Global investment management firm: delivering AI-powered solutions

The Challenge

With increasing investor and regulatory demand for ESG-related action and recognition of superior financial performance associated with sustainable business practices, our client wanted to further its ESG focus through intelligent automation. They sought support from our generative AI and machine learning experts to explore best practice solutions in the market today and to work collaboratively with AI-powered solution providers to reduce the manual workload associated with ESG-related shareholder voting and investee company rating processes.

Before working with our team, our client had adopted a labour-intensive, spreadsheet-based approach to tracking the performance of investee companies against a specific set of ESG criteria for voting and rating purposes. This method took up thousands of analyst hours and was prone to error, with subjective judgments and no way of updating assessments throughout the year and in the run-up to annual general meetings (AGMs). With the advent of new technology, like AI & ML, businesses across all industries have the opportunity to transform, unlocking new value for people, customers, and shareholders in near real-time. This is a clear example of how we helped our client to unlock that value while supercharging efficiency.

The Approach

We adopted an experimental approach to explore the technical feasibility of a new AI-powered ESG voting and rating tool. Over 8 weeks, we gathered requirements, contacted and assessed over 50 suitable solution providers, and developed a series of 5 custom-built PoCs. We worked collaboratively with our client, so key members of the voting and rating teams were engaged throughout, resulting in the selection of two PoCs to progress to a further discovery stage.

The Impact

Responsum, our large language model and generative AI platform, customised its end-to-end solution to meet our client’s unique needs. Powered by natural language processing (NLP) and the leading generative models, the platform demonstrated the ability to automate and enhance our client’s current manual approach to ESG-related analysis, voting and rating by 6-10 times. The tool has the potential now to not only reduce operating costs and errors, but also promote greater transparency around ESG performance, improving the governance and sustainability behaviours of investee companies.