This article is written by Dewansh Vaishisth who is pursuing a Diploma in M&A, Institutional Finance, and Investment Laws (PE and VC transactions) from Lawsikho.

Introduction

Private equity (PE) is an interest or ownership in a body corporate that is not publicly listed. The private equity industry comprises institutional investors like insurance firms and pension funds and firms funded by accredited investors. The second category is also known as private equity funds. 

Private equity transactions are the deals that take place with capital invested in companies that are not listed publicly. Essentially, the private equity firms and/or investors invest their capital in undervalued private companies which provides these companies an opportunity to structure and organize themselves before going public. In order to effectuate a private equity transaction, the private company undergoes a due diligence process which is carried out by the investors and the finalization of the equity deal might take months or even a year. 

Artificial Intelligence and its significance

Artificial Intelligence (“AI”) is a broad field of computer science that focuses on creating smart machines to perform tasks that may require human knowledge.

AI can be divided broadly into two categories, which being, weak/narrow AI and strong/general AI. The first category, which is the Narrow AI refers to the intelligent systems that are restricted to perform only the functions for which they were designed. These applications can be seen in everyday use, such as voice recognition in cell phones, machinery used for manufacturing certain products, and many others.

On the other hand, General AI is rather an adaptable intelligent system. General AI can perform unfamiliar tasks by applying its logistics and knowledge acquired through other domains and are not restricted to specific and explicit instructions.

Artificial Intelligence in Private Equity

The private equity investors tend to look forward to leveraging artificial intelligence to ease and efficiently increase the investment decision-making process in the desired unlisted companies. As discussed above, Narrow AI may provide ample opportunities to private equity investors to produce higher quality results at lower costs and at a faster rate by using a rigorous data-driven approach.

In a private equity transaction, AI opens up significant opportunities like deal sourcing and portfolio analysis, particularly for companies that follow a consumer subscription model or even leverage big data opportunities. On the contrary, adopting AI technologies for equity transactions poses significant challenges. Equity firms must consider the market usage and space for the AI technologies along with the workings and accuracy of such technology deployed to ensure that time and resources are used efficiently. It is also critical to know the working of technology, algorithms, location of the underlying IPs, and the storage of data sets. 

AI technology which is used for legal due diligence can also be used for financial and commercial due diligence, as well as other elements during transactions, thereby saving time and costs. In addition, AI technology is used for driving back-office efficiencies in HR, cybersecurity, IT, and data aggregation, thereby providing cost savings and faster decision making. Even in the front-end departments, AI technology can benefit the company to analyze and forecast customer patterns.

PE investors can be benefitted from the use of AI in enhancing their core capabilities, which are provided below-

1. Sourcing capabilities: AI can play a significant role to improve the sourcing capabilities of PE firms by opening up opportunities that cannot be introduced by traditional methods. Alternate data sets can be built and mined. AI has the potential to make the due diligence process more effective by comprehending a huge number of data points to determine the factors that are most suitable for different sectors. This will aid in the identification of appealing targets and the creation of more practical growth models. AI can track the most updated and current data, and streamline the most attractive opportunities instead of monitoring multiple irrelevant data sources.
2. Optimizing portfolio: on the closure of a transaction, AI can assist in monitoring and creating processes to enhance the portfolio in multiple ways. It can track important news, events, and market sentiments in which the portfolio company operates, thereby enabling the investor to make reliable market predictions and move faster than their competitors. Investors can detect emerging issues before it becomes too difficult to solve. Further, AI can simplify and automate redundant tasks that are shared across all portfolio companies.
3. Building relationships: AI can help PE investors to monitor and stay updated about their current LPs (money partners) and reach out to them if necessary. AI also helps to identify prospective LPs that may be responsive to the investor’s plan. Investors can be provided with real-time data by monitoring industries and portfolio companies. This is provided by a few investors and the use of AI can make the investor firm stand out from its competitors.

AI in Public v. Private Equity Transactions

When compared to the use of artificial intelligence in publicly listed companies, we find that the use of artificial intelligence is more common than in the private investment sector. Many private equity investors use artificial intelligence for only the investment decision-making process; the commercially available public markets items such as the Exchange Traded Funds (ETFs) are based entirely on artificial intelligence. The difference for such use of artificial intelligence between public and private investment is highlighted below:

• The number of private equity transactions is done on a small scale, is limited and the knowledge of such private deals is not widely shared. This makes it difficult to gather large quantities of private equity data to train algorithms.
• The time taken to know the details of a private equity transaction may take several months or even a year, where the performance of the deal is impacted by several factors like government policies, changes in law, or changes in interest rates.
• There is no efficient market controlling valuations, thus the unstructured data is more important in private equity investing.

To address these systematic issues, private equity investors need to modify their investing models. This may involve the processing of public data initially and then using private data to fine-tune at the end or evaluate the attractiveness of a potential PE investing using public market proxies. This may further involve the inclusion of data technologies like data lakes, thereby allowing for more unstructured data to be included in the due diligence process.

A common use of AI in PE Transactions

The use of AI has the potential to reduce information asymmetry in the investment decisions making process and allow for higher risk-adjusted returns. Following are the common use of AI in private equity investing:

1. With an indirect investment model, PE investors can gather a lot of information on external PE managers. The details disclosed in the fundraising process can be used to train AI algorithms.
2. PE investors can use AI to identify potential targets and avoid going through a formal selling process. It can also assist in identifying potential targets that are likely to outperform their publicly traded counterparts.
3. PE investors can reduce costs and increase value may use AI in diligence processes to gain a better understanding of the cost base of their potential targets and use this for valuation.

Conclusion

Adoption of AI can help the PE investor firms stand out and succeed from the competitors in the market. An investor firm can improve its core capabilities of deal procurement, portfolio management and value development, and client relationship management more efficiently and at a lower cost by leveraging the power of a data-driven approach. 

The implementation of AI technology, on the other hand, expands the possibilities for unpredictable results. Companies and firms need to consider the practicability of the products and should understand the process of paying for such technology. Every process of purchase, license, joint venture, or any other means, comes with its own set of risks. Private equity firms and companies must also understand how the learning is shared and what consequences, commercial or legal, could be faced when the system becomes smarter as a result of such training.

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