Could AI change the way private equity firms source deals?
A significant amount of effort and resource is invested in prospecting for transactions and maintaining key relationships in order to access the best deals. However, could technology help firms get an edge over the competition when it comes to deal sourcing? Steven Ford, Marketing Manager at Maven, looks at how Artificial Intelligence (AI) could potentially reshape the private equity market.
There is no substitute for face-to-face relationship building, and the job investment executives continue to do plays a vital part in the creation of a healthy deal-flow for a private equity investor. However, following enormous advancements in the digital sphere, including big data, AI, machine learning and social listening, there are intriguing new channels an investor could explore when looking to deploy a complementary approach to its deal origination tactics.
Private Equity investors have always tended to be farmers not hunters, with most of their deal-flow emanating from established personal networks. But new technology is opening up new opportunities and we are increasingly seeing investors taking a more proactive approach and becoming hunters. With competition for high-quality assets increasing, the level of dry powder at unprecedented levels, and valuations now peaking to heights not seen since before the financial crisis, are we about to see the best investors becoming the best hunters?
As competition amongst private equity houses grows, alternative ways to generate deal flow, source elusive proprietary opportunities, or simply build more dynamic relationships with businesses and/or its advisors are coming more to the fore. AI could even conceivably help get an investor ahead of the curve, by utilising complex algorithms to specifically search a multitude of channels for pre-determined criteria, steadily building a knowledge bank of investable businesses which would be ideal for equity funding - even before those businesses know that themselves.
By 2025, it has been estimated that there will be 163 zettabytes of data in world, 80% of which will be unstructured i.e. data that isn’t stored in a fixed record length format which includes documents, social media feeds, and digital pictures and videos. In layman’s terms that is masses of information on a truly colossal scale. An investor who can harness and process a fraction of that data will be furnished with actionable insights, which in turn could lead to more informed investment decisions.
Some investors are now seriously looking into how they could mine this data to enable it to map performance, market sentiment and trends, and most importantly uncover the businesses who may be ripe for equity investment. Monitoring aspects such as domain authority, web traffic, social media activity, app downloads, media footprint etc could signal genuine traction if there are notable improvements.
AI algorithms could be used to establish correlations and patterns. These algorithms would be intelligent enough to filter through all the noise and trawl through masses of structured and unstructured data derived from a variety of sources to filter and rank companies. With the advancements in machine learning, it also means that the more data it devours the more efficient these algorithms could become.
Signals such as the number of funding rounds a business has had, the age of the CEO, or if a family-run business has just hired an external manager could also be potential indictors that a business is ready to scale. The platform would automate the signal identification process and immediately flag to the origination team if a business justified a closer inspection.
Algorithms could also conceivably be created to look at the historical data of thousands of UK and global success stories to see if there are positive correlations, and if so, set out key timeline identifiers. If promising investment candidates start hitting these identifiers, or coming close, then this would be automatically flagged.
It all sounds like something straight out of the film Moneyball, the true story of the Oakland A’s general manager Billy Beane (played by Brad Pitt), who shifted from using conventional scouts when making player transfers, to building his winning baseball team on carefully chosen statistics and analytical data of player performances. Whilst this hasn’t signified the end of the position of the scout, we have seen a significant rise in sports analytics. Today, almost every major professional sports team has an analytics department or an analytics expert on staff.
Of course, any process involving data comes with increased risks. Any investor looking to adopt such technology will have to be very cautious on striking the right balance between ethics and innovation. The General Data Protection Regulation (GDPR) and AI confluence raises many intriguing issues.
So, could what has happened in sport replicate itself in private equity? Where investment teams supplement traditional methods (in this case relationship management techniques) with data driven insights, to help them source new investment opportunities? It is certainly a topic gaining more traction and several firms have claimed to have created just such technology. Increased competition for high quality assets is making many investors rethink their approach to deal origination and how they can be smarter about where and how to dig. The acid test will be in years to come when these managers can prove that the deals sourced through these innovative means have delivered positive returns for its investors.