By Aarti Manwani     

So what is delaying the adoption of AI in industries such as finance and investment management? An exploration of the asset management industry may provide some answers.

Machine learning at its core enables us to take vast amounts of unstructured data, organize it, and translate data into recurring patterns. The patterns are then used to conduct deep analysis and draw insights.

In case of the financial industry, the insights are further used to predict upcoming events, drawing on the predictive and prescriptive nature of AI.

This prescriptive nature of the technology enables the finance industry as a whole to generate insights and investment ideas that would not have otherwise been possible using traditional techniques.

Early efforts to apply AI to investing have proven to be largely successful. A recent report by Eurekahedge noted that their AI/Machine Learning Hedge Fund Index had outperformed their overall Hedge Fund Index by nearly 200bps per annum over the past five years (annualized returns of 7.35 percent vs 5.51 percent).

Institutional Investor Alphas annual list of the highest-earning hedge fund managers also bears this out, as the majority of the managers in the top 10 (such as Bridgewater Associates, Citadel, the D.E. Shaw Group, Millennium Management, etc.) are known to incorporate AI into their investment processes.

Investors have taken note and begun to allocate substantial capital to quantitative/systematic hedge funds. According to the Financial Times, quantitative hedge funds have attracted inflows for eight consecutive years and have doubled their total assets under management to $918 billion since 2009, accounting for 30 percent of all hedge fund assets.

A recent survey by Barclays found that 62 percent of systematic managers are now using machine learning in the investment process. Although, as HFR indicates, hedge funds as a group only manage $3 trillion compared to the $167 trillion and $69 trillion in assets managed by wealth and asset management firms collectively.

So what is restricting the adoption of AI within traditional firms following the success of hedge funds? Most notable issues boil down to a significant financial and human capital investment.

Probably the most common obstacle is the lack of an available talent pool. As a new field, there is a limited talent pool with experience and expertise in the field. Same goes for the data scientists and machine learning experts who are generally needed to translate the insights into actionable business actions and forecasts.

 Paysa reports that there are more than 10,000 open AI positions in the US alone, and IBM further forecasts that the number of related job postings in the US will increase by 364,000 to 2.7 million.

Besides the lack of talent, there is also a need for the investment firms to adjust to the expectations and preferences of this particular talent pool, which includes people from academia, researchers, and PhD students.

A lot of these people do not pursue traditional careers in investing and may not just be driven by money and financial security. This talent pool is in demand and can have their pick.


Instead, investment companies need to create an environment which acknowledges the needs of talent that puts a high premium on creating a positive impact, working on and discovering game-changing technologies.

Garnering support at the executive level is another key component to adopting AI within a firm. Many management teams may view AI as an IT issue that can help reduce costs and the workforce, but not as a technology that impacts the core business, let alone one that can help the firm to grow.

Changing mindsets and entering a new territory and convincing portfolio managers and related investment professionals to incorporate AI into the investment process can prove challenging.

As a final point, AI as a technology uses extremely complex data sets and opaque processes to produce results, which can make it very difficult to rationalize the predictions and investment decisions to the common man.

Making asset owners and managers comfortable with AI being used as part of the investment process is a matter of education and setting several successful examples to learn from. It will take time to build the trust and remove the fear of the unknown; however, it is clear where the future is heading.

Aarti Manwani is the Founder of Good Luck Ventures, a growth-focused product management/development and design company.