Ask an expert: How to build a robust data foundation in financial services

Q: What does a strong data foundation look like in financial services?

What I've seen successful leaders do in planning their data foundation is start at the business outcomes that they're trying to drive. They then base the data domains that get pulled into their data foundation on those business outcomes.

I’ve seen data foundations fail when they're built completely horizontally across the business. Trying to boil the ocean of data and build a foundation that works for everyone all at once usually doesn't work. The most successful leaders usually focus on a vertical slice of their data foundations—linking it to those outcomes that matter most. From there, once you've got a good solid example, then you can start to build out more horizontally. This means ‌that a foundation is going to look different in each financial services firm based on what those outcomes are.

Q: What are the unique considerations for financial services leaders when it comes to building their data strategy?

Financial services is an interesting space, because organizations are making financial recommendations, and to do that they need to have extremely high confidence that they are making the correct recommendations. So the need for human validation is greater than in other industries and, what’s more, it is a highly regulated space. Financial services firms need to offer a lot of explainability around how, for example, this person gets a higher credit limit than this one. They need to be able to justify these types of decisions to regulators.

These companies are also dealing with privacy concerns. They have to deal with data in a very secure and robust way to make sure they are respecting the privacy of their users. Because there is so much risk around offering data and AI-driven recommendations, we often see financial services firms building out AI or ML models for internal use or for human-in-the-loop use. These internal use cases become the primary focus rather than having insights or recommendations generated by AI and ML directly exposed to customers.

Q: Do you think we will start to see financial services firms extending these AI and ML tools for customer use?

A: It’s a journey. I think as we get more financial services firms building out their foundations and they’re able to generate more confident, validated, explainable insights, there will be opportunities to expose more and more of that to their customers. I think the priority right now is to use AI and ML to scale employees, to make them more productive, and to increase their bandwidth, giving them knowledge to service customers better and faster. That’s the more common business case that we see. For example, using data to power dashboards that a wealth manager or customer services rep can use to see recommendations, filter them, and advise the customer better. My colleague Sasha explored the real-world use cases that demonstrate the potential of AI in her recent blog post.

Q: How important is the quality and structure of your data foundation when it comes to implementing AI and ML tools?

A: It’s critical. The more we’re talking about making recommendations—especially numbers-based recommendations—the more important it is that you have a data foundation composed of trustworthy, harmonized data. There are some sectors and areas where, if your data isn’t quite right, there is more flexibility and less risk.

In the space of advising someone on what to do with their life savings, you need a very justifiable, explainable recommendation. Your data foundation has to be incredibly robust. And again, the more you’re building something that goes directly to the customer, the more risk that you have. The more that you're building something ‌that goes to an employee for review, the less risk that you have. And we'll slowly move along that spectrum over time with more direct exposure to the customer.