Author: David Schumer
Artificial intelligence (AI) is transforming the way investors make decisions and manage portfolios. Systematic or quantitative investing has historically leveraged data-driven insights and advanced computer modelling techniques to construct portfolios. These “black box” strategies were designed to pick up on mathematical anomalies, market trends, mean reversions, and more to help investors seek consistent portfolio outcomes amidst a world of unpredictability, without the emotion or poor judgement of human intervention. Yet, these strategies still relied on human oversight with skilled PhDs, mathematicians, and quantitative portfolio managers inserted into the process to provide contextual sanity before actually making the buy or sell decision.
Quantitative tools and analytics also support discretionary and fundamentally driven investment solutions by providing investors with a means to navigate the unpredictability of financial markets. These tools offer features such as screening criteria, risk management support, and portfolio construction optimization. Integrating AI in this process may further drive market efficiencies, but it also raises ethical and societal concerns.
Moreover, AI driving decision-making is substantially different from quantitative investing in several ways. Quantitative investing emphasizes data-driven insights, scientific testing of investment ideas, and advanced computer modelling techniques to construct portfolios. Alternatively, AI driving decision making leverages advanced AI and ML techniques to enhance and fully automate various aspects of the investment process, such as data analysis, risk management, portfolio optimization, trading, and even customer service.
AI as the new frontier of portfolio management can be alluring, with promises of faster and more accurate insights, personalizing recommendations and identifying unique opportunities that humans may overlook. However, AI driving decision making also poses real challenges that need to be addressed by investment managers. AI systems are expected to adhere to social norms, ever-changing regulations, and ethics, while also making fair decisions that are consistent, transparent, explainable, and unbiased. AI systems could, for example, unintentionally generate discriminatory outcomes because the underlying data is skewed, which risks deepening existing structural injustices, skewing power balances further, or limiting access to information.
Building the plane while flying the plane
AI is transforming the world, not just the way investors make decisions and manage portfolios. As technology companies worldwide evolve AI tools and create new AI driven solutions, companies need to evolve their approach to using these powerful new technologies responsibly.
Technology has already significantly changed the way investors evaluate global markets, seek information, and communicate. A thoughtful and comprehensive approach to AI can not only prevent problems, but proactively help investment managers drive digital transformation, promote financial inclusion, and propel sustainable finance by incorporating environment, social, and governance (ESG) factors into decision making processes and by assessing global market risks such as climate change, geopolitical tensions, and cyberattacks.
Leadership Matters
To address these challenges, investment managers must therefore consider the role of a Chief AI Ethics Officer (CAIEO). Similar to a Chief Risk Officer who assesses corporate risk or a Chief Compliance Officer who ensures adherence to legal and regulatory frameworks, the CAIEO’s responsibility is to ensure the trustworthiness of AI technology throughout its development, usage, and deployment in an evolving environment and competitive landscape. The CAIEO will help ensure that developers have the right tools, education, and training to easily embed these properties in what they produce. Furthermore, the CAIEO advises and establishes accountability frameworks for CEOs and boards on the opportunities gained through AI, as well as the unintended risks posed by AI to the organization.
A qualified CAIEO should have multi-disciplinary knowledge of AI techniques, tools, and platforms, as well as their broad impact on business strategy and public policies. Effective communication skills are also crucial for bridging the gap between technical aspects and ethical considerations.
Hiring a CAIEO not only aligns investment managers’ AI practices with their values and principles, but also ensures compliance with relevant laws and regulations. Furthermore, a CAIEO can foster a culture of ethical innovation and continuous improvement within their organization, allowing investors to leverage the power of AI while minimizing potential harm.