How Leveraging Technology Can Revolutionize the Private Equity Industry

Himanshu D. Sharma
Research Lead Posts

Challenges facing the PE industry

As per financial data provider Preqin, dry powder of the buyout industry stood at $873 billion as of June 1, 2022. Overall it estimates that the investment industry reported $1.86 trillion of dry powder (up 3% from the end of 2021), excluding venture capital. Through the end of May 2022, Preqin said that firms have raised $92 billion and the total is projected to increase to $221 billion by year end1.

The Private Equity (PE) industry looks well-funded and could survive for some time if fundraising were to become increasingly difficult. However, from limited partners (LPs) point of view, they have entrusted PE firms with capital to invest and not to keep on the sidelines, building pressure on firms with large amounts of dry powder to deploy it suitably and fast1.

General partners (GPs) currently face the delicate balancing act of meeting their LPs’ timing expectations while still performing adequate due diligence for any investments. Also, the recession and other market disturbances seriously elevate the peril of holding huge amount of uncommitted capital. Nevertheless, automated workflows through the implementation of technology can streamline processes to save time, reduce risk, and ensure committed capital is being managed efficiently for PE firms2.

Further, technology is also needed for adapting to the evolving regulatory and legal landscape where safe data storage is critical, as made very clear by the General Data Protection Regulation (GDPR) and other data privacy regulations. Institutional Limited Partners Association (ILPA) is also pushing for standard approaches to data reporting aligned with new technologies pushing PE firms to stay compliant and competitive3

Race for digitization among the PE firms

As GPs are looking for cost savings in their operations, disruptive technologies such as AI (artificial intelligence), DLT (distributed ledger technology) and RPA (robotic process automation) could play a significant role in helping them achieve this. AI is a technology of special interest for many, as experts believe that it has the potential to expedite the deal-making process, allowing managers to analyze large data sets much faster4.

Now it looks obvious that in order to maintain a competitive edge, many reluctant PE firms will be forced into digital transformation across existing workflows. However, looking into 2022 all regions across globe are clearly at different stages of digital technology adoption, as per the survey conducted by S&P Global Market Intelligence (Exhibit 1)5.

Overall, 41% of respondents say that their PE firms are in the early implementation stage, mainly focusing on using Customer Relationship Management (CRM) and digital platforms for reporting. And only 13% are in the advanced digitization stage where these firms are leveraging data science for automated deal sourcing and due diligence. Further, merely 7% claim that digital technologies have been fully implemented in their processes5.

Many PE investors believe access to meaningful data is their biggest challenge during an acquisition. Further, many say a lack of reliable information is the most significant factor that will cause a PE firm to reduce its offer — or walk away from a deal entirely. Hence data-driven firms will not only remain at the forefront of innovation but will likely be the only firms to survive the storm of digitization6.

Exhibit 1:

Source: S&P Global Market Intelligence

A few big players in the PE industry like Blackstone and EQT have already understood the criticality of technology and started to heavily invest in it. Blackstone has started projects focused on data science, big data, and advanced analytics, in which teams will collaborate with investment professionals to optimize all aspects of operations. EQT also invested heavily in technology and developed an advanced in-house technology – Motherbrain, which supports the tracking of company lifecycles, ending at a fund’s exit. The technology permits visualizing and making the data accessible, building on a common corporate memory and collectively trained algorithms to create a structural competitive advantage, enabling EQT to make faster and more substantiated decisions. Further, Motherbrain provides multiple tools for analyzing peers and competitors as well as sourcing add-ons and analyzing markets for M&A opportunities. It combines external data points with company data and 140,000 unique connections uploaded by EQT itself7.

Another major technology apart from AI is blockchain technology, which can assist PE firms with sophisticated fund administration and reporting processes. Distributed ledger technology can enable PE firms and their investors to securely integrate capital calls, fee settlements, and reporting updates, resulting in increased transaction efficiency8.

There is a possibility that other big firms in the industry like Blackstone and EQT are building their in-house AI systems, but for most of the firms it will be more feasible from a cost point of view to make use of the third-party providers of AI technology and analytics.   

AI has the potential to revolutionize the way the PE industry operates 

The existing processes followed by most PE firms are labor-intensive and time-consuming, and rely mainly on a broad network of contacts, referrals, and a good reputation in the industry. Although all these factors are important and considering that many firms have also adopted deal origination software, it’s still not as sophisticated, targeted, and comprehensive as would be ideal9.

