Why Your AI Strategy Is Solving the Wrong Problem
ยท FinMason
AI is being implemented throughout the financial services sector, in automated trading algorithms, machine learning for stock picking, and natural language processing for reporting. There is significant promise, but there are also serious implementation issues. In practice, most firms are trying to use AI to solve problems they haven't properly defined and are often using messy data.
The Foundation Problem
Here is an example: an investment management firm that wanted to use AI to generate automated commentary on its funds sought personalized insights for every portfolio. The technology is available, and the use case is clear.
The challenge was that their performance data was in one system, risk metrics in another, and private equity information in yet another. They wanted AI to create insights from data it couldn't access comprehensively.
This isn't unique. We see it regularly: Asset managers are rushing to implement AI solutions while their fundamental data infrastructure remains fragmented and inconsistent.
The Analytics Gap
Another example is a financial custodian that sought to use AI to deliver personalized investment commentary for its clients' portfolios. The goal was to enhance client reporting with relevant, tailored insights. During the discovery phase, it became clear they lacked a sufficiently broad set of investment analytics to support this initiative.
Without key analytics like risk attribution, style analysis, and multi-asset performance metrics, the AI system couldn't generate meaningful commentary. The custodian concluded 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 deliver genuine value.
First Steps
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 interconnected challenges.
First, there's automation. Financial firms often rely on manual data aggregation processes, which are prone to human error and create key-person risk. When critical knowledge resides in a single analyst's head and their personal spreadsheets, that's a vulnerability. Automating these processes ensures data accuracy and reduces dependence on specific individuals.
Then there's integration. For AI to work, data must flow seamlessly between different systems. An integrated approach breaks down silos and enables comprehensive reporting, ad-hoc analysis, and robust AI capabilities. Without integration, AI models will struggle to produce meaningful insights because they're working with incomplete or inconsistent information.
AI systems are only as good as the data they consume. If the input is inconsistent or inaccurate, the outputs will be equally flawed. Rigorous validation processes ensure that the data fed into AI models is reliable and actionable.
Auditability is increasingly important. In an era of heightened regulatory scrutiny, firms must ensure their data processes are transparent and accountable. This is particularly critical when AI-driven insights inform investment decisions. A robust audit trail maintains trust with stakeholders and regulators.
And finally, flexibility. The financial landscape constantly evolves, with new data sources and service providers emerging regularly. Flexible architecture allows firms to incorporate these new inputs without major overhauls, ensuring AI capabilities remain current.
The Real Opportunity
The real opportunity with AI is the ability to augment human judgment with complete, accurate information. That requires solving the data management problem first.
The firms that will succeed with AI will resist the temptation to jump straight to the exciting stuff and instead focus on building proper data infrastructure first. This isn't about being conservative or slow; it's about being strategic. The organizations that get their data house in order now will be positioned to move quickly when the right AI applications emerge.
The future belongs to firms that understand AI is only as intelligent as the data it can access.
FinMason helps investment firms build the data and analytics foundation that enables AI. Our clients have achieved greater efficiency, deeper insights, and better outcomes by first addressing these foundational challenges. To learn more, visit finmason.com.