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Geolocation against fraud

With increasing phone theft and mobile-banking fraud, Revolut and Monzo turn to geolocation: they let customers lock operations to trusted zones — requiring strong authentication when transfers happen outside these areas. A smart step forward in fight against fraud.

Original text here from Patrice Bernard (LinkedIn)

The use of geolocation to fight fraud — in particular within mobile banking — belongs to those old but under-exploited ideas. Re-interpreting in its own way an option introduced last year by Monzo, Revolut has unveiled a protection system dedicated to the increasingly frequent cases of smartphone theft.

Both approaches are based on the same premise: consumers tend to initiate their transfers from home, or at least from a few habitual locations (which might also include their workplace or a second residence, for example). With that in mind, both banks invite their customers to register these geographical zones in their banking app — thereby restricting operations executed outside of them.

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However, while Monzo made this an absolute “lockdown” key, Revolut opts to add additional constraints for transactions initiated outside the allowed zones — and only when the transfer exceeds a user-specified threshold. More precisely, strong authentication (e.g. biometrics) is then systematically required, and the transfer is put on hold for an hour — buying time to react in case of incident.

The feature, called “Street Mode”, directly targets a specific category of fraud that has surged, especially in the United Kingdom, where Revolut is prioritizing its deployment (also planned for continental Europe). Smartphone thefts now frequently involve coercion-based unlocking: aggressors seize the device and force a victim to unlock banking apps, then execute fraudulent operations to enrich themselves.

Overall, Revolut’s initiative seems well-intentioned and appears adapted to a real threat. Yet it raises a question: are we complicating too much the controls required to manage our money safely? The number of security parameters users must master to protect their funds may be growing toward an unwieldy complexity — perhaps to the point of being counter-productive.

Would it not be possible to imagine invisible security solutions, learning from each user’s behavior patterns over time — offering protection without so many manual settings to configure? That may be the next frontier.

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