Winning AI Adoption in Investment Management 


The recent explosion of ChatGPT, especially with the release of the latest version ChatGPT-4, is a further remark that the mass adoption of AI is unlikely to stop anytime soon. After almost a decade of large industries like Healthcare, Aviation, Space Exploration, and Automotive embracing modern AI tools to augment how professionals make decisions, we see AI applications reaching a level of reliability and maturity never seen before, and the investment industry is no exception.

This increasing reliability and explainability of machine learning models compounded with the latest breakthroughs in human-machine interaction have made the case for adopting AI in the investment process stronger than ever. 

These insights were discussed during a recent live event hosted by MDOTM Ltd. More than 1300 investment professionals and executives attended to explore the implications of AI for the investment industry.

In this article, we will summarise the key takeaways of the event and delve into the role AI is playing in investing. We will focus on the practical use cases of new technologies – such as MDOTM’s Platform Sphere – which are strengthening investment professionals’ decision-making.

Why Investment professionals need AI to navigate market complexity 

The rapid growth in data volume and complexity over the last few decades has had a profound impact on any industry, including financial markets. As technology has evolved and become more sophisticated, the amount of data available to market participants has increased exponentially, making it more difficult to identify meaningful patterns and insights. This explosion of data has made financial markets more complex, presenting significant challenges for institutional investors. In this context, the ability to analyse large and diverse datasets has become a crucial element in making more informed decisions and gaining an edge over competitors.

This is where AI can offer valuable assistance. On a general level, AI in data analysis is already used across different industries and it brings huge benefits. In the medical field, for instance, AI can help in analyzing complex medical data and images, assisting in diagnosis, and predicting patient outcomes. In medical imaging, AI tools are being used to analyze CT scans, x-rays, MRIs, and other images for lesions or other findings that a human radiologist might miss. 

If we take into account the automotive industry, instead, AI is able to analyse vast amounts of data generated from sensors, connected cars, and other sources, improving vehicle safety, and optimizing performance. Many big companies are integrating technological systems that collect data on vehicle speed, acceleration, braking, and other factors, which is then analysed using AI algorithms to provide real-time insights and alerts to drivers.

As the industries mentioned above, the investment one is benefitting hugely from AI adoption. Since institutional investors deal with enormous amounts of data on a daily basis, AI-based tools help them extract insights from large volumes of data more efficiently, providing forward-looking analysis and more accurate predictions. 

Take into account neural networks, a type of AI algorithm modeled after the structure and function of the human brain. They can be trained to identify the relationships between different economic indicators and stock market performance. This is used to predict how the stock market will react to changes in economic data, helping investors make better and forward-looking decisions. 

Hidden Markov Models (HMMs) are another powerful AI-based technology that can help investment professionals navigate complexity. HMMs are statistical models able to analyze and predict complex patterns in time-series data, making them useful in forecasting market trends and identifying investment opportunities. They are particularly beneficial when the underlying system generating the data is complex and not directly observable

These are just a few examples of how AI technologies, in a complex and rapidly changing market, have become an indispensable tool for any professional, but especially institutional investors, looking to maintain a competitive edge.

Living in the Era of AI Democratisation

Today, we live in the era of AI democratisation, where the power of AI is accessible to everyone. In the past, the development and deployment of AI were mainly limited to a handful of specialised players and individuals. However, with the emergence of cloud-based platforms, open-source tools, and AI platforms, anyone can now leverage the power of AI without having to build everything from scratch. This has brought about a new wave of innovation, enabling individuals and organisations of all sizes to develop and deploy AI-driven solutions for a variety of use cases. 

Just as airplanes transformed the way we travel, AI is transforming the way we work, live, and interact with the world around us.

Take into account ChatGPT, which has revolutionised modern work life by offering a highly advanced natural language processing capability. It has the ability to generate human-like responses to queries and provide context-based solutions, thereby helping people across various industries perform their tasks with greater efficiency and accuracy. Now, with the latest version of this software recently announced by Open AI, is even possible to turn a sketch into a webpage with ease or to create data visualizations and pivot tables on Excel in a fraction of the time.

Another powerful example is Midjourney, an AI image generation tool that takes inputs from a human (usually through text prompts and parameters, but also other images) and uses a machine learning algorithm trained on a huge amount of image data to produce unique images.

Both ChatGPT and Midjourney are examples of AI democratization, providing accessible AI tools for a broader range of users. Similarly, in the investment industry, there are technologies that democratise access to AI for investment professionals, offering powerful tools to analyze and interpret financial data with more ease.

At MDOTM, we created Sphere, a no-code platform that leverages AI to provide unbiased investment inputs and manage portfolios. 

