Machine Readable Regulation
By Kyle Hauptfleisch (@kylefrankwhite)
One of the more substantial barriers to financial innovation is regulation. Although many are quick to state that this is a self-imposed barrier–it is–there are fairly good reasons for this. Finance is complex and pervasive. It is at the heart of economies, businesses and consumer’s day-to-day activities. Almost all aspects of modern life depend on finance in some way, and when something is that ubiquitous, it’s risk level can only rise.
Risk leads to regulation. This problem gets worse as technology progresses, in stark contrast to the problem-solving reputation of disruption. Technology gives rise to new markets which are usually difficult to fit into the frameworks of existing policy. And given that it is far more difficult to change regulation than it is to create it, the complexity of compliance increases over time.
Machine Readable Regulation is one way to combat this issue. It involves codifying regulation so that technical systems can manage compliance rather than financial businesses having to rely on lawyers, compliance officers and consultants to interpret them. Imagine a world where an engineer that has never read a policy paper, act or bill can ensure the efficacy of that businesses’ compliance in its entirety? Suddenly, barriers to financial innovation begin to rapidly fall.
The issue, however, is that regulation is moving from “rules-based” to “principal-based”. Rules-based regulation can be unnecessarily restrictive because it cannot account for exceptions and edge cases. Principal-based regulation, on the other hand, can assess a given activity based on the underlying intent–the reason it’s occurring in the first place–and improve the efficiency and effectiveness of governance in general.
So how do we get the best of both worlds? Artificial Intelligence. AI is the bridge between codified and principal-based regulation; the bridge between restriction and progression.
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One of the more substantial barriers to financial innovation is regulation. Kyle Hauptfleisch discusses how AI coupled with machine readable regulation could overcome this barrier.