State Street: Institutional investors in the age of AI

State Street: Institutional investors in the age of AI

November 15, 2023

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Technologie (07) AI artificial intelligence robot

In a survey conducted in July-August 2023, State Street explored the data opportunity in the age of AI for institutional investors across their entire operations. It is the first comprehensive industry study that quantifies the data opportunity in economic terms, and provides insight into where firms stand in their data transformation, the challenges they face and the tools they have at their disposal.

Overall, State Street found that the data opportunity for institutional investors can be transformative. Those with a holistic strategy are already seeing significant benefits in the form of revenue growth and higher customer satisfaction, retention and acquisition. To capture the opportunity, firms are prioritizing substantial investments in technology over talent and are open to partnerships and outsourcing.

Most institutional investors are not yet mature in their data journey. By institutional type, traditional asset managers and insurers were most advanced while asset owners, wealth managers and alternative asset managers the least. AI can help institutional investors to handle the growing amount of data. Respondents expected AI to provide the most value in the next two to five years in five areas:

  1. Enhanced cybersecurity
  2. Automated investment analysis
  3. Customer experience and engagement
  4. Risk analytics
  5. Personalized investment advice

'Ensuring that massive volumes of disparate structured and unstructured data is fit for purpose requires a solid data management foundation capable of capturing, curating, enriching and delivering data sets to AI and machine learning modeling engines', Aman Thind, Chief Technology Officer at State Street Alpha, explains.

'Time to information is also critical. Many use cases require forecasts and predictions in near real time due to the short shelf life of actionable investment and risk data. Legacy databases, spreadsheets and data silos present major obstacles to leveraging the benefits of AI. The considerable cost and effort of connecting these disparate data sources to a centralized, AI-ready repository rarely delivers the expected benefits. A new data management paradigm is required.'

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