In In the News

January 26, 2022

Leaf Global Fintech–(EINPresswire)–Leaf Global Fintech Corporation was recently awarded a Phase II SBIR grant from the National Science Foundation for $855,394. The research and development award is to further Leaf’s work facilitating financial services through the use of blockchain and distributed ledger technology, even for users with limited or no internet access. In Phase I of SBIR, Leaf fully developed the Leaf Wallet product and demonstrated that blockchain can be used to process financial transactions quickly, affordably, and transparently in low-connectivity environments. In research and testing, Leaf processed 6,077 domestic and cross-border transactions with average speeds of 2-5 seconds and costs of $0.00083-0.00086 per transaction, allowing Leaf to provide services to customers at a fraction of the time and cost of traditional money transmitters. Since going live, Leaf has processed nearly 100,000 customer transactions. The broader impact of Leaf’s work has high potential to impact the lives of refugees, migrants, and cross-border traders, in addition to other groups currently carrying cash across borders. Leaf was recently highlighted as a positive, active, stablecoin use case by the CEO of the Stellar Development Foundation in a testimony to the US House Finance Committee on digital asset regulation.

The Phase II award will include further development of the Unstructured Supplementary Service Data (USSD) front-end and blockchain-based platform to store, send, and make transfers without a smartphone or internet connection. This first-of-its-kind system for international transactions in no-bandwidth environments increases speed, reduces exchange and transfer fees, and lowers the risks associated with carrying cash. In addition to expanding Leaf Wallet, other directives in the Phase II award include: 1) creating a globally-accessible, digital, distributed identity protocol for the global verification of identities without needing to expose personally identifiable information, and 2) developing machine learning-based microlending algorithms using offline behaviors to predict loan repayment capabilities.

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