The emergence of the Internet in the 1990s and the omnipresence of mobile phones in the 2000s as a pivot for everyday interactions between people and businesses, has led a Cambrian explosion in innovation and resultant business models and technologies. Mountains of data are being generated, ready to be used in still further sets of innovations to the degree that ‘data’ now plays a consequential and pivotal role in the development of innovations in financial ecosystem through as financial technologies (fintech) created by eponymous progenitor financial technology companies (‘fintechs’). Fintech has enabled a multitude of innovations, from lending using alternative credit scores, to digital financial services (DFS), to wealth management.
It has also facilitated the emergence of regulatory technology (‘regtech’) solutions being implemented by regulators and business to ease and ensure compliance as well as act as an early warning for the entities and supervisors alike to events of a financial integrity and systemic nature, such as liquidity crunches and attempts at wholesale money laundering.
The crucible that both fintech and regtech pivot on is this growing mountain of ‘big data’ and new artificial intelligence algorithms augmented by self-learning ‘machine-learning’ systems that are able to process and analyze more data at greater speed, accuracy and efficiencies.
Regtech represents a confluence of these activities, where this data is used by regulators for supervisory – ‘suptech’ – purposes and by supervised entities for their own internal compliance needs in an effective, cost efficient manner.
Both are still at their genesis stage though, with regulators still grappling how to enable fintechs as separate entities, and how available customer data can be used to foster competition and to implement regtech solutions. Similarly, use of artificial intelligence technology to analyze data and undertake predictive analysis on, for example, risk analysis in credit decisions may inadvertently introduce bias in decision making.
This study pieces these disparate – fintech, banking, big data - strands together to identify and analyze regulatory models available for catalyzing fintech, fintechs and regtech, including the potential need for ancillary regulation that would be a touch-point of both regtech and fintech ecosystems to close any potential regulatory gaps and to ensure regulatory certainty in the use of technologies and the surfeit of data powering both fintechs and regtech.
This includes the sourcing use of personal data, cloud computing and data localization/safe harbor rules, sharing of data for anti-money laundering purposes, rules around recognizing data stored on a distributed ledger technology/blockchain as being recognized for evidentiary and other purposes, and use of artificial intelligence and machine learning to analyses in a manner that does not create or perpetuate algorithmic biases and unintended red-lining of classes of people for access to financial services and products.