The Use Of AI In PE
Recent advancements in artificial intelligence (AI) have touched our lives through virtual assistants, self-driving cars, and medical breakthroughs. Although machine learning and cognitive computing have largely been limited to the tech world so far, the finance sector is ready to take on the opportunities and challenges that come with adopting AI. More specifically, AI holds immense potential in the realm of private equity. Below is a short list of the many ways that AI is changing private equity:
- Quick data analysis: AI is powerful because of its capacity to process massive amounts of data efficiently. With the help of AI tools, private equity firms can analyze lengthy financial records and industry trends to make critical investment decisions.
- Due diligence: In screening portfolio companies, AI can conduct comprehensive financial, legal, commercial, and operational due diligence. After this thorough assessment of target companies, private equity firms can generate deals based on AI findings.
- Deal sourcing, structuring, and evaluation: AI is also being used to identify companies that fit private equity firms’ specific criteria such as an EBITDA range and market position. After the initial identification, AI can bring a fresh perspective to deal structuring and evaluation as it receives feedback to generate the perfect settlement.
- Predictive analysis: Utilizing a large volume of historical data, trends, indicators, and industry-specific information, AI algorithms can identify patterns and make predictions. Private equity firms can take advantage of AI insights and anticipate market movements, industry performance, as well as potential risks and opportunities.
- Risk analysis: AI can effectively mitigate risk for portfolios by quantifying risk factors and predicting potential issues. Furthermore, private equity firms can utilize machine learning to come up with strategies to mitigate risk through resource allocation and planned exits.
- Portfolio management: Many firms use AI to monitor their portfolio. Depending on their performance indicated by AI, firms can identify issues and opportunities early on. Data collected by AI can help private equity firms reallocate resources to increase a company’s value.
- Exit strategy: Private equity firms that utilize AI tools have a competitive advantage in developing exit strategies. With existing market trends and company data, AI can help firms identify optimal timing, potential buyers, or exit routes.
Across the board, AI can increase a firm’s efficiency. Automation, data analysis, and optimization are just a few of the many ways that machine learning can reduce manual work and save time for private equity firms to spend in other productive ways.
As AI gains traction, some of the trends that we will see are improvements in natural language processing, increased automation, stronger cybersecurity, and blockchain integration. Natural language processing (NLP) is AI’s ability to understand and mimic human language and communication. Since AI learns and adapts to the information that we feed it, it is likely that company reports, press releases, and current news articles will continue to improve AI’s capability to identify opportunities and trends. As AI analyzes more and more information, private equity firms can implement automation that takes care of some backend processes such as data entry, document organization, market research, and preliminary communications. AI is also beneficial in the domain of cybersecurity. Private equity deals and processes include a lot of proprietary information. AI can help safeguard confidential negotiations and documents by monitoring the data and controlling access. Finally, one of the more ambitious trends is the integration of AI and blockchain. These two technologies have gained tremendous popularity within the past decade and hold immense potential for all industries. Blockchain is a decentralized, secure ledger that can store any type of data. The efficient, accurate, and transparent technologies of AI and blockchain can enhance contract execution, secure data sharing, and potentially streamline an automated investment cycle protocol from deal sourcing to portfolio management. Combining the security of blockchain with the analytical abilities of AI, private equity firms can potentially boost productivity as well as performance.
If AI can change private equity, what is stopping us from implementing it? According to Private Equity International, one of the biggest roadblocks that are preventing firms from adopting AI is the lack of standardized data. Private equity firms do work with large amounts of data; however, this data is not uniform because all industries and deals are unique. Even within a given portfolio, information other than pure financials is rarely standardized. For AI to be effective, it needs to learn from substantial metrics. So, before we can rely on AI and automation, we have a lot of groundwork to lay in structuring data uniformly.
Even after data is consistent and usable by AI, many professionals claim that the culture of private equity places an emphasis on acumen and experience. Private equity firms are not willing to invest in and explore AI simply because they don’t fully trust AI to provide the same insights as an expert. This is understandable for now, but as cognitive computing grows more sophisticated, finance culture is likely to shift.
Private Equity’s Contributions to AI Development
So far, our discussion has been about how private equity firms can utilize AI to promote productivity. It is just as important, if not more, to recognize how private equity firms can and have contributed to the development of AI in general. According to S&P Global, private equity investments in AI and machine learning have increased significantly this year. Just during the first quarter, there were 247 deals in the AI and machine learning space. These transactions amounted to $5.81 billion, almost a 50% increase from the previous quarter. Private equity and venture capital firms invested in diverse software companies that specialize in AI applications from healthcare to finance to energy. Thanks to private equity deals, companies can develop more advanced technology, which contributes to collective efficiency.
We are just beginning to see what AI can do for private equity and other industries. Machine learning holds vast potential, but for private equity at least, it will require a few years to become integrated into standard practice. Nonetheless, it is exciting to picture a dynamic economical workflow streamlined by AI.