New technologies are finding their way in the asset management industry, but the advancements are slow.
Big Data, Blockchain, Artificial Intelligence (AI) and, Machine Learning (ML) are all tools that are starting to find their feet in the asset management industry as different managers are trying to leverage on technologies to give themselves a competitive edge and also to meet investors increasing demand for better performance and lower fees.
Asset managers are looking for ways to develop more advanced strategies by integrating AI. ML can help to build evolving strategies that learn to adapt to the market and generate better long term returns than traditional ones that are just based on back-tested data.
More and more asset managers are employing data scientists and AI specialists to implement proprietary machine learning techniques. Their goal is to more quickly incorporate thousands of economic and market data points into bottom-up investment thesis.
There has been a rise also in the usage of robo-advisors in the wealth management space attempting to automate the investing process, especially in the stock market.
The Blockchain approach allows for a more transparent and efficient administration of a huge number of shareholders through tokenization and a distributed ledger. Thus implementation of blockchain by banks and asset managers makes complete sense.
We’re seeing more and more attempts from the managers’ side to upgrade and adopt new technologies, but they are still superficial compared to what those technologies can offer. In the other side, with allocators or asset owners, the attempts have been even less, and the adoption is way behind.
Which role can new technologies play in the asset owners side and especially their managers due diligence processes?
Big data is the fuel for ML and AI, but it gets its full value only when it is turned into meaningful information. In investment manager due diligence processes, where information is crucial for making justified decisions and choices, big data, in combination with AI and ML can be of huge help in revealing patterns, hidden trends or associations.
Managers selection is mainly focused on two aspects – the track records and qualitative assessment.
With regards to the quantitative side of the assessment, we are seeing some initiatives from some investors like Japan’s Government Pension Investment Fund (GPIF) – the world’s biggest pension fund.
Indeed, the principle of using deep learning can be applied to detect the investment style of managers from trading-behavior data. AI can help by finding out when trading patterns changed, giving the opportunity to ask managers for information and explanations for any change.
This might be possible for big investors like GPIF, which have the leverage to access this granular data and have the budgets to put in place such tools.
The challenge is how can smaller investors benefit from the latest technologies and speed of information to influence their portfolios.
Across all asset classes, the ‘desk work’ and analysis of documents and questionnaires collected during the due diligence process of fund managers requires a review of hundreds of hard copies. It is a very laborious and time-consuming process, and, in general, allocators lack the time and human resources to go through all them thoroughly. This is clearly an area where technology can have an impact. Digitizing the data from the source, structuring it for normalized digestion, applying rules-based scoring will significantly impact an investors ability to make a more informed decision.
In none of these cases, the machine will be fully recommending or taking a specific investment decision, it will remain the human’s responsibility. Its objective will always be helping end users grapple with a big volume of information and messy data.
Even though not yet widely accepted in the investment industry, technologies such as AI and machine learning, big data, or blockchain will sooner or later turn from distinctive advantage to a necessity. Manager due diligence processes are no exception to this trend.
The steep cost of big data acquisition and AI implementation may mean only larger players will be able to capitalize on these technologies if they are built internally. The rest will automatically look to work with third-party technology providers to leverage on their ready to use tools.
But the first prerequisite to deploying these tools, or other advanced technologies in manager selection and monitoring processes is to have structured and digitized data. In this context, digitizing asset managers due diligence is the first step to be done on the way towards using the benefits of such technologies.
Diligend enables you to digitize your asset managers data, to detect and analyze changes. To find out how we can help to make your future managers selection and monitoring processes faster, secure, and efficient, contact us on email email@example.com or via our contact form.