We have three more predictions for 2024!
Private equity is a hyper-competitive space, with firms vying to invest in a limited number of qualified companies — being the first to engage with a company can give them a leg up on a potential deal.
Forward-thinking firms are starting to take advantage of AI’s ability to process vast amounts of unstructured data, such as regulatory documents, broker research, legal filings, and news coverage, in order to identify potential opportunities much faster and more efficiently than traditional manual legwork.
And when it comes to pre-screening, AI can help firms quickly identify any company-specific risks that may otherwise remain hidden, so they can decide faster to continue pursuing an engagement — or move on and focus resources elsewhere.
Managing idiosyncratic company risks (e.g. ESG, reputation, supply chain) has long been a significant challenge in asset management. Every portfolio manager has at least one horror story of waking up to news that tanks a portfolio company’s stock price, no matter how much time and effort they invested in spotting risks bubbling under the surface.
The question is always “why didn’t we see that coming?”
AI is changing this, as portfolio managers are beginning to effectively manage non-financial company-specific risks for the first time ever. Asset management teams are using generative AI solutions to continuously process vast amounts of unstructured data from local news coverage, legal filings, and other non-financial data sources to identify controversies early and act — before markets react.
ESG is an extremely complex topic that continues to bedevil asset managers and private markets firms alike. Effectively evaluating companies on ESG performance is a hard problem to solve that requires a lot of time and effort due to the vast amounts of unstructured data involved — and a lack of standardization for capturing and conveying ESG data.
This complexity is why third party ESG scores are popular, even as concerns about inconsistent methodologies and a lack of transparency grow, and few firms’ unique priorities are served by one-size-fits-all ratings. In addition, ESG complexity — and opaque ESG scores — help facilitate company greenwashing.
Generative AI offers financial services firms with a new and improved approach to ESG research. By summarizing large amounts of unstructured data, generative AI solutions with a strong retrieval model can handle the task of finding relevant information, cleaning it, and generating ESG insights efficiently.
WIth the right AI solution in place, researchers can start with custom insights and focus on high-level analysis and make decisions more quickly, such as whether to engage, invest, or lend.