The Role of Artificial Intelligence in Private Equity Investment Strategies

As technology continues to rapidly evolve and innovate, our lives, and the way we do things, are constantly changing. The invention of artificial intelligence (AI) is affecting and altering how we live, how we work, and even how we invest our money. 

AI is involved in everything from smart homes to chess-playing computers, to self-driving cars — and so much more. It even has made its way into private equity (PE) investing. The role artificial intelligence plays in private equity investment strategies is a nascent yet exciting one.  

Although it has not been widely adopted by the private equity industry quite yet, it is anticipated that the future role of artificial intelligence in PE investment strategies will have a profound impact on many aspects of PE investment and analysis.  

Whether it is analyzing data, or finding an investor accreditation program, AI is revolutionizing how private equity works. 

What is AI? 

Designed by humans, artificial intelligence is computer-generated task solving. 

AI encompasses a variety of distinct technologies that enable computers to imitate human thinking. Using artificial intelligence, computer programs can now solve tasks and problems that require several human attributes, such as:

• visual perception

• speech recognition 

• decision-making, and 

• word translation.

Imitating these human attributes with different technologies, AI allows computers to perform tasks practically like humans — right on down to being able to learn from their experience!

The technologies that AI employs to give computers this human-like intelligence include:

• machine learning

• natural language processing 

• computer vision, and 

• robotics

These different technologies are changing how software is developed and how computers are programmed. The financial industry, in particular, has benefited from AI technologies. Several protocols in the financial arena have been improved by machine learning, natural language processing, computer vision, and robotics. The invention and application of these technologies have assisted financial companies in automating their processes, detecting patterns of economic growth, and predicting future market trends.

AI Subsets and their Role in AI Development. 

Each one of the AI subsets listed above plays a pivotal role in AI development.

Machine learning allows computers to learn from data and improve their performance over time — without having to be reprogrammed to do so. Machine learning uses algorithms and data to imitate the way humans learn.

First, a computer programmer will choose a computer learning model. Then, after inputting all the necessary data into the computer, the programmer simply lets the computer model train itself. 

A machine learning system can be:

• descriptive — the system uses the data to describe what happened,

• predictive — based on the data, the computer predicts what will happen, or 

• prescriptive — the computer prescribes what actions should be taken. 

Each one of these capabilities adds to the many and varied ways in which machine learning supports the system alterations that AI brings to any industry it is applied to. For example, in the area of finance — and investment in particular — machine learning can identify patterns that signal attractive investment opportunities. It can also assist in predicting a company’s future performance. 

Natural language processing (NLP) is a subset of AI that works to allow computers to recognize words — both written and spoken — and to respond to them. NLP can also create new text. 

NLP is the technology that allows Apple’s Siri and Amazon’s Alexa to recognize a person’s speech and respond appropriately to it. NLP is also what a computer is using when you type in a Google search and the search results pop up even before you have finished typing. NLP technology is what allows computers to recognize certain emails as “spam,” as well as to categorize emails based on their contents. 

In the world of finance, NLP is being used to (among other things), perform investment analysis. NLP’s text recognition capabilities and algorithms allow it to quickly sort through vast amounts of publications (e.g., blog posts, news articles, reports, etc.) to identify market trends and predict the future performance of select securities or industries so that investment strategies can be based on the most current information.

Computer vision is the subset of AI technology that trains computers to understand and interpret visual information. Computer vision allows AI to pick up information from visual images, like pictures or videos. This technology empowers devices to use human-like visual capabilities to analyze information or detect patterns in visual images. The information gathered this way can then be used to make necessary decisions. For example, in finance, computer vision can translate digital visual information to authenticate a user’s identity. Facial recognition is an example of this. It can also verify documents such as passports or IDs. In addition, computer vision enables computers to process complicated and extensive documents like loan documents. Using computer vision, AI can sort through and authenticate vast complex documents quickly and accurately.   

Robotics process automation (RPA) is a type of AI that uses software robots to automate repetitive tasks. Some examples include data entry and automatically generating reports. Because the software can be trained with specific information, RPA can reduce errors and improve efficiency. 

How Artificial Intelligence (AI) is Transforming Private Equity Investment Strategies 

Artificial intelligence is transforming industries — and individual expectations — worldwide. It is rapidly being adapted to a variety of sectors to change or improve data analysis and decision-making.

AI is having a transformational impact on the private equity industry as well. True, many PE firms have been slow to adopt AI, yet this may be changing as its unique benefits become more well-known. Experts in the field believe that the more forward-thinking PE firms willing to leverage AI’s capabilities will have a distinct competitive advantage.

One aspect of PE investment that AI is affecting is private equity strategies. The following are some ways in which AI can be used to improve PE investment strategies: 

Target Identification 

Any PE investment must begin with identifying a company to invest in. However, because PE investments are made in private rather than public companies, it can be far more difficult for PE firms to find investment opportunities. It is also more difficult to find information about private companies. Traditionally, PE firms have had to rely on personal networks, use search engines, or manually wade through public data to find the information they need about potential investment opportunities and companies. 

