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LocalBitcoins - Case Study

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LocalBitcoins is on a mission to promote global financial inclusion, connecting everybody to the Internet Economy through Bitcoin. Operating in a number of countries without easy access to global financial systems, LocalBitcoins is, for many, the only way customers can access digital currencies and global financial systems. Cryptocurrency trading is still a relatively new space, and is growing at an increasingly fast pace. So getting more people onboard and offering them new ways of using bitcoin are key goals for LocalBitcoins. And to stand out from the crowd, LocalBitcoins positions itself as the most secure and user-centric cryptocurrency platform on the market. Get security wrong and customers could lose their assets. Knowing their customers is therefore vital, both in terms of KYC requirements and preventing fraudsters from accessing the platform. LocalBitcoins needed an identity partner that could help them balance these different goals. They had to meet KYC requirements, stop fraud and ensure their sign-up was seamless for new customers. The challenge LocalBitcoins is the world's leading peer- to-peer bitcoin marketplace, based in Finland. They enable users around the world to trade bitcoin quickly, easily and securely through over-the- counter trading of local currency in exchange for bitcoin. 189 allowing LocalBitcoins to have a global reach countries from which customers have onboarded getting customers to experience value sooner to approve a customer for LocalBitcoins 16minutes case study LocalBitcoins chose Onfido as their identity provider to help them onboard customers as part of a tiered fraud prevention and KYC process. LocalBitcoins tier their identity verification based on a customer's activity. For 'Tier One' customers, LocalBitcoins triggers a Document and Selfie check. For 'Tier Three' customers (those trading at much higher volumes) LocalBitcoins triggers an Onfido Video check. And to stop the most sophisticated fraudsters, LocalBitcoins uses Onfido's Known Faces, identifying repeat fraudsters entering their system by flagging if a face has been 'seen' before by Onfido. The solution

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