Centrifuge GmbH

Centrifuge has built an open protocol for the financial supply chain that allows businesses worldwide to connect, and create a verifiable history of shared business transactions and masterdata.

Centrifuge OS is built on a permissionless blockchain and provides an immutable and censorship-resistant single source of truth for all participants of the network.

Centrifuge OS allows its users to exchange financial documents, such as invoices and purchase orders with each other, as well as create digital twins of said documents, in the form of Non-Fungible Tokens (NFTs). The functionality of Centrifuge OS allows using the beneficial credit rating or liquidity of the large corporate to finance the working capital of the smaller enterprises at the beginning of the supply chains (Deep Tier Finance).




F&M TECHNOLOGY S.A.S. (F&M) is a  FinTech Company specialized in electronic invoicing, including invoice creation, acceptance, payments reconciliation, and factoring. The company started operations in 2009 and in 2010 was certified ISO9001 for quality management. F&M is currently the market leader in the Colombian electronic billing market and aims to become one of the leading companies in the secure exchange of electronic documents and related services (financial, logistic, digital, technological), in 2015 was certified ISO27001 for information security management. Companies such as GM, Apple, Cemex, Dell, Pirelli, and TransUnion(TU), among others, have trusted the services of F&M.

F&M is the owner of eBILL, a software recognized by Microsoft and local institutions as one of the most innovative projects in Colombia. In 2016, F&M processed more than 3.6 million electronic invoices, up from 4.6 million in 2016. eBILL is one of the most used portals in Colombia.



Dean Caire

Dean Caire is an experienced credit risk consultant with 16 years of experience working in 45 countries with over 70 financial institutions on credit risk evaluation and modeling. He possesses a deep understanding of financial instruments and markets as well as the strengths and limitations of various data-mining techniques. Using open-source statistical software and machine-learning techniques, he has developed over 100 credit scorecards for different borrower segments in both thin-file and data-rich settings.