Alternative asset management is growing. In fact, between 2017 and 2020, analysis by PwC shows this sector is expected to grow from US$11.2 trillion in assets under management (AUM) to US$13.9 trillion in 2020. Then things get really interesting. From there, the growth accelerates to reach US$21.1 trillion in 2025.
The main push is from a changing investment landscape, where the search for income is increasing. Real estate and infrastructure are set to be the fastest growing sectors, with infrastructure seeing a massive growth of 26.7% between 2017 and 2020, slowing to a still considerable 15% from 2020 to 2025.
Private equity is expected to expand by 6.3% and 9.8% respectively each year for the two periods, reaching a total of US$10.2 trillion in 2025, almost double the US$5.3 trillion reached in 2017.
The thing is, appetite for alternative investments is increasing at such a pace that investors cannot put money into these assets fast enough. The result is growing amounts of ‘dry powder’ cash – which is committed by funds and institutions, but not yet invested.
The more money there is to invest, the greater the fight for the best assets. To counter this, it is important to use technology to help traders becomes more able to identify and secure the best assets as soon as possible.
However, alternative assets are not easy to trade. You have a series of multi-layered compliance to work through, which takes time. Traders also need to consider the ‘what if’ scenarios more carefully, because of the long-tail nature of the investments being made.
This is where connected data can help with some of the heavy lifting. It can be used within compliance scoring to represent a trader’s strategy, with machine learning algorithms put in place to help any compliance officer assess that strategy and agree it in a much shorter time than you would without a technology intervention. It is a perfect example of humans and AI working together.
Once refined, graphML techniques can be overlaid to assist the trader, with real-time recommendation of the best possible actions to achieve desired outcomes. This helps to improve time efficiency, supporting traders who are then able to achieve a higher workload in a shorter time, giving them the best chance of targeting the top trades before their competitors who are still using the old, traditional methods of analysis.
Of course, representing this information to the trader in a way that is simple, easy to understand and has vastly reduced potential for misinterpretation is a further challenge. But it can be done. When it is done effectively and you can automate some of the investment decisions, it creates a perfect scenario for growth for both the trader and the institution she is working with.