ElectronX
Building a CFTC-regulated electricity derivatives exchange
Traditionally, lenders evaluate loan applications based on the borrower’s credit score, which can give an unfair picture of their creditworthiness. Theorem developed a new method that evaluates default risk based on machine-learning analysis of millions of historical loan records. Using Theorem’s risk scores, loan originators can approve loans that would otherwise be declined and expand their customer bases.
Building a CFTC-regulated electricity derivatives exchange
Making electronic trading more efficient for institutional investors and traders
Payment and commerce tools for businesses in Africa and beyond
Building privacy-preserving zero-knowledge infrastructure for blockchains