Now is the time for banks and other financial institutions to act. The data is out there – and the organization that learns how to take advantage of it with a wide-ranging AI-based analytical platform will be a winning organization.
With all the AI tools at our disposal – advanced general intelligence, supervised learning, unsupervised learning, neural networks, LLMs, and many more – most organizations that profess to utilize AI – actually only stick to the basics. This begs the question, with all this firepower, how come they are not using more of it?
This muted approach goes for companies in the financial space as well – including the quant houses that apply AI to their investors’ portfolios. They may use machine learning to achieve a very narrow and specific set of operations or goals, but this is a far cry from the AI potential. Not that there’s anything wrong with that; it’s just that by limiting themselves to one technique, one basic “AI move,” they are limiting their possibilities – for profit, forecasts, trend analysis, and all the other things their clients rely on them for.
The reality is that many of even the most AI-savvy investment houses are “one-trick ponies,” limiting their engagement with technology to one tried and true path. For managers and investors, that’s often enough; after all, it makes money for their clients and themselves, and if that objective has been achieved, why go further?
The Value of More Data – More Alpha Potential
But there is reason to go further; clients could be taking advantage of even better investments that will help them achieve their goals – with less risk, more understanding of risk, and with better diversification. Going beyond the basics could help investors – and advisors – avoid future problems, identify nascent opportunities, and build a better and healthier portfolio more resistant to future events that could harm their interests. It makes sense; if some data can achieve good results, it stands to reason that more data can achieve even better results.
For example, advanced AI systems could parse huge databases that include events of the past – political, economic, social, national, and international – and how specific investments were affected. If patterns emerge that show a specific connection between a series of events and an investment result, it could apply that set of circumstances – and the likelihood that those circumstances could emerge – and how it would affect current and future investments. Other factors to consider are those closely connected to the asset and the financial markets, such as quarterly reporting for companies, volatility, trading volumes, and more. At the same time, the AI-based system would analyze the needs of clients – their investment goals, life situation, location, family situation, and more.
New AI Approaches for Investment Houses to Find Alpha
The key to accomplishing this is to develop an infrastructure that adapts to differing contexts and can handle weighing multiple factors. A piece of data needs to be analyzed in the context of possible events, weighed against the effect of perhaps millions of other issues that could affect the context of how a factor will affect investments. And that contextual evaluation has to be done for all the relevant data. The computing power needed to accomplish this is substantial, but the result is what all investment experts should be striving for – a holistic approach that looks at the entire market in context, finding the patterns that can and will most likely result in a predictable outcome. This optimized approach makes investing a science rather than a gamble – and is likely to benefit investment houses and their clients far more than the piecemeal approaches most investment houses utilize.
Such a platform could be deployed to automatically execute trades and other investment moves based on the amount of risk clients want to take. It could also be used to advise clients about the right investments for them. While AI cannot, of course, predict the future specifically, it could determine whether it is likely that certain current events could lead to a situation that would affect an investment – giving investors that opportunity to decide whether they are willing to take on the risk likely to be associated with an investment. And investment houses could use the same platforms for their own holdings – expanding their array of investments, seeking out the best new opportunities, and reducing risk based on advanced analytical assessment.
See also: State of AI in the Financial Services Industry Going into 2024
Partnering for AI Magic – Tech Companies Are Partnering With Investment Houses
The only way to achieve capabilities like this is by utilizing data – lots of it – that encompasses a wide range of the environment investors are operating in, and by giving each factor the right weight and context. Few organizations – even the biggest investment banks – would be capable of deploying a platform that could capture and analyze this amount of data. Thus, investment houses would be likely to partner with AI-based organizations that have a platform specializing in data analysis for this purpose.
Right now, most financial institutions that use AI are using it piecemeal – for specific products, specific clients, and specific objectives. But by failing to look beyond the piecemeal, banks and investment firms are missing out on a whole world of opportunities – and, to some extent, placing their holdings and their clients’ portfolios at greater risk than necessary. They are also running the risk of falling behind the growing number of AI companies and nimble fintechs that are rapidly developing this type of comprehensive and advanced data analysis. If banks and investment houses do not take steps to embrace such technology, their customers may decide to seek it elsewhere.
Now is the time for banks and other financial institutions to act. The data is out there – and the organization that learns how to take advantage of it with a wide-ranging AI-based analytical platform will be a winning organization.
