Charting the Growing Role of Data Science and Artificial Intelligence in Financial Decision Making

Data ScientistAll around the world, the most successful and dominant organizations are implementing advanced data analysis driven by machine learning to mitigate risks and capture new opportunities. For financial service providers and asset management teams, leveraging the full potential of data is quickly shifting from a leading-edge opportunity to a core capability and key value driver. 

In an uncertain world with continuously rising costs, there are real strategic advantages to be gained from adopting data-driven decision-making to remove uncertainty and develop more agile and responsive approaches to maximizing investment performance. Top asset managers overwhelmingly see the benefit of adopting data science and artificial intelligence to drive more effective and responsive financial decision-making.


Harnessing the Full Potential of Data in the Hunt for Alpha 

The business case for digital transformation and the use of advanced investment data science is compelling and in no way lost on the leading minds in the asset management industry. Recently, Northern Trust, a Chicago-based firm responsible for over $1.5 trillion in assets under management, published a white paper titled The Art of Alpha: It’s All About Investment Data Science outlining the findings of a global survey of over 300 asset managers.

This survey contains a wealth of insights about the current data analysis capabilities and future aspirations of leading asset managers when it comes to harnessing the full potential of structured and unstructured data streams:

  • Over 98% of the CEOs, Chief Investment Officers, Chief Data Information Officers, and other executives queried by this survey have already implemented advanced data analysis capabilities or plan to do so over the next two years. 
  • However, while 57% of respondents said their data strategy includes leveraging a central platform for investment data consolidation, nearly half (48%) admitted that their organizations are still measuring the investment skill-level of their investment team by using a “qualitative measurement, which mainly relies on anecdotal evidence of proper decision-making.”
  • 66% of respondents said they currently leverage five to eight investment data sources, with ESG data (59%) and traditional factor data (55%) prioritized but alternative, consumer, ESG, risk, and sentiment increasingly used in the search for new sources of alpha.
  • 52% said their organizations are still using spreadsheets to aggregate internal and fundamental data; other data sources are accessed manually (email, PDF, etc.) and integrated to make investment decisions.
  • 52% of respondents said “making their best investment ideas repeatable” was the investment process that could most benefit from data analytics.

“This survey shows asset managers are aware of the need to implement a digital operating model that enables efficient and safe growth, but at the same time are rightly focused on the imperative to spend scarce capital wisely. We recently did a survey of 300 global asset management firms, and 52% said their organizations are still using spreadsheets to aggregate internal and fundamental data. The “tyranny of spreadsheets,” to borrow an expression from the book, The Technologized Investor, causes analysts to spend time on data acquisition, aggregation, and compilation – not true analytics. There are horror stories of individuals printing a report before they walk into an investment committee meeting to present – and invariably, their response to questions is “let me get back to you on that.”

“Data science tools can practically eliminate data compilation and aggregation labor, so analysts can do true analytics work and provide timely insights. This need is becoming more urgent as investment data grows more complex. Our survey also found that 66% of global asset managers said they were leveraging five to eight data sources, and I expect that number to rise. So consuming large amounts of data is increasingly vital to managers. Without data science technology, you simply cannot analyze the large data sets required to surface the right signals and key insights you need to make better investment decisions,” said Paul Fahey, head of Investment Data Science (IDS) at Northern Trust.

 “As evidence grows around the value of investment data science, asset managers are looking to their data to help them drive high-quality outcomes so they can invest more effectively in their core activities.”

“There is growing evidence that incorporating investment data science helps managers better meet their obligations to regulators, owners, and investors,” says Gary Paulin, head of Global Strategic Solutions at Northern Trust. “Asset managers need to become more digitally conversant, not only because it will lead to improved investment outcomes, but because it’s being demanded more by their stakeholders, who are leveraging data science tools to do analysis of their own.”


Precision Intelligence Delivers True Competitive Advantage 

The current data science revolution represents an incredible challenge and unsurpassed opportunity for leading asset management teams. Achieving the far-reaching digital transformation initiatives currently being completed across the international business landscape is never easy or simple. It is quite challenging to refine business operations and adopt new technology-driven processes. 

The process of experimentation and refinement can be extremely challenging, yet there is light at the end of the tunnel. With every passing day, it becomes more and more apparent that success in the near future will be determined by the ability of firms to develop the precision intelligence they need on the fly in real-time. 

“Importantly, we should think about data science technology as not artificial intelligence, but augmented intelligence. We still have human beings applying intelligence and expertise, but these portfolio managers are using data and technology to be better at what they do.” said Paul Fahey, head of Investment Data Science (IDS) at Northern Trust. “The technology is helping to surface the signals to assist in their process, and that’s the ultimate outcome.”


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