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Data Science in Private Equity: Applications and Examples

Technology
Private Equity
Last updated:
August 25, 2022

Artificial intelligence (AI) and machine learning (ML) are hot topics across industries. From the first instances of AI in the middle of the 20th century to more recent advances, the drive to create smarter computers continues. However, these developments have not come without problems.

Programs such as Dall-E have the artistry industry worried. Deepfakes have been causing issues with reliability and fraud in multimedia to the point that even the Department of Homeland Security has become involved. Zillow's iBuyer fiasco, along with several other prominent AI-driven project failures, clearly spells out the current limits to this technology.

Nevertheless, for data-driven industries such as private equity, the benefits of data science — including AI and ML — cannot be denied. So where does AI belong? And how can data science in private equity be used to firms’ advantage?

What Are the Applications of AI in Private Equity?

Knowing where AI excels and where it falls short is imperative to take advantage of the technology. Let's look at 3 applications of AI.

Application #1: Process a lot of data

AI and ML need a lot of data to do their jobs well. For instance, the early deepfakes were buggy and lived wholly within the "uncanny valley.” But as the deepfake programs were fed more and more data — in this case, thousands of images of a specific person with many different facial expressions — they became more lifelike and difficult to detect. 

AI thrives on data and can easily comb through millions of points far quicker than any human can imagine. The data points are how an AI program's algorithm learns. But, as we'll discuss later, AI's downfall is its inability to think and reason. Cut off its training too early or give it bad data, and what you get out of the AI program will not be usable.

Application #2: Organization and categorization

AI is also very useful for finding patterns within data given the rules humans set for it. Text-based AI especially has come a long way in recent years. As an example, ecommerce companies are starting to use AI to better organize and tag their products based on text descriptions. This application also has great potential within the private equity space.

Identifying promising investment opportunities requires firms to leverage multiple data sources and sort through the company information they provide, such as industries, executive contacts, job openings, and much more. Fortunately, rather than manually organizing all of this data, firms can leverage AI-enabled tools to quickly find, categorize, and rank companies that match their ideal company profiles. They can even rely on this technology to automatically alert them each time a new relevant opportunity is identified.

Application #3: Find trends and make predictions

Perhaps one of the most well-known and exciting applications of AI is trend analysis and forecasting. AI especially excels at numbers-based analysis, and many companies are using AI-supported software to augment their internal data science teams to more easily identify trends.

Combining historical information with other key data sources to forecast future outcomes is game-changing, especially as firms brace for the possibility of a recession. Using AI to predict company revenue growth —or stagnation — can greatly improve the success of dealmakers’ sourcing strategies and lead to proprietary advantage.

Data-driven Decision Making Examples: How to Use (And Not Use) AI in Private Equity

The benefits of AI and machine learning in private equity are clear. The key is how your firm decides to implement them. To help, we've created some data-driven decision making examples of what to do — and not do — with AI:

DO: More Easily Source Ideal Opportunities

Private equity firms have the opportunity to use AI to more easily find ideal investment opportunities. By using available data, platforms can not only categorize entire industries into comprehensive and highly detailed market maps, but they can even identify when a company may be looking to raise capital.

As an example, a leading private equity growth firm was able to triple its deal volume by using data science to integrate and analyze information from its private company intelligence platform and other key sources. With the help of a team of data experts, the firm was able to run complex analyses and develop a proprietary data model to surface previously hidden opportunities that met their investment criteria.

DO: Identify Trends in Your Portfolio

Using AI to monitor data feeds and automatically alert you when a certain threshold is met or an anomaly is detected takes pressure off teams and saves significant time. This type of pattern recognition and trend analysis not only flags pacing issues on revenue goals, but can also surface security concerns or even attrition risks in your portfolio companies.

The key here, though, is to not go too far: Automating the entire process, including what to do in the case of an anomaly detection, is a potentially disastrous mistake. Once your automated, AI-supported workflow finds a potential issue (or opportunity), the last step it should take is to notify your team. They can then decide what to do with the information.

DON'T: Rely on AI Without Human Reasoning

One of the biggest downfalls of AI is its artificiality. The way AI "thinks" is driven by complex algorithms, and its decision-making is governed by the rules its programmers give it. It has no intuition or contextual thinking, and takes the data it is given as fact. While the more data AI gets, the better it will learn, it's only learning what to expect — not whether that data is “good” or“bad.”

This is where human reasoning plays an important role and must be a critical part of your strategy. The best data sources pair AI with human intelligence to supervise and check the quality of the data and insights it provides. People also play an important role in telling the story behind the data. While AI can certainly process all your data, categorize it, and surface trends to your team, actually using the data requires human interaction and interpretation.

The Best of Both Worlds

AI can determine your portfolio makeup by company size, industry, etc., and tell you how your portfolio is changing over time. What AI cannot tell you is why or what to do about it. This is why data scientists and analysts (i.e., the human element) are so important to successfully use artificial intelligence in private equity.

If your firm wants to take advantage of AI and ML, start by implementing a private company intelligence platform that gives you the best of both AI and humans.

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