In private equity, as in most industries, decision-making relies on accessing accurate and valuable information. However, these firms might encounter several challenges when looking for good enough data, mainly because they often deal with private companies. This article dives deep into how to identify good and valuable data on small companies and emphasizes its value for investment decisions and risk management. It also discusses how artificial intelligence-powered technologies can help identify these risks early on, leading to higher rewards.
The challenge of identifying valuable information for Smaller Firms
Lack of valuable data
According to Sturgeon's Law, "Ninety percent of everything is crap (or noise)." With the overwhelming amount of data available on the web, this theory becomes highly relevant. This is particularly acute for organizations that need information on private companies, such as private equity firms, trying to find golden nuggets of information among the vast amounts of ore. The information available for those companies is often sparse and hard to identify, particularly using conventional methods.
Diverse language and terminology
Smaller firms often face more existential risks, and the rewards for identifying these risks early on can be much higher for the private equity firms that own them. Mainstream methods of identifying risks are insufficient, as those firms often lack the standardized language to describe materiality, and their risks can be discussed in different ways, leading to increased difficulty in identifying relevant information. That's why it's crucial to adopt a specialized approach to analyze and understand the specifics of different business idiosyncrasies and terminologies and ultimately "translate" them into a standardized language.
Different industries talk about risks differently, making it hard to assess them. For example, an insurance company deals with uncertainty and potential losses as a part of its daily business, so it might use more negative language when discussing risks. On the other hand, a beauty company may use more positive language, focusing on things like new products, brand image, and customers' needs. These differences can make it tough to do a risk assessment that works for all industries. For this reason, a tailored risk approach is necessary to assess risks and analyze data through a common lens. With an insurance company, an understanding of financial models, insurance principles, and rules and regulations is essential. For a beauty company, looking at things like product safety, consumer preferences, and market trends is more important. By understanding the specific context of each industry, you can better identify and evaluate possible risks, which helps with decision-making and planning how to deal with those risks.
Leveraging AI for better risk management for small firms
Natural language processing (NLP) and machine learning are powerful technologies for private equity firms seeking to monitor potential risks in small firms. These technologies can analyze large amounts of data, extract the valuable 10%, and identify patterns, trends, and subtle nuances in the language used to describe risks. These patterns can provide insights into potential risks that might not be immediately obvious. This is particularly valuable as it helps identify risks and controversies preemptively, allowing for proactive risk management. Ultimately, these technologies can provide a more comprehensive and nuanced understanding of the risks small firms face, leading to more effective risk management and mitigation strategies.
This is where SESAMm's advanced technology comes into play, enabling firms to efficiently navigate the vast digital landscape and extract only the golden nuggets of information needed for decision-making.
How does SESAMm's advanced technology help with risk assessment?
Through its proprietary AI algorithms, SESAMm's TextReveal® can quickly find and extract those golden nuggets regardless of how hidden or specific the information is.
TextReveal can analyze and understand the language and terms used when risks on small firms are discussed on the web, notwithstanding subtle nuances. This empowers private equity firms to objectively understand the materiality of certain risks or if emerging threats still need to be formally recognized.
In summary, it is possible to assess risks and generate financial insights on small firms with the help of new technologies. Artificial intelligence and natural language processing, in particular, can help private equity firms conduct due diligence studies and monitor risks and controversies on target and portfolio companies.
Reach out to SESAMm
TextReveal's web data analysis of over five million public and private companies is essential for keeping tabs on ESG investment risks. To learn more about how you can analyze web data or request a demo, contact one of our representatives.