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ESMA Affirms Importance of Data-Driven Strategy for EU Digital Finance Initiative

The European Securities and Markets Authority (ESMA), the EU’s securities markets regulator, has submitted a response to the European Commission’s (EC) consultation on a new digital finance strategy for the European Union (EU), in which it reiterates the crucial importance of a data-driven financial services sector.

Benefits of digitalisation, according to ESMA, would include increased speed, efficiency, convenience and greater economies of scale as well as automated tools that help firms and authorities detect cases of poor conduct; while risk areas include data security, operational incidents, data privacy, pricing, sales practices and the financial exclusion of some individuals. However, the regulator stresses the importance of ensuring a technology-neutral EU financial services regulatory framework that supports innovation.

Cooperation around financial innovation at EU level is key to remove fragmentation in the digital financial services market, emphasised the regulator. ESMA outlines a number of specific initiatives that could support this goal, including the development of Digital Financial Identities, usable and recognised throughout the EU, and based around the existing ISO 17442 global standard of the Legal Entity Identifier (LEI), which the regulator stresses should be “promoted to the maximum extent possible.”

ESMA also notes the importance of promoting a well-regulated data-driven financial sector, and the crucial role of technology vendors in order to do so.

“A challenge for firms, authorities and consumers alike is to build the necessary knowledge and expertise to benefit from digital technologies,” says the regulator. “Key requirements for efficient and easy use of data are data standardisation and harmonisation, security of IT-systems and legal certainty regarding pertinent responsibilities, liabilities and usage permissions.”

Finally, a key area of interest for ESMA  moving forward is the potential for AI-based tools (such as machine learning) to support the authority’s statistics-related activities.

“For publicly available data to be easily usable, they need to be subject to unrestricted access in a timely manner. Data quality issues should be addressed through robust verification mechanisms, and text data need to be in machine-readable format,” it notes.

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