Our CEO Tommaso Migliore was invited to the headquarter of Confindustria, the main Italian Association representing manufacturing and services companies, to bring his expertise and know-how on the positive impact that Artificial Intelligence is having on investments' professionals daily workflow.
In this article, we will go through the main takeaways from the conference as well as the three main principles we've learned at MDOTM, with over 7+ years of collaborating with C-level executives at leading banks, insurance companies, asset, and wealth managers to create AI-driven investment solutions and enhance their investment processes.
Living In The Era Of AI Democratization
To any investment professional, the recent explosion of ChatGPT, especially the latest version, is a further remark that the mass adoption of AI is just at the beginning, and it 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 support mission-critical tasks and augment how professionals make decisions, we see AI applications reaching a level of reliability and maturity never seen before. This trend has contributed to the growing buzz surrounding AI democratisation.
The greater access and diffusion of AI-powered solutions, like Chat GPT or Midjorney for image creation, are allowing more and more people to enjoy the benefits of AI, first of all mitigating the risk of error-prone human tasks and improving overall efficiency.
The concept of Democratization applies also to the investment industry, with the creation of AI-driven, no-code platforms that make it easier for institutional investors to harness the power of this technology to boost their decision-making processes.
The Three Pillars of Successful AI Adoption
Thousands of nuances make every investment process and portfolio unique and require every asset allocation to be carefully designed to achieve specific investment objectives, given well-defined risk budgets and constraints. If the benefits of AI tools like ChatGPT are visible and up for everyone's grabs, being able to systematically transfer them to a different domain, like portfolio and risk management, is more complex.
That is one of the main reasons why integrating AI in the investment process (both at a product or process level) requires agreeing on some ground truths and standard best practices.
Here is what we learned at MDOTM in our almost decade-long experience supporting investment professionals with AI-driven investment solutions.
AI Makes Bespoke Solutions: The New Standard
Over the last decade, one of the reasons that pushed the financial industry towards a unifying framework to manage portfolios has been that portfolio customisation and design have long been a complex and costly process that was virtually impossible to replicate at scale.
Although standardisation has been key to scaling this process and increasing the market for investment products, it has heavily relied on the concept of a model (or benchmark) portfolio that, in the current market environment, sometimes risks decreasing the likelihood of final clients reaching their goals.
In this respect, AI has contributed to the development of tailor-made solutions that meet the specific needs of each investor ﹣an approach frequently referred to under the name of 1-client-1-portfolio. This has brought a new level of customisation and personalisation, allowing institutional investors to fully understand clients and shape their risk-return profile and target allocation.
The Benefits of Personalised AI For Portfolio Managers
The process of customization is frequently performed using AI-powered investment platforms that Wealth and Asset managers can integrate seamlessly into their investment routines. One of the best perks of harnessing the power of AI in this way is that it gives an unbiased and cold view of how asset classes, sectors, and risk premia are likely to perform, given the current market scenario. They can then combine these almost real-time insights with the manager’s or investment committee’s views and outlook to build a more robust asset allocation and take advantage of untapped sources of diversification that would have gone otherwise unnoticed with traditional methods.
When we created our AI platform Sphere, we had this principle in mind, “Customise and Integrate”. What makes Sphere a tool trusted by 30+ top financial institutions is that it allows them to design their portfolios in every aspect so that they reflect their specific investment objectives, risk tolerance, and other constraints, while leveraging Sphere’s AI to optimize performance and minimize risk.
Customising portfolios at scale makes AI tools - including Sphere - well-suited to enrich and improve existing and complex investment processes.
AI has to adapt to you, not the other way around.
Living in AI’s mass adoption era should also include an additional remark. Unlike decades ago, today, we have access to No-Code AI Models that have been progressively replacing complex and error-prone spreadsheets. In that case, we finally have the chance to develop tools that adapt to how investment professionals think and to their way of getting things done – and not the other way around.
AI-powered investment tools are flexible and allow investors to combine their views and objectives with AI-generated insights. By doing so, investment professionals can tailor their investment decisions to their specific needs and preferences, and more importantly, be aware of the current market regime. In this sense, full customisation and integration are key for successful AI adoption, especially when we think about mission-critical activities like supporting the investment committee’s Strategic Asset Allocation or developing a new investment product, like a discretionary mandate, fund, or certificate.
What’s In It For Investors
The commitment to this second principle, “AI has to adapt to you, not the other way around”, should be at the core of any AI platform design and it definitely lies at the heart of Sphere. Sphere empowers investment professionals to design portfolios that reflect their unique investment objectives, constraints, and views. We built its AI-powered tools to enhance the decision-making process, not replace it.
By providing investment professionals with the flexibility to customize their workflows, integrate their existing processes, and incorporate their domain expertise, AI tools like Sphere enables them to make better, faster, and more informed investment decisions.
Compound and Augment Internal and External Know-How
Developing AI from scratch involves a high risk, years of large R&D investments, and a considerable amount of human (and brain) power deployed only to serve this cause.
Over the last decade, financial markets’ complexity has driven the asset and wealth management industry to shift from vertical to horizontal integration. In this light, the double-digit rise in open innovation partnerships is evidence that more than ever, innovation is frequently obtained by mixing up (and compounding) different know-how, and not every company has the necessary resources – or strategic incentive – to undertake such an initiative. For this reason, Open Innovation partnerships with external AI specialists and data providers can be a game-changer.
By leveraging the expertise of both internal and external experts, companies can ensure that their AI-powered investment tools are of the highest quality and are constantly being updated with the latest market trends and data.
This trend is also backed up by recent data from the strategic consulting firm Cornerstone Advisors that shows how in the last three years, almost two-thirds of banks, insurance, and Asset Management companies have entered into a strategic partnership with a fintech company to develop new products or to improve the efficiency of processes.
How can Asset Managers Leverage the AI’s Alpha?
As AI becomes mainstream between Asset and Wealth Managers, investment professionals must recognise its benefits to the investment process. The increasing reliability and explainability of machine learning models compounded with the latest breakthroughs in human-machine interaction make the case to adopt AI to support the investment process not only compelling to improve the overall investment process, but also worth significant bottom-line results as they push towards higher productivity and overall efficiency of the Asset Allocation and Portfolio Construction process.
As we move forward, the rise of No-Code AI platforms brings AI to every investment manager’s desk, allowing a more sophisticated and complementary approach between human intelligence and machine learning. In this perspective, Financial Institutions leading the technological revolution will increasingly benefit from the emergence of additional sources of Alpha that stem out of the increased process and operational efficiencies (i.e. Operational Alpha) as well as better long-term diversification and efficient use of risk budgets that comes from using machine learning to support investment decisions (i.e. AI’s Alpha).
Going beyond the ChatGPT buzz then means truly understanding the results of all these years of investments in AI: AI adoption is unlikely to slow down soon. This makes its integration into well-defined and existing investment processes a strategic priority that will drive the financial institutions’ ability to generate more revenues and AuM growth in the next decade.
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