Technology & Innovation In Private Finance: Machine Learning & Big Data

Summary

In this content series, we write about how technology will reshape the private finance industry. Despite it being one of the largest sub-industries of finance, private finance (and M&A) has had less of an immediate need to utilise cutting edge technology. We dig deeper into the reasons of resistance and offer our views on the most interesting use cases that can start shifting the mindset. 

This month we focus on machine learning & big data. By unlocking and utilising the large quantities of data generated by running M&A operations, it’s possible to extract deeper insights than are available at face value, helping firms in their analysis and risk assessment, identifying more relevant targets faster, and automating initial analysis and many other aspects of the deal process.

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Data is Hard to Access, but it Exists

Big data and private markets are often thought of as contradictory. Unlike public company financials, accurate private company data is extremely hard to get a hold of, often limited to being available to just the company owners, and even then, the format can be inconsistent over historical periods. 

Over the recent years, however, certain types of private company data have become more available. Many trade registers are building web accessible APIs to their data, and there are numerous companies aiming to build company profiles based on metadata, such as websites and social media presence - all but the smallest companies now leave a digital footprint. 

Further, many serial acquirers will have access to a lot of internal data on a relevant sample of companies, and as with any statistical modelling approach, a small set of relevant data is much better than a large amount of irrelevant data. This is of particular importance for corporations with an inorganic growth strategy on a specific industry niche.

Finally, it is important to consider the many other different types of data generated during an M&A transaction, other than specific company data. Project timelines, emails, due diligence findings, success / completion reasons. All of this “project metadata” can be used to improve processes.

Consolidating Your Data Can Be Automated

The data generated during a deals life cycle comes in all shapes and sizes, ranging from Excels with financial data to PDFs of contract terms, CRM data to emails. By using specifically built database software, data can be stored in a structured manner, making it ready to use in big data models, however, it still needs to be entered. Until recently, this would usually be a very painful exercise, best done by a member of the team manually going through each document and extracting the correct values. 

Now, with advances across various technologies, specifically natural language processing and Generative AI (see last month’s post), a lot of this work can be automated with accuracy not seen before. This allows the vast quantities of hidden unstructured data to be unlocked and interrogated for its insights.

The Wasted Potential of Private Data 

We now have a unique, very valuable private data set in our hands. So what should we do with it to gain a competitive edge? 

The number of potential applications range far across the M&A process, with some of the key benefits deriving from sourcing and initial screening, where quick initial analysis can help focus valuable resources. For example, you could run:

• Instantaneous benchmarking of new targets against companies previously looked at, comparing both financial and non-financial metrics, and identifying outliers in their data;
• Automatically predicting liquidity events - when a family owned business is likely to sell, or a sponsor’s fund is coming to an end could enable more focused deal sourcing efforts;
• Initial estimations of valuations, incorporating the internal synergies seen when acquiring similar companies.

Further, once a deal is in full swing, the ability to ingest and analyse deal data in near real time allows professionals to focus on execution, and not manual re-processing data.

Conclusions

With the help of new tools and increased data consciousness, generating a corporate memory and a database from each firm's own private data, and extracting insights from it, will eventually become normal operating practice. As appropriate tools and a base level of acceptability for data collection is established, using private data (enabled by machine learning) for smarter decision making and more focused execution will become accessible to all. There is no doubt that machine learning and data will therefore bring advancements to the industry as a whole.

That said, individual firms can gain competitive advantages by:

1. Starting their data collection early, therefore having a more comprehensive dataset than their peers, 
2. Having unique access to certain private data that their peers are not collecting, or 
3. Coming up with more innovative analysis and ways of using the data

Each of these points allow firms to become smarter and faster than their peers - the race is on. One thing we know for sure: firms not taking any action in using their data will fall behind their technology enabled peers, and go extinct.

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‍Authors: Matthew Jones, Heini Salonen