Over the past few years, the adoption of Artificial Intelligence into institutional investors' investment processes has proved to be successful in increasing investment results and operational efficiency. The remarkable application of such technology in this field has then grabbed substantial attention, particularly from large financial institutions, to the point that Forrester – the world's leading strategic consultancy firm – estimates that by 2025 an estimated $64 billion will be spent by large organizations to buy AI softwares and platforms.
However, there’s no such thing as two financial institutions sharing a similar investment process. Thousands of nuances make decision-making and portfolio construction processes unique for each firm and so require AI adoption journey to be carefully crafted to achieve a specific set of investment objectives, risk budgets and constraints. That is why seamlessly integrating Artificial Technology in the investment process – both at a product or process level – requires agreeing on some ground truths and standard best practices.
In this article we share a common framework that Wealth or Asset Managers can use to seamlessly integrate this powerful technology into their investment process, answering the most burning questions we collected from our clients that everyday use it to support their investment decisions.
Table of contents:
- The 3 Principles of AI adoption in investments
- What are the steps for AI adoption in asset management?
- How you can start your AI adoption journey today
The 3 Principles of AI Adoption in Investments
At MDOTM, we have been supporting institutional investors with AI-driven investment solutions since 2015. During our decade-long experience, our team of investment data scientists and experts have been challenged with one of the most complex tasks: developing reliable AI for institutional investors, banks, insurance companies and family offices. We successfully did it by creating Sphere, our AI investment platform that is used and trusted by hundreds of investment professionals at a global level.
“Adopting MDOTM's AI in our investment process has given us an augmented humanity to make better-informed investment decisions”
CEO at a Global Investment Management firm
However, integrating this technology into the investment process is the tip of the iceberg, as it often forms an integral part of a company-wide innovation journey. This process frequently starts by recognising that there is an untapped potential for large organizations to improve processes with Artificial Intelligence. What follows are the best practices what we learned from supporting dozens of investment professionals and large financial institutions integrating AI-driven investment solutions.
Principle #1: Customise & Integrate
AI should adapt to you, not the other way around
Living in the technological era should also include an important 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 forward-looking 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. This pillar emphasizes the importance of technology adapting to you, rather than expecting you to adapt to it, fostering a more user-centric and tailored experience.
Principle #2: Leverage internal and external capabilities
AI is not a "two guys and a laptop" thing
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.
Source: Cornerstone Advisors, The State of Bank-Fintech Partnerships
Principle #3: Having the right mentality
AI doesn't replace humans, it empowers them
Having the right mentality is crucial when it comes to AI adoption: technology is not meant to replace humans but rather to empower them. Having the right mindset enables individuals and organizations to leverage their capabilities as a tool to enhance human decision-making, efficiency, and productivity. By recognizing the potential of AI as a complementary technology, investment professionals can embrace collaboration between humans and machines, leading to innovative solutions, improved outcomes, and the exploration of new opportunities. The right mentality fosters a harmonious integration of AI into various processes, harnessing its power while keeping humans at the forefront of decision-making and strategic thinking.
What are the key steps for AI Adoption in Asset Management?
Even though the final output of AI’s Adoption may differ from one institution to another according to each one’s objectives and requirements, there are many common traits that serve as a baseline to assess where your institution is in this journey.
Step #1: Exploration Phase
During the Exploration Phase, financial institution’s decision-makers typically investigate and explore the benefits of AI applied to a specific part of their process, identifying the areas where AI can provide valuable insights and improvements.
Step #2: Partner Selection
As a result of the Exploration Phase, clients typically end up with a range of potential solution providers and then evaluate, based on a specific set of rigid criteria, whether their solution can be applied into their existing framework and how reliable it is.
Here is a list of factors to consider when evaluating partners:
- Proven track record: it refers to past achievements or performance of a company in the Fintech industry. The track record should be reliable, robust, and heterogeneous
- Science-rooted Investment philosophy and approach to research
- Ability to relate to different stakeholders: this means creating a strong and long-lasting relationship with different partners, such as universities This facilitates collaboration and access to new ideas, resources, and expertise
- Solid R&D department: it can help drive innovation and ensure that the business stays ahead of the curve in terms of technology and product development
A well-aligned partnership will ensure smooth integration and optimal outcomes.
Step #3: Investment Process Support
At this point, investment professionals need to analyze and break down each step of their existing investment process. This examination helps them identify specific areas where AI can provide valuable support, such as automation, data analysis, or pattern recognition. By pinpointing these opportunities, they can strategically integrate AI to enhance decision-making.
Step #4: Training of the Models
To align AI with investors’ unique requirements, it’s pivotal to train AI models to fit in their investment process precisely. This step involves feeding the models with relevant historical data and refining them to provide accurate insights. Tailoring the models to specific investment objectives and risk tolerance ensures optimal performance.
Step #5: AI Integration
This step involves combining the insights generated by AI models with the expertise and perspectives of your asset management team. This integration allows asset managers to leverage the capabilities of this technology while maintaining human judgment, ultimately enhancing the decision-making process. Of course, after such integration, continuous monitoring and optimization are essential for successful AI adoption. Regularly assess the performance and effectiveness of the integrated system and make adjustments and improvements as needed to maximize the value and benefits derived from AI.
How you can get started with AI Adoption Today
The integration of Artificial Intelligence in asset managers' investment processes holds immense potential for enhancing operational efficiency and improving outcomes. As we mentioned, the AI software market is poised for significant growth, highlighting its importance in the financial industry. By customizing AI to specific needs, leveraging internal and external capabilities, adopting the right mentality, and following a structured adoption process, asset managers can easily start integrating this technology into their processes, without the need of the IT or Operations teams. Embracing these best practices will enable any investment professional to outperform their competitors and deliver greater value to their clients.
However, to start the whole journey, it’s crucial to find the visionary innovator within the organization, the person who is willing to challenge the status quo and pave the way for a positive and impactful change in the daily workflow.
To discover more about how to integrate AI into your investment process, reach out to our experts
