The Project is led by the Cambridge Centre for Alternative Finance (CCAF) in collaboration with The Natural Language and Information Processing (NLIP) Research Group and the Cambridge Centre for Data-Driven Discovery (C2D3) at the University of Cambridge and aims to map the structure, combination and evolution of regulatory obligations across industries. Once advanced, it will pave the way to regulation that is truly machine- and human- readable: a needed capability for the transfer of value in a digital economy and a core objective of the emergent RegTech industry.
Compliance can be hugely complex, but regulators’ toolkits are not. Regulators match a finite set of process and control components to a finite set of desired outcomes to create obligations. They benchmark their regulatory frameworks against others’, copying what they find useful. Obligations that work, and fit local conditions, tend to survive and even propagate internationally.
Starting with financial services, the RGP will leverage the insights of industry experts and regulators to build a complete matrix of obligations; map a global feed of regulatory content against these; and offer this rich content feed back to the world as open data.