The Office of the Comptroller of the Currency (OCC):
We are exploring opportunities to create a regulatory sandbox, an environment in which companies can test new products and business models without worrying about the regulatory consequences.
The U.S. Securities and Exchange Commission:
At this point in our risk assessment programs, the power of machine learning is clearly evident. We have utilized both machine learning and big data technologies to extract actionable insights from our massive datasets
CFTC:
The purpose of LabCFTC is twofold: The first is to provide greater regulatory certainty that encourages market-enhancing fintech innovation to improve the quality, resiliency, and competitiveness of our markets. The second is to identify and utilize emerging technologies that can enable the CFTC to carry out its mission more effectively and efficiently in the new digital world.
FCA:
We are looking at the extent to which we can make parts of our handbook initially machine-readable and then fully machine-executable. Effectively converting, probably initially our regulatory reporting rules, into truly unambiguous rules that machines can interpret and implement directly.
ASIC:
There this rapid shift of the financial landscape will end up is not yet clear, but for regulators, I can confidently say that remaining open, engaged and globally connected will mean the best chance for this technology to bring benefits to all participants in the financial sector.
ESMA:
The end goal of a data-driven supervisory process is to shift from a retrospective to a forward-looking approach. The fast-growing amount of data reported to supervisors combined with technological improvements in fields such as artificial intelligence and machine learning allows for the potential of a much more automated supervisory process.
BaFIN:
The end goal of a data-driven supervisory process is to shift from a retrospective to a forward-looking approach. The fast-growing amount of data reported to supervisors combined with technological improvements in fields such as artificial intelligence and machine learning allows for the potential of a much more automated supervisory process.
SFC:
RegTech intersects with SFC’s remit in two ways. First, it can be used to achieve greater compliance and risk control by those we regulate, including both traditional intermediaries and fintech players. Second, we can use some of these technologies to supplement our own risk monitoring, surveillance and supervisory capabilities.
ASIC:
The reason why we’re so interested in RegTech is that we see it has the potential to revive better outcomes for consumers. For us it seems that this is a win for us as a regulator, for financial institutions to do their job more effectively and efficiently, and for consumers because it should deliver better outcomes in the end.
