We had the honor of speaking with Rhodri Preece, CFA, Senior Head of Industry Research for the CFA Institute, who shared his insightful opinions on the new CFA Institute research report, the significance of artificial intelligence in the investment management industry, and the advantages associated with it, elements that senior leadership of an organization must establish in addition to the ethical framework, their contribution to the organization’s culture, and the spectrum of artificial intelligence applications.
Rhodri Preece is Senior Head, Research for CFA Institute and is responsible for leading the organization’s global research activities and publications, managing the research staff and collaborating with leading investment practitioners and academics. CFA Institute produces the highest-calibre research on issues and topics most relevant to the investment industry, including rigorous in-depth research, forward-looking thought leadership content, applied investment insights, and commentary on trending investment topics.
Rhodri previously served as head of capital markets policy EMEA at CFA Institute, where he was responsible for leading capital markets policy activities in Europe, Middle East and Africa region, including content development and policy engagement.
Mr Preece is a current member of the PRI Academic Network Advisory Committee, and a former member (2014-2018) of the Group of Economic Advisers of the European Securities and Markets Authority (ESMA) Committee on Economic and Markets Analysis.
Prior to joining CFA Institute, Mr. Preece was a manager at PricewaterhouseCoopers LLP in the investment funds group (2002-2008). He has a BSc and a MSc in Economics and is a CFA charterholder since 2006.
When did you start your career? And how will you describe your role as a Senior Head, Research for CFA Institute, being responsible for leading the organization’s global research activities and publications, managing the research staff, and collaborating with leading investment practitioners and academics?
I began my career in the finance industry in 2002 at a professional services firm where I oversaw a range of investment funds and financial services clients. I studied for the CFA Program at the same time and obtained my CFA Charter in 2006. In this role, I became well-versed in investment fund strategies and asset classes, products, and business models. From this role, I moved to the CFA Institute in 2008 where I initially served as Director of Capital Markets Policy, and since 2018, I have headed the organization’s research activities and publications.
In my current role, I am responsible for leading the global research team and overseeing the creation of research and thought leadership content on key investment industry themes, including data analytics and technology, sustainability, capital market structures, and others.
What are your thoughts on the new CFA Institute research report, Ethics, and Artificial Intelligence in Investment Management, a Framework for Professionals?
The use of artificial intelligence is rapidly increasing across a broad spectrum of finance and investment applications. AI has enormous potential and when used in an investment context, it can empower professionals with the technology needed to enhance their work and deliver improved outcomes. However, the adoption of AI also brings new ethical challenges and considerations to light.
Our report sets out a framework for implementing ethical AI practices across organizations and provides a starting point for the industry to refer to as adoption becomes more widespread. The ethical framework is comprised of principal considerations and professional standards that provide guidance to investment teams as they approach the brave new world of AI. By embracing this approach, firms can ultimately ensure that client interests are best served by demonstrating their ethical commitment to the advancements of such technologies.
Since artificial intelligence is utilized by numerous industries, how do you believe it is essential for the investment management industry, and what are the benefits?
The investment management industry is really starting to recognize the benefits and potential risks that come along with the implementation of AI in the investment process. Firms are now able to bolster investment strategies with new insights that are borne out of the incorporation of big data and new tools to parse information.
Firms now incorporate machine-learning applications including artificial neural networks, deep learning, and other non-linear methods supporting a variety of investment strategies. At the same time, big data is becoming increasingly commonplace, including alternative and unstructured data such as earnings-call transcripts and company filings, social media, satellite imagery, and others.
What are the various elements that the senior leadership of an organization must establish in addition to the ethical framework, and how will these elements contribute to the organization’s culture?
Beyond these complex ethical considerations, firms must put in place a broader framework to manage the risks and opportunities brought about by AI, encompassing organizational culture, risk management, skills, and competency. It’s important that the senior leadership of investment organizations establish a vision and strategy for the development and use of AI in the firm’s business model. Creating a culture conducive to the collaborative development of AI across functions and teams with different skill sets will help ensure the success of an AI initiative.
The organizational culture should also encourage an appropriate degree of innovation and risk-taking within an ethical and client-centric context. The risk management framework should encompass the responsibilities and ultimate accountability of senior management and establish appropriate governance structures, such as approval bodies or cross-functional expert oversight committees for AI development, along with ongoing management supervision.
Finally, firms should ensure the relevant business units possess sufficient knowledge, skills, and abilities in the areas of AI and data science since these fields are sufficiently distinct from the investment expertise of the core staff. The scale and complexity of AI projects necessitate a collaborative approach to AI development, with professionals working effectively in what we call T-shaped teams – the combination of specialists in data science and investments, respectively, joined by an innovation function comprising product specialists, knowledge engineers, and others. This T-shaped team should overcome the knowledge barrier regarding technology deployment and ensure that ethical considerations are understood across the respective functions.
What is the spectrum of issues brought about by AI tools and big data in investing? And what are the possible solutions to those issues?
The availability of AI tools to harness big data can introduce more complexity in the investment management process. Potential risks include how data is sourced and processed by AI tools, where issues of data integrity and potential biases exist, as well as transparency and accountability, with potentially limited ability to observe or explain the decision-making process of an AI application to clients or supervisors. The AI tools described in the framework cannot yet think and act analogously to humans, which is both a strength and a weakness in their use. Because AI algorithms do not intrinsically possess fundamental ethical attributes of honesty, fairness, loyalty, and respect for others, they must be imbued as design principles by the professionals responsible for their development and use. Therefore, the HI (human intelligence) + AI (artificial intelligence) paradigm is important, as HI provides supplemental cognitive capabilities that, combined with AI, provide for a more effective and robust overall solution.
The use of big data in machine-learning applications can comprise vast petabytes of data, and models may undergo numerous iterations before being finalized for client portfolio use. Firms need to ensure they establish an appropriate framework and accompanying systems to support record retention and data storage, including descriptions of datasets used, model specifications, and results from testing and deployment.
What specific recommendations do you have for investment management organizations planning to implement AI Technology?
The following principles apply to the ethical design, development, and deployment of AI in investment management:
Data integrity – Data needs to be checked and cleansed so that it is fit for use in an AI program, and firms must respect and adhere to data privacy laws and protections in the sourcing and use of data where applicable globally, especially where developers use unstructured and alternative data. Users must also be aware of existing biases to avoid discrimination against certain groups of people arising from the classification of incomplete or biased data training sets.
Accuracy – The AI application needs to be reliable and perform as intended. Professionals must also balance the complexity of models with the need for accuracy.
Transparency and Interpretability – The AI model should be comprehensible so that firm staff can interpret and explain to clients and supervisors when appropriate, and investment professionals need to understand the key features of any AI program that informs investment decisions.
Accountability – There needs to be sufficient human oversight, governance, and accountability mechanisms in place to ensure accurate and appropriate AI program outcomes while managing risks. Accountability begins with senior leadership establishing a strategic vision and ethical culture for AI development within an organization.