As per UDU – an AI and data analytics firm – a traditional analyst can determine if a company is minimally relevant by looking at the company or its website for about one minute; however, AI-powered sourcing can determine 195 results within the same time frame. Putting things in context, AI can source ~200x more companies in the same time frame compared with a seasoned analyst, with high accuracy and detail, and without taking breaks. The advantage of AI technology is that it can run constantly, updating and refining the results based on the changes in the market and continuously learning from user preferences. As AI can practically be operated 24/7 without human involvement, it can be programmed to trigger alerts once companies match the criteria set by the user, monitor the existing portfolio of PE in real time, and identify potential add-on targets. AI can also allow the PE firms to expand faster into new geographies because of its ability to adapt with high accuracy to new economic and political dynamics.

AI can be programmed to recognize and check company data as per the user exclusive standards. Significant amounts of data can be gathered rapidly to evaluate opportunities to make more informed decisions. For example, if a user wants to find a target company for acquisition using AI, the first step will be to define the deciding criteria based on any metric. The AI will then look through thousands of data sources and provide the most relevant output, after which the relevant results are structured and iterated based on user feedback to improve the algorithm. In the end, final data is scored and organized as per client preference. 

Adopting AI technology can dramatically change the deal origination method by gaining quick access to clean data and analytics across all market segments, businesses, and countries, by scanning millions of data points and providing the most up-to-date information and insight. Mentioned below are a few ways in which advanced technology can transform the PE industry9:

Deal Sourcing: AI can industrialize the method of finding businesses and can even remove the necessity for manual data scrutiny, allowing the PE workforce to focus on getting the deal underway. Advanced technologies can provide instant information, including opportunity identification, market assessment, contact lists, and businesses financial performance9. Further benefits of deal sourcing using technology is that it allows pre-qualification of leads based on data analytics, which saves a significant amount of time and ensures a high conversion rate, identifies the key person from the target company, and matches this with existing connections, allowing for an easy introduction instead of a cold call or email. As more data is accumulated, the algorithms get smarter, enabling the development of improved models10.

Research & Deal Evaluation: AI can gather myriad data on thousands of targeted businesses and generate quick analyses on the financial health of these companies, as well as industry and geographical dynamics. Machine learning (ML) can produce extrapolative analysis, allowing identification of businesses that may generate high returns in the future for PE firms. With this real-time analysis, GPs can identify trends in industries, sectors, and geographies that were indiscernible before9.

Post-Close Deal Activities: AI-driven platforms can provide vast benefits, as it allows through a single system the management of multiple relationships, real-time reporting to investors, third-party data integration, high security, and compliance, unifying data for both internal and external stakeholders11. AI can help PE firms monitor thousands of target companies, along with their products and services portfolio, website traffic, employee attrition rate, employee satisfaction, competitors, and many other key performance indicators with a speed that humans can’t match. PEs can leverage the same technology in their portfolio companies for inventory management, algorithmic pricing, demand forecasting, process automation, and sales analytics, among others12. AI and ML-powered tools can help analysts track the portfolio company every second of the day, targeting the most relevant information and filtering out irrelevant information, like generic marketing information, driving optimized monitoring13.  

Exit Support: At exit, it’s important for PE firms to have their information stored and organized, as this can allow a potential buyer to easily analyze the company and generate a bid more quickly. Integrating business units and capturing their growth trajectory in a single system will allow PE firms to effectively demonstrate the value added by them to potential bidders upon exit14.

Basic process behind developing a Machine Learning model

According to SYFTER – an AI and data analytics firm – building a proprietary ML model is a multi-phase approach which starts with investment professionals engaging with data scientists and ML experts to develop algorithms based on a unique investment thesis. It follows the following stages15:

  • Collation of data: Integrating data from multiple data providers used by PE, feeding them into one platform based on KPIs that meet the investment strategy.
  • Defining approach: Analysts then define the most important criteria to target based on geography, sector, size, revenue, employee strength, etc. Additionally, analysts add the other subtle signals, like if the target is looking to expand into new geographies, invest more in R&D, and hire a CFO.
  • Designing the proprietary ML models: In this step, based on the initial criteria, a data scientist explores the best ML techniques to create ML models and further do iterative collaboration with investment professionals to refine the model.
  • Analyst annotation: In this process, analysts refine the ML model through annotating information regarding the target in order to identify specific signals which will ultimately influence the scoring of that company’s attractiveness.
  • Data study: In this stage, ML scientists investigate different data science techniques to test and tune the hypotheses on the data. With a set of subtle signals and rule-based criteria as constraints, further study is conducted to achieve optimization. Output of this phase is multiple child models – which capture the subtle signals in different sets of sub models – and one comprehensive scoring model operating at a company level. Metadata captured about each company is then combined with the output from the child models. This scoring model is then ready to be deployed into the operational system.
  • Deployment phase: In this phase, everything that has been done in previous steps is integrated into the live system, which requires the involvement of full-stack software engineering experts.