Sphere allows Institutional Investors to easily combine their experience with state-of-the-art AI and create forward-looking investment portfolios in every market scenario. Every day, it analyses market, fundamental, and macroeconomic data to study market regimes and provide unbiased market views across asset classes, geographies, and sectors to support asset allocation and portfolio management.

While ChatGPT has transformed the way we access and gather information, Sphere has democratised access to powerful investment and market insights, revolutionising the way we approach investment decision-making.

The process of decision-making relies on two essential elements:

  • Experience: refers to the ability to combine past knowledge and the outcome of events that have occurred over time in a non-linear way.

  • Data Evaluation: involves thoroughly analyzing the available information to gain the best possible understanding of a given situation at a specific point in time.

When it comes down to making an investment decision, an experienced investment professional is likely to follow a similar path. They would start by collecting information on past experiences, market phases, dynamics, and black swan events. Then, they would contextualize this knowledge by examining the current market situation and gathering as much relevant information as possible to make an educated guess about potential future outcomes.

In this sense, our technology uses AI to provide actionable investment insights based on a joint evaluation of the same components:

  • Experience – which is gained through a methodology that we called Non-Chronological Learning: a clustering analysis of past market dynamics and events.

  • Data Evaluation – which happens through a methodology that performs Regime Analysis: a derivation of HMM (Hidden Markov Model) that allows us to assess which type of risk framework we are in and the likelihood of transitioning to a different one.

How leading institutional investors use AI to improve investment decisions

Institutional Investors across the world are using Sphere’s AI every day to make more informed investment decisions. They each use it in their own customised way to make it compatible with their respective investment process.In order to better understand how our platform is actually integrated into each different investment process, the following are three real-life applications of Sphere, and the result they each gave to the respective financial institution leveraging its AI. 

Case 1: Portfolio Optimisation

One MDOTM Client, US Asset Manager is currently leveraging Sphere to:

  • Optimise their range of investment portfolios
  • Conduct advanced scenario analysis
  • Forecast market regime shifts

With Sphere, the client can interact with the market regime forecast feature, integrate their personalised views, and combine them with the one produced by the AI. Additionally, the client has the possibility to add a reference portfolio functionality, so they could upload a tactical/strategic Asset Allocation or custom benchmark as an additional dimension to control the enhancement of an existing portfolio. As a result, the client obtained the AI-driven optimisation of their portfolios in minutes, ultimately being more precise and adaptive as well as saving significant time.

Case 2: Hyper Customised Portfolios

One MDOTM Client, a Large EU Insurance Company is using Sphere to:

  • Enhance their Asset Allocation
  • Obtain forward-looking indicators
  • Internalise external mandates
  • Get a 360° dynamic market outlook

Sphere got integrated to support the allocation of the portfolio of the insurance’s assets. Since Solvency constraints play a significant role in guiding the insurance company's allocation, Sphere was trained to allocate having these constraints ex-ante the optimisation process. Additionally, it is used to construct an equity portfolio to hedge and optimise the overall portfolio positioning allowing it to tilt the exposure against the portion of the portfolio that is more constrained.

With Sphere, the client achieved an approximate cost saving of US$3.000.000 and 2+ years of time-saving, as most time-consuming tasks are now conducted by Sphere, allowing managers to focus on more value-added activities.

Case 3: Manage Multiple Portfolios

One MDOTM Client, a Swiss Wealth Management company is leveraging Sphere to:

  • Create multiple model portfolios starting from their monthly market view
  • Align individual client portfolios to their model while maintaining personalisation 

In this case, Sphere is used for the management of multiple portfolios with customised constraints across multiple portfolio targets, for controlling personalised investible universes integrated with the existing security/fund selection activities, as well as for getting immediate alerts and notifications any time portfolio alignment or market conditions change. Thanks to the platform, the client could achieve top-line growth, better client interaction, as well as more control over the client’s portfolio

Conclusion: Winning AI Adoption in Investments

The era of AI democratization is characterized by the increasing accessibility of artificial intelligence to a broader range of people and organizations. In the investment world, this represents a huge opportunity that should not be missed to boost performance, increase efficiency and create new opportunities across organizations.

In this context, Assisted Decision-Making Platforms, are providing investment professionals with unbiased inputs, assisting them in building market views and creating forward-looking investment portfolios. With Sphere, for example, investment professionals can leverage AI to analyse large and diverse data sets, providing unbiased market views across asset classes, geographies, and sectors to support their investment process -- all of this without the need for coding skills or building highly qualified and advanced data science and machine learning teams. 

As the AI democratisation trend continues, investment professionals who embrace these technologies will be well-positioned to outperform their competitors and deliver greater value to their clients.

Originally published on Finextra

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