A commercial spin-out from the University of Cambridge spinout, Regulatory Genome Development Ltd (RegGenome), has just announced that it closed a $6 million seed funding round.
The investment round was led by Evolution Equity Partners with participation from AlbionVC, Cambridge Enterprise, and Mastercard. Besides the investment, Richard Seewald, Managing Partner of Evolution Equity Partners joined RegGenome’s board of directors.
RegGenome will use the proceeds of this funding round to accelerate the development of transformative regulatory data structures.
“We are thrilled to be working with a group of investors that share our view that the world is rapidly entering into a period of regulatory uncertainty, requiring interoperable content to power the next generation of regulatory applications for the digital economy,” commented Management Practice Professor Robert Wardrop, Executive Chairman of RegGenome.
Richard Seewald, Founder & Managing Partner at Evolution Equity Partners, stated, “We are excited to be partnering with Regulatory Genome and the tremendous team it has in place. Deepening regulatory intelligence and digitizing compliance and risk management functions utilizing machine learning and interoperability with cybersecurity, fintech and ESG presents tremendous opportunities for the company. I look forward to working with Robert Wardrop and the RegGenome team.”
“RegGenome is a superb example of how the University of Cambridge’s transformational intellectual property can be applied for global impact. RegGenome is primed to deliver a quantum leap in how regulatory content is shared and harnessed. Cambridge Enterprise has been an integral part of RegGenome from the outset, from enabling access to the required technology to investing in its funding. We are looking forward to seeing RegGenome’s vision come to life,” said Dr Marcio Siqueira, Head of Physical Sciences, Cambridge Enterprise.
Uses AI-based textual extraction
RegGenome works with the vision to transform the way the world consumes regulatory information. The company provides structured machine-readable regulatory content that is dynamic, granular, and interoperable. This is possible with the use of AI-based textual information extraction techniques.
It enables regulatory authorities to increase accessibility and dissemination of regulatory information and empowers organisations to deepen their regulatory intelligence and digitise their compliance and risk management processes.
RegGenome is building a network of application providers, regulators and financial firms that allows the commercial and regulatory worlds to provide feedback to each other without any preferential treatment. This accelerates regulatory standardisation to improve the use and digestibility of content and provide more powerful capabilities.
What is Regulatory Genome Project?
The response to technological and socio-political change, financial regulation has become increasingly complex. The absence of a common approach to comparing different regulatory regimes creates a challenge for both regulators and regulated firms.
The Regulatory Genome Project (RGP) founded by the RegGenome together with Cambridge Judge Business School has been established in response to this gap. The RGP’s primary mission is to develop and support the adoption of the Cambridge Regulatory Genome (CRG), an open information structure that enables regulatory obligations to be organised and compared across jurisdictions.
How does RegGenome work?
RegGenome structures regulation into a powerful code. The RegGenome content services are not bundled with any regulatory applications and are vendor neutral. It enables the Application Provider (RegTech) ecosystem by providing content that is interoperable by design, thereby making it easier for institutions to integrate best-in-class applications and mitigate vendor lock-in.
Eventually, firms no longer need to develop or subscribe to multiple, proprietary, vertical applications. They can just plug into a single source of content and steer in any direction with interoperability between systems.