Artificial intelligence (AI) is rapidly transforming industries, and financial services is no exception. From portfolio management to customer service, AI has the potential to revolutionize how financial firms operate. However, many organizations face a significant obstacle: the lack of a solid foundation in data and investment analytics. Without addressing these foundational issues, AI initiatives often fail to deliver the promised value.

The Data Silo Dilemma

Consider an investment management firm aiming to leverage AI to generate automated commentary on their investment products. Ideally, such a system would seamlessly pull data from various sources, analyze it, and produce insightful commentary tailored to different asset classes. However, in reality, the data required to support equity investment commentary often resides in a separate system from the data for fixed-income products. This siloed architecture leads to inefficiencies, data inconsistencies, and a fragmented view of the firm's investment universe.

A similar challenge arises for pension funds. One client of ours envisioned using AI to streamline the process of writing portfolio commentary and finding relevant news articles. But this initiative was hindered by the fact that performance data, risk metrics, and private equity information were scattered across different systems. For the AI to work effectively, it required seamless access to these disparate data sets—something the current infrastructure couldn't provide.

Insufficient Analytics: The Case of a Financial Custodian

The challenges don’t stop at data silos. A prospective financial custodian client wanted to use AI to provide personalized investment commentary for their clients’ portfolios. Their goal was to enhance client reporting with relevant insights tailored to each portfolio. However, during the discovery phase, they realized that they lacked a sufficiently broad set of investment analytics to support this initiative.

Without key analytics such as risk attribution, style analysis, and multi-asset performance metrics, the AI system couldn’t generate meaningful or actionable commentary. This led the custodian to conclude that before implementing AI, they first needed to expand their investment analytics capabilities. Only then could they provide the AI with a rich enough dataset to work from and deliver insightful portfolio commentary.

Why Solving the Data and Analytics Problem Is Critical

Before firms can unlock the full potential of AI, they need a comprehensive data and analytics strategy. This involves more than just aggregating data; it requires addressing several key dimensions:

1. Automation for Reliability and Key-Man Risk Reduction

Financial firms often rely on manual data aggregation processes, which are prone to human error and inefficiency. Automating these processes ensures that data is not only accurate but also reduces the risk associated with the dependency on specific individuals.

2. Integrated Systems for Seamless Operations

To fully leverage AI, data must flow seamlessly between different systems. An integrated approach allows firms to break down silos, enabling comprehensive reporting, ad-hoc analysis, and robust AI capabilities. Without integration, AI models will struggle to produce meaningful insights due to incomplete or inconsistent data.

3. Data Validation for Consistency and Reliability

AI systems are only as good as the data they consume. If the input data is inconsistent or inaccurate, the outputs will be equally flawed. Incorporating rigorous data validation processes ensures that the data fed into AI models is both reliable and actionable.

4. Auditability for Strong Data Governance

In an era of increasing regulatory scrutiny, firms must ensure that their data processes are auditable. This is particularly important when AI-driven insights are used to make investment decisions. A robust audit trail provides transparency and accountability, helping firms maintain trust with stakeholders and regulators alike.

5. Flexibility for Future Growth

The financial landscape is constantly evolving, with new data sources and service providers emerging regularly. A flexible data architecture allows firms to easily incorporate these new inputs, ensuring that their AI capabilities remain cutting-edge.

6. Broader Investment Analytics for Richer Insights

As seen in the case of the financial custodian, having a robust and comprehensive set of investment analytics is crucial. Without this, AI systems lack the depth required to generate high-quality insights. Expanding analytics capabilities ensures that AI models can deliver more nuanced and valuable commentary.

Building the Right Foundation

Solving the data and investment analytics problem is not a one-time project; it is an ongoing journey. Firms need to think strategically about their data architecture, ensuring it is robust enough to support current needs while remaining adaptable for future advancements.

At FinMason, we have seen firsthand how addressing these foundational issues can unlock the true potential of AI. By implementing a comprehensive data strategy, our clients have been able to achieve greater efficiency, deeper insights, and, ultimately, better outcomes for their stakeholders.

Conclusion

AI promises to revolutionize finance, but it cannot succeed without the right data infrastructure and investment analytics. Firms must prioritize solving these foundational challenges before embarking on ambitious AI projects. By doing so, they will not only maximize the value of their AI investments but also build a more resilient and future-ready organization.

About the Author

Philip J. Taylor, CFA, is the President and Chief Analytics Officer of FinMason, Inc., a leading provider of investment analytics solutions. With extensive experience in the financial services industry, Philip is passionate about helping firms unlock the power of data and analytics to drive innovation and performance.