Regulation technology, or regtech, is a relatively new area of innovation for technology companies, because with costs of compliance in regulated industries like financial services on the rise, there is increased demand for technology solutions that can help businesses comply while keeping costs in line.
The demand for digital services, such as online banking, digital payments, online-only financial companies like robo-advisors and marketplace lenders — plus the desire to have “real time” funds movement — add stress to compliance control frameworks and give rise to increased cybersecurity, data privacy and fraud risks.
With the increase in digital data, regulated businesses have no choice but to look for scalable and cost-effective uses of technology to meet this challenge.
There are many solutions out there that “solve” for different processes, but the challenge for regtech is knowing which manual processes actually need a technology layer. Unlike some of my colleagues, I personally do not believe that AI-driven tools can completely eliminate the need for humans in a regulatory compliance process. However, AI’s ability to process large volumes of data with speed and accuracy is extremely valuable to a business’ compliance process.
Transaction monitoring, onboarding, reporting and compliance with new rules and regulations are the areas that benefit the most from regtech solutions. When rule changes are published, they may run to hundreds of pages or more, but applications can synthesize and summarize critical portions of the document, assisting a compliance officer with actionable insights, while saving hours of time of reading and digesting the full document.
Technology to automate the onboarding process for new accounts can rapidly crunch mass amounts of data, enabling real-time decisions, while cutting costs and keeping bad actors from entering their platforms.
Regtech also may play a critical role in the relationship between regulators and industry because it could help regulators communicate regulatory changes to organizations in a more effective way. This involves the use of the “semantic web” or “linked data” to enable computers to support more trusted interactions over the web.
Such tools would allow businesses utilizing deep learning systems and natural language processing to read compliance requirements directly from regulators' websites, providing notice and alerts of any changes. Further, if regulators and industry operators structured more internet data to be “machine-readable,” then regulatory agencies could monitor business compliance indicators on a routine basis, rather than occasionally performing time-consuming and costly review audits.
While the growth of regtech is promising (and necessary to meet future regulatory compliance challenges), it will not be a panacea for all compliance requirements, given the subjectivity and numerous other variables that must be considered in managing the risks unique to each regulated business.
