchers, and analysts.
As is noted in the post, new technologies like artificial intelligence could make it significantly easier for bad actors to commit fraud. Do security professionals have the insights and resources to overcome the advanced AI tools that power this fraud?
“The same technology that allows companies to prevent chargebacks could also be turned to automate creating false chargebacks that are far better ‘quality’ than those that can be produced by amateurs,” Monica says in the piece. “Doing so would allow bad actors to work at far larger scales, with chargeback claims that have a higher chance of not being detected.”
Ultimately, the question boils down to one of intent. When machine learning systems are developed, what are they intended to do?
“The machine-learning algorithms used by Chargebacks911 and other anti-fraud companies aren’t designed to mimic humans. They are built to extract certain information from a dataset. They do not make ‘decisions’ with this information, but rather follow decision-trees based on parameters set by its overseers. These systems can be very sophisticated, up to the point of being able to improve themselves. But they are not ‘intelligence’ in any real sense, and perhaps this is for the best.”