Not only is this time-consuming and labor-intensive, it can also be overwhelming for the analysts assigned to gather and go through mounds of information trying to parse out what is relevant.  

But AI is changing all that. For those PE firms willing to take on this new technology, AI can help PE firms find investment opportunities quickly, accurately, and efficiently. By implementing the NLP subset technology, AI can rapidly and accurately analyze vast amounts of data. The software can filter out the relevant information from large amounts of data and can then turn that information into relevant and actionable reports. 

Due Diligence

Once a target has been identified, of course, the next issue is that of due diligence. Due diligence requires delving deeply into a company’s history and assets. Again, extensive amounts of data must be sourced, compiled, analyzed, and summarized. Conducting thorough and proper due diligence is critical to any PE investment. Good due diligence identifies the risks as well as the potential rewards associated with purchasing a company. Without it, a PE firm (and its investors) may be taking unexpected or unacceptable risks. 

But due diligence is, once again, a long and time-consuming process. Typically, it requires reviewing and analyzing a company’s confidential information memorandum (CIM) — a massive and comprehensive document. In addition, due diligence demands doing outside research and fact-checking the CIM for accuracy. All of this takes a significant amount of time and effort.

Luckily, AI is helping to streamline this process as well. 

AI data-driven algorithms can rapidly analyze vast amounts of data, — enabling PE firms to conduct comprehensive due diligence reviews efficiently and accurately. This, in turn, allows PE firms and investors to make better and more informed decisions. Making better decisions can reduce some of the risks associated with private equity investing. Plus, in a market as competitive as private equity, being able to move quickly to seize an opportunity is critical for both PE firms and their investors.

Although AI is useful for the automation of many tasks, many laws only allow real humans to complete due diligence such as the third-party requirement under Rule 506(c) of Regulation D for verifying investors

Portfolio Optimization

The evolution of AI is also changing portfolio management in private equity. 

Portfolio management, the process of establishing investment strategies to optimize returns and minimize risks, requires selecting, overseeing, and managing diverse investment products. This can be done by individual investors, professional portfolio managers, or financial advisers. Regardless of who is managing the investment portfolio, portfolio management requires a deep knowledge of many key investment concepts including (not limited to) risk management, asset allocation and diversification.

Portfolio management also demands constantly keeping up with expert finance and investment publications. Constant research related to market conditions and global developments that could affect investments is a necessary part of portfolio management. Not surprisingly, this requires assimilating a considerable amount of complex data. The traditional process of portfolio management relies heavily on human judgment and experience and involves considerable manual effort to research and analyze investment options and market trends.

AI’s algorithms can be instrumental in assisting PE firms in managing multiple portfolios. Because AI can be programmed to detect trends and scrutinize vast amounts of data, it can:

• streamline the portfolio management process,

• alert PE members to trends and patterns that may indicate concern, and 

• predict future market changes or events. 

In addition to being faster than human-driven portfolio management, AI-driven portfolio management is far more precise. 

By using AI to create forecasts and predict future events, PE firms can optimize PE portfolios by balancing investments while at the same time mitigating risk.  

Integrating AI into portfolio management does present legal challenges and ethical considerations — not the least of which is increased scrutiny from the Securities and Exchange Commission (SEC). Thus, PE firms should consult with all appropriate legal and regulatory counsel, as well as other professionals, before implementing AI technology. 

Nevertheless, this new and innovative technology can process vast amounts of information at astonishing speeds and improve human decision-making. Ultimately, enhancing PE systems with AI can deliver better returns for investors.  

Ethical Considerations to be Aware of if Integrating AI Into Your Investment Strategy

As innovative and exciting as AI may be for the future of private equity, it certainly is not without its challenges or concerns. The following are just some of the ethical issues and considerations that PE firms and investors should consider when adding AI features to their existing systems.

Privacy

One of the biggest concerns raised by AI for private equity applications is the data that the technology relies on. The use of AI immediately raises data privacy issues because AI uses, collects, and analyzes enormous amounts of sensitive personal financial data. While necessary for the system to access this data, it is vital that an investor’s personal financial data be protected. Integrating AI into your investment strategy requires carefully balancing these competing needs. 

Bias

Bias in AI algorithms is another concern for AI use in private equity deals. Because computers are trained on data input by humans, the algorithms used may contain certain biases. This can lead to discriminatory trading practices. PE firms using AI must ensure that the models they use will perform correctly and that their application will lead to fair and accurate results.

PE firms considering integrating AI into their investment strategies should be aware of and address these and other ethical considerations applicable to this new technology.

What Does the Future Look Like for AI and Private Equity Investments?

While many PE firms may have been slow to adopt AI, this appears to be changing somewhat. Experts agree that getting on board with AI can give PE firms a competitive advantage. Plus, it cannot be denied that AI is fast becoming a part of our everyday lives — including private equity investments.

AI advancements are changing our everyday lives right here, right now. This technology is currently altering the way we work, communicate, live, drive our cars, and invest. 

While AI presents certain challenges for PE firms, experts agree that it will continue to innovate and improve — increasing the opportunities for growth and investment success.

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