How AI And RPA Are Upping The Hedge Fund Technology Game

Many hedge funds still live in the dark ages. They utilize manual systems to open investor accounts. Subscriptions and redemptions cannot take place unless an employee initiates the process and completes each of the required steps.

But an irreversible shift is taking place. “Smart Automation” or “Intelligent Automation” is gradually replacing the work carried out by humans making hedge funds much more efficient. The combined power of artificial intelligence and robotic process automation (RPA) offers multiple advantages that include lower operational costs, faster on-boarding, operational efficiency, speed up transactions, improved accuracy of data, improved investment decisions, etc.

According to a recent report by management consulting firm A.T. Kearney and CRM solutions provider Arvato, RPA will automate 53% of back-office processes by 2028. Robotic process automation solutions use artificial intelligence to learn and improve continuously. RPA bots allow repetitive tasks to be carried out accurately without the need for human intervention.

Some of the contributing technology drivers are,

  • Cloud services enhancements with greater computing power, flexibility, speed of deployment
  • Availability of a large amount of structured and unstructured data
  • AI/ML to help detect patterns/trends not possible with statistical models & humans

Technology Driver: Cloud Services

Cloud services are making rapid inroads into the $2.1 trillion hedge fund industry. How exactly do cloud services work? The cloud computing model allows hedge funds to use web-based software to service their clients. The activities that can be conducted “in the cloud” include client relationship management, data management, and various types of support services for the fund’s front, back, and middle offices.

Key drivers for adopting cloud services


Source: Cloud Computing in the Hedge Fund Industry

There are three ways in which hedge funds can deploy cloud-based services:

Private cloud: This option involves infrastructure being developed specifically for a particular hedge fund. The private cloud could be created by an internal team or an external provider. The advantages include gaining access to a platform that is tailored to your requirements. It’s the best choice in terms of security and performance. If changes need to be made often or you’re worried about losing control over the platform, it’s logical to go with this option.

Public cloud: For hedge funds looking for affordability and a robust glitch-free solution, the public cloud is the way to go. Deployment is much faster. Initial costs are far lower, too. However, there are disadvantages to the public cloud. Making tweaks to the system may not be possible. Additionally, for many hedge funds, the total cost of ownership could compare to the amount that would be spent on developing a private cloud platform.

Hybrid cloud: This could offer the best of both worlds. A hedge fund could choose to pick the most appropriate solution for itself by developing the system that it needs while still making use of a standard package offered by a service provider.

Technology Driver: Data

The advancements in computing power are providing new tools to hedge fund managers. It is now possible to process vast pools of data in a fraction of the time that it took earlier.

How does this help? Consider the possibility of combining big data sets pertaining to external as well as internal information. A data repository that comprises, say, individual client data, their transaction history, and customer relationship management details could provide invaluable information when it is merged with external sources.

This illustration from a McKinsey & Company paper titled Advanced analytics in asset management: Beyond the buzz shows how this could work.

A robust client-data repository includes the best of internal and external sources


 Source: Advanced analytics in asset management: Beyond the buzz

Technology Driver: AI/ML

AI has disrupted many industries including Fintech. Modern AI systems offer immense possibilities for analyzing large datasets. AI models can detect hidden patterns in data and hence can make useful deductions/predictions. Traditional methods are inadequate for this task.

Big data can be used to train AI models to predict hedge fund performance. Over time these models can learn many new patterns that are invisible to humans and can help the fund to boost their profits.

Is there any evidence that AI helps hedge funds increase their returns? A report on Preqin points out that 152 AI hedge funds performed better than the Preqin All-Strategies Hedge Fund benchmark by about 3% over a three-year period.

Cumulative Three-Year Returns: AI Hedge Funds vs. All Hedge Funds


Source: The Rise of the Machines: AI Funds Are Outperforming the Hedge Fund Benchmark

Why did AI hedge funds provide a better return? They can process massive amounts of data in seconds. This allows them to adapt to changing market conditions quickly. Additionally, machine learning helps the AI system update itself as it collects more data. This is especially useful in volatile markets.

Hedge Fund Use Cases of AI/RPA

Below explains how machine learning can supplement the existing investment process in various stages and automate/evolve/improve the investment process.


Source: J.P. Morgan Asset Management

An East Coast Hedge Fund Company – A multi-billion dollar hedge fund was able to automate the collection of intelligence and devise a system to convert it into actionable reports. The process involved the extraction of Excel data submitted by brokers and merging it with information from online broker databases and the company’s records.

Updating the broker stock estimates report – A company that specializes in robotic process automation helped an asset management company deploy RPA to review and collect data from various sources to prepare the daily broker stock estimate report.

The automation process involved opening Outlook to access the emails from brokers, reading the cells in the attachments, and entering the relevant data into the master spreadsheet.

What was the specific benefit that the RPA platform provided? The number of clicks of the mouse for preparing one report was reduced to three from the earlier pre-RPA 100+. The time taken for making a report was lowered from 15 minutes to one minute.

Artificial intelligence to “revolutionize” the operations function — A large hedge fund used AI to rethink how their back-office operates. The areas that have seen a change with the introduction of AI systems include the bringing together of the data that lies in different parts of the giant hedge fund.

Other areas of hedge fund operations that are being influenced by AI:

  • Analysis of large data sets to predict market movements.
  • Trade reconciliations: This involves matching the transactions with brokers with the hedge fund’s accounting systems.

 The bottom line

The hedge fund back office will never be the same again. AI and RPA will reduce headcount, improve efficiency, and boost accuracy. It’s up to the industry players to capitalize on these trends, those who can will be able to lower expenses and provide an improved level of service to their clients.

Drop a comment or write us an email with any feedback about this article, queries – info@pirimidtech.com View our portfolio of work and services offered on our website.

Leave a Reply

Your email address will not be published. Required fields are marked *

Recent Insights

Popular Tags

Get Email Alerts When We Update Our Insights.