Machine Learning and Big Data in Private Equity: Is Networking Still Needed?

The private equity industry is a world of risk and payoff, but digitized firms using machine learning technology greatly reduce their risk and reap larger returns from startups growing into large companies.A survey conducted by KPMG asked several private equity firms about their takes on big data and machine learning..79% were aware of the technology, 9% were considering implementing it, and 12% were already using a form of related software..Based on historical data, Critical Future projected that AI and machine learning industry revenues are expected to grow by more than six-fold by 2025.The following venture capital firm is an example of the success that following the trend of digitization and big data can bring.Motherbrain — EQT VenturesEQT Ventures, a Swedish private equity firm, is an example of the success that confidence in digitized methods can bring..They have expertly used a newly developed software called Motherbrain to lead the investment decisions in their venture capital wing..In 2017, they used the software to make 20 investments, most of which focused on the industrial, consumer goods, technology and health care industries..The firm has closed more than 30% of its investment deals based on the software’s results and has raised an impressive $50 billion of capital across 27 funds..Unlike most other firms around the world, their portfolio was raised upon the connections found by machine learning technology.How it WorksThe machine learning software works by identifying a trend within startups and using it to label them as either high or low growth potential..The software analyzes several time series — a set of data points indexed over time — of the startup’s financial performances and attempts to match them with the times series of successful companies..The more similar the data, the higher chance of success and more inclination to invest..Likewise, trends matching those of companies that experienced failure or had less success indicates lower growth potential.As EQT feeds Motherbrain with data from its own and external investments, the software’s algorithms will continue developing a better understanding of the trends that led to the success of the surveyed companies..The longer and more often Motherbrain is used, the more capable its algorithms become in differentiating between high and low growth potential startups.Big Data Makes Big ResultsEQT initially trained the software using data from their own successful investments found through traditional methods..Since then, the focus has been compiling financial data, including past funding, web ranking, app ranking, social network activity, and much more to add to Motherbrain’s data base for its ever-improving algorithms.Motherbrain does not only find new companies for EQT.. More details

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