How machine learning is driving innovation across the institutional investing landscape

Digital transformation throughout the investing landscape is accelerating at a rapid pace, particularly for institutional investors. Emerging technologies have brought a heady blend of anticipation and skepticism among key industry players, but their disruptive potential is already making a profound impact. 

In particular, the rise of machine learning (ML) and its functionality alongside large-language models (LLMs) is already pioneering brand-new processes within institutional investing and will continue to do so as its disruptive impact is refined. 

These technologies fall under the umbrella of artificial intelligence, and institutions are already seeking to adapt their operations to ensure that integrated AI insights are harnessed. 

While only 29% of systematic investors are using AI today to develop and test investment strategies, more than 75% intend to do so in the future. 

In addition, 64% of investment professionals claim that they’re either pursuing or plan to pursue, skills development in AI and ML. This figure rises to 71% among younger professionals. 

Capturing opportunities in investment management

Machine learning will fundamentally change the way investment strategies are built by managers. This will be down to the capabilities of deep learning in building structured data that was initially sourced and synthesized through ML. 

This technology can help to develop sentiment insights from text sources like earnings calls and SEC filings. They can also conduct advanced social listening and even study satellite imagery for parking lot or crop data to shape actionable investment insights. 

For asset managers, ML will become an essential tool within a matter of years and will blend with human tasks to undertake more comprehensive risk management. 

Fundamentally, the machine learning ecosystem within institutional investing will only strengthen as we continue to grow into an age that’s built on increasingly sophisticated data. With this in mind, AI and ML can unite to fine-tune processes and pave the way for the successful implementation of robot advisors in the landscape. 

There are a great many benefits that machine learning can bring to institutional investors and broker-dealers alike, including: 

• Utilizing open finance data to identify investment opportunities based on consumer spending habits that can help them to reach their own personal financial goals.

• Tapping into unstructured data like satellite data for retail car parking lots to identify fluctuations in brick-and-mortar foot traffic for certain brands. This can also be used to identify supply chain growth across global shipyards that can be converted into investment insights. 

• Conducting social listening to accurately gauge sentiment towards stocks or brands that can help to indicate whether a spike or decline in demand is coming. 

Machine learning and artificial intelligence have the potential to equip institutions with unprecedented insights into market trends long before they emerge on the charts and graphs of traditional analysts, helping to make decisive movements long before an opportunity emerges for the rest of the market.

Capitalizing on ML insights

The value that machine learning brings to institutional investors stems from the technology’s ability to actively guide decision-making processes by interpreting vast quantities of unstructured big data. 

Rather than acting as a replacement for human professionals, ML can help to interpret masses of data in a way that can form a solid foundation for capturing, curating, validating, and quantifying insights that can leverage guidance in a way that’s never before been possible 

While we’ve already touched on the different types of unstructured data that ML insights can help to interpret for Institutions, this is only the tip of the iceberg for machine learning capabilities. 

Crucially for institutions, machine learning can provide next-generation risk assessment tools for professionals that draw on historical data and predictive analytics to identify emerging trends and patterns and prepare firms accordingly. For asset managers, this can help to democratize risk and ensure that client portfolios remain safe even during periods of significant market volatility. 

Embracing the full functionality of ML

Machine learning can also help institutions to automate account compliance for clients around the world and provide powerful analytical insights across operational spending and approval workflows. 

For institutions that operate on a larger scale alongside a network of partners, AP automation can help to drive ML capabilities across vendor payouts and help to ensure that internal operations continue to run smoothly throughout the firm. 

Through advanced automation techniques, machine learning can be utilized by institutions on a more comprehensive scale, helping to work alongside human staff to minimize the risk of error and biases that don’t correlate with the performance of accounts and other processes. 

Welcoming LLMs to the Fray

Over the coming months and years, we’ll see large-language models (LLMs) like ChatGPT offer more value to institutional investors. 

While natural language processing (NLP) is nothing new, LLMs take this to the next level by leveraging a more fluent understanding of human interactions on a conversational level. Crucially, this can add significant levels of context to the collection and interpretation of unstructured data from sales calls, for instance. 

In a fast-paced environment where time quite literally is money, the greater conversational fluency of LLMs means that institutions can leverage strategic insights faster to anticipate trends and act on them in a frictionless way. With the ability of LLMs to even predict the next word in a sentence and produce human-like content, we could soon be looking at a landscape where institutions can act on data as it’s generated. 

Built on a machine learning framework, LLMs are capable of simulating human reasoning in a way that can conform to an institution’s core values and risk appetite. This can help the technology to automatically vet opportunities as they emerge and determine whether any further action is needed before automating the trading process. 

As the LLM taps into ML to learn of what’s acceptable and unacceptable when opportunities emerge, institutions can use these generative AI models to fully automate the process of identifying and leveraging investment opportunities–or semi-automate them at their discretion. 

The next generation of institutional investing

Institutional investing is data-based, and our reliance on big data will only grow as machine learning becomes more fluent in interpreting information for actionable insights. 

The ability of AI algorithms to discover patterns and trends that are invisible to the human eye will be invaluable in uncovering a competitive advantage for institutions and we’re set to see investment in ML grow significantly over the coming years for this reason. 

While this will also lead to an intensification of competition among institutions in discovering and acting on insights, the insights available for professionals to digest will be limited only by the availability of data. This means that the sky really is the limit for the next generation of institutional investing.

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