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Adjust offers newly acquired Unbotify behavior authentication technology to combat fraud bots

With automated bots causing increasing problems for mobile apps, Adjust has acquired Unbotify to provide a bespoke solution tailored to the individual app to distinguish bots from legitimate human users.

Unbotify research indicates that bots cost mobile apps approximately 10 percent of revenues, as well as causing user frustration and damaging app reputation, according to a blog post by Adjust CTO Paul Müller announcing the launch. The software learns the natural flow of users in the app and builds models of authentic behavior with machine learning based on sensor data from interaction between the device and user. Once post-install bot activity is detected, the app developer can take action to stop it, making the app more user friendly and protecting revenue.

“(I)n-app bot fraud isn’t just a monetary issue — it has knock-on effects on user experience, retention, in-app analytics, and breaches that compromise users’ data and scar a brand’s reputation,” comments Unbotify CEO and co-founder Yaron Oliker. “Our AI and machine-learning technology detects bots in real-time and offers the best possible insurance to protect the user experience.”

Unbotify says that in addition to the effect on gaming apps, a Distil Networks report shows nearly 22 percent of e-commerce traffic is attributable to “bad bots.”

Chargebacks911 co-founder and COO Monica Eaton-Cardone expressed disappointment last year that many companies seem to focus on point-of-sale fraud prevention technologies such as biometrics despite the higher rate of increase in card-not-present (CNP) ecommerce fraud. Juniper Research forecasted in 2017 that retailers could lose $71 billion in fraudulent CNP transactions through the end of 2022.

The standalone product is offered as part of Adjust’s Fraud Prevention Suite, and can be implemented alongside attribution services from Adjust or other vendors. The acquisition was completed in January, and terms were not disclosed.

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