November 12, 2018 | AtoZ Markets
In the D.C. Fintech Week Conference at Georgetown University held last week, J. Christopher Giancarlo, the chairman of the US Commodity Futures Trading Commission (CFTC) gave a speech on the distributed ledger technology (DLT) and its role in automating the regulations for derivative markets.
In his speech, Giancarlo said that DLT-powered Quantitative Regulation could help regulators observe the markets more efficiently with less cost.
If used with the machine learning algorithms, the technology could help identify the segments of markets in case of a high risk or probable vulnerabilities rise.
According to the CFTC chair as well, Quantitative Regulation could “standardize and distribute critical information” to market actors and regulators.
“We can also envision the day where rulebooks are digitized, compliance is increasingly automated or built into business operations through smart contracts, and regulatory reporting is satisfied through real-time DLT networks, the machines here at the CFTC would have the ability to communicate regulatory requirements and consume and analyze the data that comes in through such systems.” Giancarlo said at the conference.
Giancarlo explained that CFTC is considering an active form of regulation, that is capable of responding to real-time challenges the new technologies trigger, emphasizing decentralized markets and disintermediated traditional actors and how they are connected to the challenges mentioned.
“the ability to keep pace with those who attempt to defraud, distort, or manipulate”, Giancarlo confirms, referring to that it may have already started building systems that will automate “derivate market regulations”.
The Technology promises of real-time data reporting
As the machine learning and DLT systems will be capable of digitizing rules and regulations, in addition to consuming, analyzing and processing data in real time, the aforementioned technology would allow the CFTC to analyze data once reported.
It would further allow the regulatory agency to study the impact of certain provisions and how they can be modified to ensure an optimal outcome.
“Rather than rely on static rules and regulations that were put in place without knowing exactly the consequences or results they would drive in the market, we may be able to measure data, real-world outcomes, and success in satisfying regulatory objectives”, explained Giancarlo.