Conclusion

From 2015 to 2020, the amount of data created by PE firms rose steeply from 15.5 zettabytes to 59 zettabytes and it is predicted to reach 148 zettabytes by 2024. This kind of data will be extremely difficult to handle and analyze by current norms and will need the involvement of AI and other advanced technologies. Therefore, the firms who are still waiting for the technological transition will soon find themselves in situations where they will struggle to raise money, find opportunities quickly, and meet the compliance requirements of internal and external stakeholders16. Both the survival and success of PE firms will depend on their adoption of the latest technologies and how quickly they embrace and implement it both in-house and in their portfolio companies17

The adoption of technology by the PE industry will undeniably improve how unstructured data is analyzed; it will also build greater collaboration across teams and enhance LP communications. However, the movement towards advanced technologies will likely result in employment losses at the junior analyst level. One important factor is that the transformation to AI and other technologies can be expensive; therefore, PE firms will need to scale it to achieve a satisfactory ROI. Another bottleneck is that data scientists and ML engineers are expensive to hire because of their high demand.

However, acceptance and acceleration of technological transformation in the PE industry will benefit not just the PE industry but the entire economy in general because of the optimal allocation of the significant amount of dry powder accumulated by PE firms.

How Evalueserve Can Help

Through our MIND+MACHINE approach we can provide support at every step of the PE deal lifecycle, including the process of DEAL SOURCING (opportunity identification, market assessment, comparable analysis, company profiles, etc.), RESEARCH & DEAL EVALUATION (industry analysis, financial modelling, capital structure analysis, due diligence, etc.), POST-CLOSE DEAL ACTIVITIES (periodic monitoring & reporting, profit improvement strategies, CRM data maintenance & data enrichment, etc.), and EXIT STRATEGIES (exit scenarios & return analysis, potential buyer screening, preparing marketing materials, etc.).

We also have the capabilities to help grow the PortCos of our private equity clients. We offer solutions to PortCos that enable their integral teams to devise business strategies. Our solutions include CFO & CXO support (variance analysis and financial analysis), MARKETING TEAM support (growth analysis and CRM maintenance), STRATEGY TEAM support (competitive intelligence and sector intelligence), and BUSINESS OPERATIONS support (IPO support, creative support for presentations, and business follow ups to fast-track action items). 

References

1https://www.marketsgroup.org/news/Private-Equity-Dry-Powder

2https://www.allvuesystems.com/resources/private-equity-dry-powder-hits-new-highs-and-brings-old-challenges/

3https://www.untap.pe/strategyexecutionblog/when-is-the-right-time-to-think-about-portfolio-monitoring-software

4https://www.broadridge.com/_assets/pdf/broadridge-infographic-private-equity-top-5-2022-trends.pdf

5https://www.spglobal.com/marketintelligence/en/news-insights/research/2022-global-private-equity-outlook

6https://www.affinity.co/blog/private-equity-firms-use-technology

7https://eqtgroup.com/motherbrain

8https://www.antwort.lu/news/privateequity-should-start-their-digital-journey/

9https://www.mnai.tech/article/5-ways-ai-can-help-accelerate-the-deal-sourcing-process

10https://www.sourcescrub.com/post/a-guide-to-top-deal-sourcing-strategies

11https://altvia.com/the-buyers-guide-to-private-equity-technology/

12https://www.financierworldwide.com/intelligent-automation-and-analytics-in-private-equity#.Yr9CeHZBxPZ

13https://syfter.ai/2020/08/13/how-investment-analysts-can-embrace-ai-to-become-the-rising-star-in-their-funds/

14https://www.pwc.com/us/en/tech-effect/ai-analytics/applying-data-and-analytics-in-private-equity-firms.html

15https://syfter.ai/2020/10/30/turning-a-private-equity-origination-strategy-into-proprietary-machine-learning-models/

16https://www.impactmybiz.com/blog/how-tech-is-changing-portfolio-monitoring-process/

17https://www.affinity.co/blog/private-equity-firms-use-technology

Evalueserve Disclaimer

The information contained in this report has been obtained from reliable sources. The output is in accordance with the information available on such sources and has been carried out to the best of our knowledge with utmost care and precision. While Evalueserve has no reason to believe that there is any inaccuracy or defect in such information, Evalueserve disclaims all warranties, expressed or implied, including warranties of accuracy, completeness, correctness, adequacy, merchantability and / or fitness of the information.

 

Share on facebook
Share on linkedin
Share on twitter
Share on email
Himanshu D. Sharma
Research Lead Posts

Latest Posts