The rise of generative AI brings with it new approaches to the ways in which investing and management of market risk should be handled. The author of this article considers how data will be used, and explains why AI is a “co-pilot.”
Anush Newman, CEO and co-founder of commercial data solutions
provider JMAN
Group – which has offices in London, New York and
Chennai – explains how the use of generative AI is
redefining the future of investment strategy. The firm works with
private equity organizations and portfolio companies. (JMAN is a
Baird Capital portfolio company.)
The editors are pleased to share these views and invite
readers to respond. The usual editorial disclaimers apply to
views of outside contributors. Email tom.burroughes@wealthbriefing.com
Ever since its arrival, ChatGPT has dominated the business
narrative with its ability to generate deceptively human-like
text capabilities. It can summarize lengthy documents, create
reports and other vital business communications, research the
most complex economic trends and industries, and even code. The
result is a huge opportunity for businesses to automate key
processes, streamline and enhance overall operations, especially
in the current climate. The investment field is no exception.
A new age of investing
Traditionally investment strategies were formulated by combining
human intuition and experience, usually supplemented by a basic
level of market analysis. But with the exponential growth of data
and an increasingly competitive and changeable marketplace, this
approach is reaching its limits. At the same time, the data
skills deficit means that few private equity firms or other types
of asset management companies have the sufficient in-house human
skills to analyze the vast amount of data now available. It is
clear that we require new ways of closing this
intelligence gap. Enter the game-changer: AI.
Together, generative AI and machine learning are accelerating a
new age of data-driven decision-making in investment. It is now
possible to automate the analysis of millions of data points –
market trends, economic data, company financials, academic posts,
news sentiment, and more – to report back on important trends and
insights in minutes.
Generative AI can also deliver data-driven insights that
challenge conventional investment strategies, uncovering
patterns, correlations, and opportunities that human analysts
might otherwise overlook. Through the creation of synthetic data
that replicates actual market prices, economic indicators and
customer behaviors, investors are now able to test their
investment thesis under various conditions and scenarios in order
to optimize their portfolios more effectively. This allows for
investment managers to execute trades with unparalleled accuracy
and efficiency to mitigate risks and provide higher returns.
Also fundamental to this shift is the opportunity to
overcome the fallacies of human emotion in the investment
decision-making process. It is well documented that, whether
aware of it or not, emotional biases can get in the way of
investing, leading to bad decisions and poor returns. By
marrying human judgement with data-driven recommendations backed
by deep factual analysis, investors minimize this risk and make
more informed, rational decisions. The approach not only results
in better decision-making but also fosters a more stable and
rational market environment.
Building the data foundations
The reality though is that unlocking the full potential afforded
by generative AI requires a solid data foundation. Yet PE firms,
like most other organizations, often lack basic data skills
across their teams. Without the ability to fundamentally
understand statistics and ask good questions of data you cannot
begin to effectively use even the most straightforward data
analysis techniques or technology platforms.
Investment and portfolio managers will be unable to apply
data insights safely in their day-to-day working life because
they are unable to assess the accuracy of results and fully
determine their meaning. Consequently, many find themselves
solely reliant on their data experts. This naturally creates
bottlenecks and single points of failure, but it also severely
inhibits a firm from becoming truly data driven.
So what is the best course of action for PE firms seeking to use
AI to enhance their investment strategy? The answer is far from
straightforward. It will depend on the commercial strategy of
individual firms and their portfolio companies. What part of my
existing investment process can be automated, augmented or
improved by using AI or better analytical techniques? This is a
great starting point for identifying high ROI use cases;
choose one and demonstrate value, grow momentum and buy in from
the organization before building a broader infrastructure for
further value capture.
Further down the line it is likely that there will also be a
strong business case for investment in upskilling and retraining
staff across the board.
This should include everyone, including all senior teams. Even
today, it still surprises me how few senior stakeholders are able
to understand and interpret their core business data, instead
relying on a handful of experts. After all, it’s impossible to
know what you don’t know – and a secondhand account of somebody
else’s understanding, no matter how advanced it may be, could
never substitute for your own personal analysis. By building up
your own expertise now, you and your senior team will be able to
ensure that your data-led decisions are the best possible
choices.
AI as a co-pilot, not a replacement
But while AI is a powerful co-pilot, it’s not a replacement for
human expertise. Even as it continues to improve efficiency and
decision-making in the investment sector, AI still has
limitations when dealing with vast unstructured datasets, natural
language understanding, and complex contextual analysis.
And, as with all exciting disruptions, the increased reward is
mirrored by increased risk, from increased vulnerability to
cybersecurity attacks to privacy and ethical concerns. In this
way, the need for human intervention remains paramount for
navigating these complexities and delivering sound
recommendations which align with individual investor goals.
Alongside these risks, PE funds and management teams should be
simultaneously assessing the danger of maintaining a legacy
business model: are your competitors adapting faster than you,
are new players entering the market, are consumer behaviors
changing as a result of easier access to AI tools? These are just
a subset of the questions investors should be asking to de-risk
their investments, but they will also help them leverage AI to
turbo-charge their returns.
Embracing the AI revolution
The integration of AI into investment strategy is far from
another technological upgrade but rather a defining shift in the
financial paradigm.
In the coming years, we can expect generative AI to play an even
more dominant role in investment thesis, from enhancing
predictive analytics, automating trading strategies,
hyper-personalizing investment solutions, improving risk
assessment, delivering real-time sentiment analysis, and
beyond.
At the same time, as AI technology develops it is likely that we
will see even greater focus on the development of
investment-specific tools and applications. This will lead to
more accurate, agile and effective strategies while ultimately
redefining the approach to investment strategy.
With this, the reality is that PE firms cannot afford to lag
behind the AI curve. Of course, there may be extra requirements
needed to meet these new developments, not least reskilling
or upskilling employees, hiring new personnel, and potentially
embarking on structural change. However, in an increasingly
AI-driven future, it will, most certainly, be an investment which
pays dividends.
