What are RegTech and SupTech, and how do they work?

Banks and financial institutions are frequently overwhelmed by the sheer volume of laws, rules, and regulations, and regulators’ role has been made more difficult by the faster speed of digitisation, big data, and the complexity of modern financial crimes.

To address both of these issues, Indian regulators, like their global counterparts, are leaning on RegTechs and SupTechs to modernize their operations.

What is regulatory technology (RegTech) and how does it work?

A RegTech helps a bank, credit union, or other financial institution solve problems, optimize operations, and manage risks for regulatory compliance by utilizing technological breakthroughs in data mining, artificial intelligence, blockchain, Machine Learning, automation, and other areas.

Simply defined, a RegTech, or Regulatory Digital, assists a regulator in using technology solutions to guarantee that businesses follow the rules.

RegTech products are available in a wide range of shapes and sizes. Single-rule solutions can focus on a single area, whereas business solutions can provide a real-time overall perspective of compliance and risk.

Unlike FinTechs, which are focused on the “consumer experience” and competition, RegTechs are more concerned with institutional reactions, and their efficiency and efficacy should benefit both shareholders and customers with profit and protection.

In India, popular RegTechs include Enforcd, Reg Room, Elliptic, and Ayasdi.

What exactly is SupTech, and how does it work?

RegTechs can be divided into two categories: technology that assists organisations in meeting regulatory compliance responsibilities and technology used by regulatory authorities to monitor and ensure regulatory compliance by regulated businesses.

The last type of RegTech is known as ‘SupTech,’ which is an abbreviation of the term ‘supervisory technology.’

Regulators get data flows directly from the companies they’re in charge of.

Rather of going out and collecting data, the data is fed into their systems, where it is analysed using machine learning and natural language processing to identify questionable transactions or behaviours.