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AI for Financial Institutions: T+1 Compliance and Finding Alpha

Opportunity for strategic upgrade lurks in compliance requirements

Some financial transactions should be moving along a little faster in the United States this summer. At the end of May, the SEC’s new T+1 settlement rule took effect.

As of May 28, all applicable securities transactions from financial institutions in the US are required to settle within one business day, as opposed to the old two-day (T+2) standard implemented back in 2017. The T+1 rule applies to most broker-dealer transactions for stocks, bonds, municipal securities, exchange-traded funds, certain mutual funds, and some limited partnership transactions.

The SEC decision to officially make US “market plumbing more resilient, timely, orderly, and efficient” comes as no surprise. The ruling can be interpreted as a delayed—and inevitable—reaction to a pandemic-driven capability inflection point. When the whole sector went remote pretty much overnight in 2020, institutions were quick to demonstrate their ability to move data around fast on a global scale, even when conditions weren’t exactly optimal. Markets have also been moving at an increasingly accelerated pace for nearly half a decade, so the SEC is just standardizing the obvious: nobody really needs two days to reconcile anymore.

Also Read: Fintech, AI And What ‘Inspires Inclusion’

The T+1 compliance deadline was officially on the radar  for over a full year, which means US financial institutions should have long since amended their settlement pipelines to meet the new requirements. Canada and Mexico also moved to T+1 in May; India was already operating at T+1; and the UK and EU are expected to join the speedy-settlement-cycle party in the near future.

But T+1 won’t be the new normal for long, and firms shouldn’t expect to wait another seven years for the next compliance nudge. China is already at T+0 (same-day settlement) for stock, and straight-through processing (STP) is where every market is heading — fast.

According to the Corporate Finance Institute, STP involves automating “all operations related to trade” to function without manual intervention, which streamlines and speeds transactions enormously. The only hitch is that STP requires seamless digital communication protocols, concurrent data exchange infrastructure, and real-time processing capability. It’s all about the data. Coincidentally, these are some of the same requirements for effective integration of hot new technologies such as Generative AI, which can supercharge STP implementations. However, many financial institutions just don’t have all their systems and data processes prepared for this leap.

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My firm recently released research surveying 500 data leaders from large organizations across major industries (with respondents holding director-level or higher positions in IT and analytics roles at US companies with 1,000 or more employees). A whopping 95% of all surveyed organizations say Generative AI will be critically important to their success by 2027, and 85% expect to have implemented Generative AI tools by the end of 2024. But among respondents in the Financial Services and Insurance (FSI) sector, only 26% report that they had defined Generative AI use cases ready for implementation. A substantial 74% of FSI organizations plan to address their aging data architecture by modernizing in 2024, but only 30% have already modernized their data stack and centralized on a primary cloud data platform.

Smart firms will be using new regulatory requirements as an incentivizing force to seriously upgrade their data architecture, passing through compliance en route to greater profitability. Don’t get me wrong. The SEC’s new policy should reduce risk and increase market liquidity, but nobody is going to make money off of T+1 compliance.

However, those who anticipate what’s coming next and assess what they want to achieve can view the regulation as an opportunity to upgrade and stack those other use cases on top of compliance requirements. If you modernize your systems now, for example, to enable straight-through trade processing, you’re also setting up complete trade flow visibility. That means you can actually start to understand the full lineage of trades: where they happen, where they’re getting stuck in the process, and what the blockers are. You can enable your front office, mid office, and back office to start working in tandem—for real.

That fundamental “flow experience” improvement is the brass ring. If you enable STP,  you also enable the ability to genuinely leverage AI to create tools for better portfolio construction, signal generation, algorithmic trading, more robust back-end stress testing, research for quants, and so on. All of it depends on the clean, integrated flow of data that T+1 and other upcoming technology-based compliance standards are demanding anyway.

Also Read: FinTech Transformation in 2024: Leveraging AI in Embedded Finance

The end product is that, because your “forced” data architecture upgrade works better, you can actually capture AI’s power and you’ve enabled your firm to build solutions that you only ever dreamed of beforehand. Because you used T+1 compliance as a justification to build out STP, now you’re not just finding alpha, you’re creating it.

So if you’re playing your cards right in upgrading data infrastructure and architecture to support new regulatory standards, you also get the capabilities to go make more money. And the beauty of it all is that risk and compliance just paid for your new toys.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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