Regulators are increasingly aware of the disruptive impact and security threats that weak data governance (DG) and data management (DM) practices pose in the investment industry. Many investment firms are failing to develop comprehensive DG and DM frameworks to keep up with their ambitious plans to leverage new technologies such as machine learning and artificial intelligence (AI). It is crucial for the industry to establish legal and ethical guidelines for the use of data and AI tools. A collaborative dialogue between regulators and the financial industry at both national and international levels is necessary to define these standards.
Steps Towards Data Efficiency and Effectiveness
First, set clear and measurable goals for the short, medium, and long terms. Next, create an initial timeline that breaks down the effort into manageable phases. Start with a few small pilot initiatives to kick things off. Without specific targets and deadlines, the responsibility for data governance and management might fall back solely on the IT department, leading to a lack of progress.
Starting with a clear vision that includes milestones and deadlines is essential. Consider the approach to meeting those deadlines as you define and establish the DG and DM processes. Future-proofing systems, processes, and results is key. Ensure that data definitions, procedures, and decision-making policies align with the overall company strategy. Strong commitment from management, team involvement, and customer engagement are also crucial components.
Successful DG and DM initiatives often involve a T-shaped team approach, which combines business leadership with interdisciplinary technology teams that include data science professionals. Setting realistic expectations and demonstrating achievements are vital, as DG and DM frameworks cannot be established overnight.
Why are DG and DM Important in Financial Services?
Turning data into accurate, forward-looking, and actionable insights is crucial for investment professionals. Information asymmetry is a significant profit source in financial services, and AI-backed pattern recognition can provide insights from diverse data sources. Data and analytics play a vital role in regulatory compliance within the heavily regulated financial industry.
While sophisticated data and AI models are valuable, ensuring that data and models are “human-meaningful” is essential for user perception and decision-making. Transparency, interpretability, and accountability are key aspects of AI models to prevent biased results and unethical practices.
Data- and AI-Driven Initiatives in Financial Services
As financial services increasingly rely on data and AI, it becomes important to focus on DG and DM to address various challenges. Defining problems and goals is critical, as not all issues are suitable for AI solutions. Transparency, interpretability, and accountability are needed to prevent systemic risks and ensure regulatory compliance.
Human oversight and involvement in AI modeling are essential for feature capturing and maintaining ethical practices in financial services. Technical capabilities are required for supervision and intervention in data and AI-based systems to ensure explainability and auditability.
The Growing Risks
Firms must enhance their DG & DM frameworks to leverage opportunities and mitigate risks associated with the increasing volume and variety of data and AI-backed analytics. Proper controls and adherence to legal and ethical standards are crucial.
The use of big data and AI technologies is not limited to large financial institutions, as smaller firms also have access to data aggregators and cloud service providers. However, the widespread use of similar data and AI models could lead to market risks, herding behavior, and potential collusion without human intervention.
Explanatory and reproducible AI models are essential to mitigate risks associated with big data use in financial services. Transparency, explainability, and interpretability must be prioritized to ensure compliance with existing regulations and frameworks in the industry.
Adopting a proactive approach to DG and DM in financial services is crucial to manage risks and maximize the benefits of data and AI technologies. Collaboration between stakeholders and regulatory bodies is necessary to establish standards and guidelines for ethical and legal use of data and AI tools.
References
Larry Cao, CFA, CFA Institute (2019), AI Pioneers in Investment Management, https://www.cfainstitute.org/en/research/industry-research/ai-pioneers-in-investment-management
