The Deception Detector feature has been rolled out by Bumble. Check details. (Unsplash)News 

Bumble introduces an AI-driven Deception Detector feature to identify and eliminate fraudulent profiles and scams.

Bumble, the popular online dating platform, has introduced a new AI-driven feature to enhance user experience by detecting fraudulent profiles, spam, and scams. This women-centric app aims to offer its users a higher level of security and convenience. With the implementation of this advanced technology, Bumble strives to create a safer and more enjoyable dating environment.

Fraud detector on Bumble

The new AI-powered feature is called Deception Detector. Its purpose is to weed out all fake profiles and scams on the online dating platform before users see them. When testing this AI tool, Deception Detector managed to block 95 percent of fake and spam profiles. It also resulted in a 45% reduction in the number of fake and spam accounts.

Daters are worried about fake profiles and scams on the platform, according to Bumble research. About 46 percent of the women who responded to the survey said they were concerned about the authenticity of matches on online dating sites.

Announcing this feature, Bumble CEO Lidiane Jones said: “In recent years, the online environment has evolved significantly and we see a growing concern about authenticity. We focus on women’s online experience, so we understand that in the age of AI, trust is more important than ever. We are considering how best to leverage new models to reduce the anxiety of connecting and to support our community, with AI as the main focus.

Previous AI features

Mashable reports that this isn’t the first time Bumble has taken advantage of artificial intelligence to make its platform a safer place. In 2019, it released a feature called Private Detector. This feature will automatically censor a potential nudity, allowing users to choose whether they want to see it. Rejecting it will result in the removal of the image from the chat and the possibility of blocking the user.

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