How to reduce equity analysts' research time by 30%

Learn how Kainos assisted a leading UK investment company with empowering their equity analysts using Azure OpenAI.
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Date posted
22 October 2024
Reading time
5 minutes

This case study examines how Kainos, a trusted delivery partner, assisted a leading UK Asset Management in implementing Azure OpenAI. This initiative empowered equity analysts to work more efficiently, analyse more stocks and delve deeper into their analyses using advanced tools.  

The challenge

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Equity analysts in this leading UK Asset Management company were examining 3000+ stocks each year.
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Analysts evaluated each stock using a pool of circa 100 questions.
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Each stock took around 3-4 weeks to examine, with analysts having to manually search through company data, internet sources and call transcripts.
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Customer requirements

  • The organisation wanted to explore the use of Large Language Models to deliver significant productivity gains when analysts used generative AI. 
  • The design needed to be repeatable to enable further generative AI use cases, with Kainos transferring skills and learnings through capability building techniques for embedded value. 

The solution

  • Kainos’ secure Azure OpenAI accelerator demonstrated the potential business value of generative AI to the organisation’s analysts and senior leadership team. 
  • Azure OpenAI was integrated into the customer’s existing equity research system.
  • Development of an AI self-service tool for analysing structured and unstructured datasets, enhancing decision-making with natural language queries. 

The benefits

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30% reduction in the time taken to analyse stocks by equity analysts enables the team to analyse more stocks, allowing the business to scale.
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Equity analysts were empowered with the tools to research a wider range of investments by having more time.
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The organisation has the foundational AI platform to further build on capabilities.

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