Artificial Intelligence: A New Era in Private Equity?

1. What is AI?

Artificial Intelligence (AI) is the science concerning the creation of intelligent robots and computer programs capable of learning and solving problems in a way that replicates the complex mechanisms used by the human mind itself. Examples of this sector include virtual voice assistants, image perception programs and pattern recognition.

The most important branch of the AI ​​in the financial sector is the “Machine Learning“: differently from what happened previously where computers were driven by already written and predefined codes, Machine Learning (ML) consists of a set of algorithms able to learn from examples or supplied data.

Within ML we can then distinguish the important area of ​​Deep Learning, which can be defined as the driving force of the current success to which artificial intelligence is going through; in the deep learning the main characteristic is that the program used is not written by human beings, but instead is written and created by the same algorithm.

 

2. How will AI impact the Private Equity industry itself?

Artificial Intelligence can actually transform the whole process on how the target companies are identified and which one is the best investment technique to use case-by-case.

Using deep learning mechanisms, the work of a whole team can be reduced to a single entity capable of running queries and proceed with the entire transaction continuously, with no apparent gaps or delays. The real revolution in this area is the actual ability of a previously written algorithm to evaluate a whole company using just the inputs given from financial statements and reports, matching the results with an intelligent software capable of handling different types of information in numerous nuances.

Three are the main areas that would be affected by the implementation of AI in Private Equity industry:

  • Deal Sourcing

  • Deal Evaluation

  • Due Diligence

The first function is the one concerning the probing of the market looking for opportunities on which investing the funds raised. This is normally a task assigned to analysts that requires considerable amount of time and dedication. The use of AI can help in this part of the process: software adequately written are able to scan financial statements of a pre-determined group of companies and discover possible correlations between assets and liabilities useful in an investing perspective. Moreover, these software can for example easily scan the web looking for potential investments published on the last 24 hours. It’s important to note that the feedbacks given on the quality of previously found opportunities will make the artificial intelligence an always more accurate searching software, capable of providing in the future results with a certain level of quality.

Deal evaluation is a more complex part, in which most effort of a PE company is often involved. Nevertheless, AI is able to operate even in this area; indeed these machine learning softwares are able to analyze terrific amount of data and build complex excel models practically error-free (depending on the quality of the inputs). Combining this analysis with all the information regarding every statement filed from the company in the past, dig out from the same software looking over the internet and databases, it is possible to obtain different scenarios of investments and relative returns.

Last part of the process is the one of the due diligence, usually reserved for internal or external consultants. Instead of using physical people however, the implementation of AI software allows a company to conduct the whole diligence with a simple “click” on the tab. The system would be able to find any kind of legal or financial information, then catalog this data and eventually report some discrepancy. This method would reduce months of due diligence preceding an acquisitions to just a few days, in which consultants just have to check and analyze the software outputs.

3. So, is it time to leave Private Equity to computers?  

The use of artificial intelligence is, however, very far from a full application. This is because there are different complications:

  • New technology; this new system of investment analysis will need years of developing and money outflows in order to align the thoughts and actions, especially in a sectors related to investments and capital markets;

  • Slow learning; AI software will need time and effort to obtain a full working algorithm able to invest by itself;

  • Simulations problem; the main problem about investing in private equity is the always changing environment where decisions and investments are made. AI can take care without too much effort of the quantitative factors of the markets, but it needs to conduct innumerous quantity of simulations concerning the changing of the qualitative factors in which it operates;

  • Risk Prediction; financial markets and business activities are linked to uncertainty by definition: the future implications and unexpected problems can’t be predicted;

  • Reactions to possible collapses of the markets, financial crisis or new regulations can’t be included in the AI predictions.

To conclude, there is no doubt that Artificial Intelligence is going to be the future of numerous economics aspects, but it’s a long way to the top for a fully functional deep learning software.

Private equity attention in these years is not indeed focused on integrating AI in its processes as much as investing in AI developing companies. Anyway:

  • Hone Capital, the Silicon Valley-based arm of CSC Group (one of the largest venture capital/private equity firms in China), partnered with AngelList (an online platform for startup investments) to use AI to support the investment team in selecting seed investments.

  • Deep Knowledge Ventures, a Hong Kong life science venture firm, appointed—with a vote—an AI system called VITAL to its investment committee. The system makes its decisions by scanning prospective companies’ financing, clinical trials, IP and previous funding rounds.

This investing trend on the AI industry is shown in the following graph (obtained after a research provided by CBInsights):

 

Further data is showing this massive reshaping of this industry. Indeed private equity players have already identified artificial intelligence as the next area on which investments have to focus and there are different reasons to support this:

  • The number of financial transactions to AI start-ups increased 10 times over the last six years;

  • The share of companies using AI is expected to grow from 38% today to 62% in 2018;

  • The markets for AI solutions is forecasted to grow at a 55% CAGR (at least) between 2016 and 2020

  • A 2016 study made by Accenture and Frontier Economics suggests that AI has the potential to boost its profitability rate by an average of 38% by 2035

In the graph below, Tractica represents predictions about AI’s revenues till 2025, based on the recent accelerated development of this sector:

 

Editor Responsible: Carmelo Spallino

Sources:

  • Forbes: How Artificial Intelligence Is Revolutionizing Enterprise Software In 2017 – June 2017

  • Accenture: How AI boosts industry profits and innovation – 2017

  • Narrative Science: Outlook on Artificial Intelligence in the Enterprise – 2016

 

 

 

 

 

 

 

 

 

https://aspeclub.wixsite.com/bspec/single-post/2018/05/03/Artificial-Intelligence-A-New-Era-in-Private-Equity



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