As AI ripples through virtually every aspect of commerce, it’s remaking the landscape of nearly every industry. The financial industry is one sector poised to see seismic shifts from the disruptive forces of artificial intelligence.

As banks, brokerage houses, and fund managers further understand the intricacies of AI and the advantages it could bring to their operations, it’s becoming clear that the pace of financial markets could become more rapid than ever thanks to the competitive edge that automation could provide—as well as new benefits for security and liquidity.

One thing is certain: Wall Street firms that have long relied on legacy technology are seriously looking at AI. “Wall Street firms have been suffering from antiquated, old market infrastructure,” says Gabino M. Roche, Jr., founder and CEO of Saphyre, an AI-driven fintech platform that automates and expedites institutional trading processes for firms. “They’ve been hesitant to embrace new technologies due to the fact that many of the largest firms, with the biggest footprint of trades, have many internal, old systems from acquired institutions held together by duct tape.”

But as the interest in AI-enabled processes intensifies, a new wave of innovation is taking place across Wall Street and beyond. Amidst this backdrop, the U.S. Securities and Exchange Commission is introducing a seismic shift of its own in 2024: the standardized settlement period within which financial trades must settle is being reduced from two days (often called T+2) to just one day (T+1). AI stands to play an instrumental role in making this monumental shift possible without breaking down systems.

Here are what some of those changes may look like.

AI Could Cut Institutional Trade Settlement Times in Half

The T+1 settlement cycle is set to replace the T+2 infrastructure that firms currently abide by, and institutional investment firms that have incorporated AI into their T+1 plans rely on that technology as an important piece of their roadmap for meeting the deadline.

Post-trade support teams encounter several issues that AI can address. It can remember and keep track of account and tax IDs, along with all other data pertinent to clients and companies, thus reducing the time support teams must spend deciphering codes and abbreviations standard to the Society for Worldwide Interbank Financial Telecommunication (SWIFT) network.

AI can help streamline Standing Settlement Instructions (SSIs), cited by most financial firms as a pain point in settling transactions, by “memorizing” the relationships between multiple parties and updating an SSI instantly whenever it changes.

Within the institutional framework, an AI model can also send real-time push notifications when it detects potential compliance and risk issues and analyze which parties are appropriate to escalate trade exceptions.

The transition to T+1 is playing a very real role in accelerating the adoption of AI technology out of necessity. As Roche says, “T+1 is forcing the band-aids to be ripped off, and be a real, tangible catalyst for a more holistic and innovative market infrastructure where institutions and retail customers will benefit, and even more competition will thrive as a result.”

AI Fuels Algorithmic Investing for Quicker, More Efficient Transactions

While algorithmic trading isn’t new—stock exchanges started using computerized trading nearly half a century ago—AI is supercharging the practice with advanced capabilities, empowering institutional investment firms to save time and broaden their business missions. While banks have not yet adopted AI in their algorithmic trading strategies, it’s very likely that as the competitive advantages become apparent, innovation and adoption will take place.

“I strongly believe banks will eventually embrace generative AI, once they resolve concerns they have with it,” said Pawan Jain from Fortune. “The potential gains are too significant to pass up—and there’s a risk of being left behind by rivals.”

Predictive analytics, for instance, helps institutional investors digest massive amounts of data across several metrics, providing better clarity on market trends and refining asset allocation strategies. Similarly, machine learning algorithms increase adaptability, allowing institutional investors to react quickly to shifting market conditions.

AI algorithms cover the entire spectrum of institutional investment practices. Risk management, portfolio maximization, asset allocation, troubleshooting, regulatory compliance, and expense reduction are all elements of trading that algorithms can address.

AI Strengthens Efforts to Enhance Cybersecurity and Detect Fraud

The immense amount of information that AI synthesizes helps institutional investment firms boost their system security and combat financial fraud. With improved data analysis and pattern recognition, AI gives institutional investors a hand in staying safe from threats and criminal acts.

Anomaly detection indicates unusual behaviors and suspicious patterns that could threaten regular network activity. AI can also detect and prevent intrusions into institutional investment frameworks, including malware and phishing, helping firms improve their response times to emergency incidents and identity management issues.

Those same tools can root out fraudulent activity at the institutional level. AI constantly monitors transactions for activities like spoofing, pump-and-dumps, wash trading, insider trading, money laundering, and identity theft.

AI Increases Liquidity in Financial Markets

Liquidity and cash flow are especially important to institutional investors. AI has played a crucial part in increasing both by refining strategies, elevating trading efficiency, and giving investors more flexibility.

High-frequency trading algorithms complete astronomical numbers of transactions in record time, allowing institutions to take advantage of fractional price variances for profit. They make real-time adjustments to capitalize on market conditions and set bid and ask prices.

AI also enhances asset allocation, data analysis, risk models, and smart order routing to execute transactions faster. Automated solutions can increase liquidity, allowing institutional investors access to more capital and bigger profits.

AI Reduces Operating Costs and Expenses

In automating manual routine tasks, streamlining complex trade activities, and shoring up compliance standards, AI is helping firms save money on expenses across the board.

AI solutions have allowed companies to automate risk management and client reports, and robo-advisors help brokerage firms empower account holders’ transaction strategies, finding undervalued assets and recommending sell-offs to improve bottom lines.

Automating everyday tasks also reveals redundancies and inefficiencies that plague employee pools, and by doing so, employees are free to take on bigger responsibilities that manually executed routines take away from them. AI can do it all at a fraction of the cost of hiring additional personnel.

Institutional Investors Won’t Look Back

AI’s inflection point in financial markets has already happened. Algorithmic elements are integral to investment strategies, and AI refines them daily. Aggressive companies who anticipated the waves are already realizing improvements in their operations and profits, so it may be crunch time for fence-sitting institutional investors to keep up by integrating AI into their businesses.