Sec lending sees growing emphasis on data collection

There is a growing emphasis on data collection in the securities lending space, according to Matt Wolfe, OCC vice president of business development.

In a blog post, Wolfe explained that decisions are being influenced by data analytics.

Wolfe suggested that the future of securities lending will be data-driven and the leaders will be those that make the most effective use of data.

He noted that strong personal relationships have always been a cornerstone of the securities lending marketplace and while they will always be important, the growing emphasis is towards the data.

According to Wolfe, one of the catalysts driving an increased emphasis upon data is Securities Financing Transactions Regulation (SFTR).

SFTR is forcing market participants to make technology investments to collect data for regulatory reporting. The ability to collect more data and integrate systems across entities can make inventory more accessible, he added that it is all about utilisation.

Wolfe also said that he expects to see two technology changes in the securities lending space that will benefit beneficial owners: machine learning and distributed ledger technology (DLT).

Firms and vendors have been investing in new technologies that could enable participants to apply machine learning in order to discover surprising and valuable insights.

For example, Wolfe explained, programmes that can anticipate changes in the demand for securities enable firms to make more informed decisions about when to lend and rerate securities.

Similarly, he said that DLT has the potential to not only improve the transparency for beneficial owners but also to potentially enable them to take a more active role in their lending programmes.

Wolfe commented: “OCC believes that investing in technology to improve client outcomes will prove to be a wise decision in the securities lending marketplace.”

“We are focused on technology that can give our clearing firms an advantage by improving their capital and operational efficiency, lowering their costs, and raising their utilisation.”

“We believe that DLT has great promise for increasing efficiency, reducing reconciliation costs, and making stock loan programs more profitable.”

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