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Regulators Eyeing Tech To Boost Financial Stability

Regulators Eyeing Tech To Boost Financial Stability

Regulators around the globe are eyeing tech to boost financial stability

Enhanced supervisory technology (SupTech) with strong governance and skilled human oversight could well have important benefits for financial regulators around the in efforts to increase economic stability in their nations and around the globe, said a report prepared for the G20.

“SupTech could improve oversight, surveillance and analytical capabilities, and generate real time indicators of risk to support forward looking, judgement based, supervision and policymaking,” regulators told the Financial Stability Board.

Importantly as well, real-time and non-traditional data may allow authorities to be more pro-active in their supervision, FSB said.

As an example of the efficiencies SupTech can provide financial regulation, the authors of the report pointed out the U.S. Securities and Exchange Commission has found

algorithms are five times better than random testing at identifying language in investment adviser regulatory filings that could merit further investigation for potential wrongdoing’

The possible improvement in quality arising from automation of previously manual processes has significant appeal to the overseers, the FSB reported in a study released today.

The use of SupTech innovations and strategies in regulatory reporting for microprudential regulation has grown significantly in the last four years, the authors found.

Artificial intelligence was the most commonly deployed application of SupTech.

National financial regulators surveyed by the FSB lauded SupTech’s potential for gains in the effectiveness and efficiency of oversight and improvements in risk management and compliance.

But the risk of handing too many of their functions to technology vendors and the potential for poor data quality were on the minds of the regulatory leaders responding to a poll and other resources consulted by the FSB’s Financial Innovation Network.

“(They were particularly concerned) over-reliance on methods built on historic data could lead to incorrect inferences about the future,” the study

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