business intelligence

How business intelligence is used in finance and banking

Recent news of bank bailouts have many remembering 2008—a time when confidence in the financial sector was at an all time low. But even before this news broke, confidence in the financial sector was declining. In 2022, J.D. Power reported that consumer trust in retail bank advice had fallen by 30 points from 2021—the first major decline in recent years. 

Rebuilding trust in the financial system requires personalized customer service, advice, and guidance from financial institutions. That same J.D. Power report revealed that customers who receive personalized advice have 16% higher satisfaction when compared to those who received generic advice. In an industry built on life-long relationships, a 16% bump in satisfaction is statistically significant.

Creating a customer relationship with this level of customization requires a lot of data—and more importantly, the ability to work with that data to derive detailed insights. To achieve this, your data must be: 

  • Accessible and reliable

  • Handled responsibly 

  • Built into the culture 

We spoke to data leaders at some of the top financial firms, to find out how they’re navigating the choppy waters of consumer demand using top-of-the-line business intelligence. Here’s what they shared with us. 

The role of data is to enable the business

In finance, there can be a tendency to overemphasize absolute accuracy in all data. After all, in highly regulated markets, flawless information is critical for data-driven decision making! However, as Cara Dailey, EVP and CDO for LPL Financial, points out, sometimes you’re using the data simply to point the way forward. Dailey explains that, when it comes to the level of data accuracy, “not all data is created equal.” 

It depends on the use case. If you’re working on improving an internal product or process, directional indicators may be good enough. To keep up with the pace of innovation and competition in the finance sector, it’s crucial not to let yourselves get so bogged down in data purity that you can’t make data-led business decisions. 

In the end, the purpose of business intelligence data is to enable business performance. To quote JoAnn Stonier, the Chief Data Officer for Mastercard, “The role of the CDO is to engage the business today in tomorrow’s business.” In other words, implementing data governance, management, and quality are just table stakes for today’s financial institutions. You need to proactively use that data to identify opportunities for future value creation. 

Data must be handled with care 

That said, you can’t be too careful with your data when it comes to banking and finance. As Stonier puts it, “If you have trust, you have a sustainable business going forward. If you're not going to have trustworthy practices of every kind, you're just not going to have a sustainable business.”

Stonier believes that her role at Mastercard is to step back and consider the impact that the data products, solutions, and services can have on real people—both positive and negative. She aims to apply “individually centric design principles” to every aspect of her work.

When it comes to data governance, Julien Molez, the Innovation Data & AI Leader at Société Générale, urges financial data leaders to err on the side of caution. He explains that Société Générale has built compliance and governance into the data architecture at every level, to protect individuals from any risk to their data security. 

For instance, accessing cloud software (even something as seemingly harmless as Google Docs) or sharing files by email from within the bank’s servers is completely impossible. It creates headaches, for sure—but it protects the bank from any risk of accidental data leaks. 

“What it means to be a trusted third party when managing such sensitive data—applying the strictest rules of processing at every stage, even when it slows down your ability to process them.” —Julien Molez, the Innovation Data & AI Leader at Société Générale

Data leaders must balance offense and defense 

The tension in the finance sector is how to balance the necessary levels of data governance with the evolving business need to harness that data to drive decisions or create a personalized customer experience. At LPL Financial, Dailey has championed the idea that she’s not just there to protect the organization's data. She also asks herself how her team can actively contribute to LPL’s objectives. 

“You need to ask yourselves, ‘Is this aligned to our privacy strategy?’ You have to make sure you’re not violating regulations. But I think you can create the right guardrails and still offer a personalized experience to your customers. That's both an offense and a defense strategy.” —Cara Dailey, EVP and CDO for LPL Financial

Julien Molez agrees. The new fintechs entering the financial sector have transformed consumer expectations, he explains: 

“We saw the different fintechs and big techs entering the financial services space, and becoming competitors. We saw that these competitors were leveraging data in their client value proposals. So we knew we also had to adapt.”  —Julien Molez, the Innovation Data & AI Leader at Société Générale

For Molez and the data team at Société Générale, it’s also about taking the offensive, leveraging data to “bring more added value to our different processes, and especially client processes.” 

To be data-driven, you need to build a data culture 

Kjersten Moody knows a thing or two about driving cultural change. As the first Chief Data Officer at Prudential Financial, she has turned a new position into an opportunity to deliver transformation across the entire organization. Her advice? If you want to become a truly data-driven organization, then data needs to become a part of your cultural currency.

“​​Data must be part of a comprehensive, unified strategy, where all relevant parties understand this data as a baseline to drive change.” —Kjersten Moody, Chief Data Officer at Prudential Financial

Moody goes beyond simply creating and distributing financial data visualizations throughout the organization. Instead, she has positioned the data function of the organization as part of the overall business change structure, “bringing together all the components needed to deliver that business impact from data science, software engineering, risk compliance, [and] the HR dimension.” 

One advantage that Moody perceives is that the culture at Prudential is already one of “collaboration and partnership.” This has made it easier for her team to secure buy-in at a cross-company level, even when working through very serious questions and challenges:

“There's a consistency to how the different roles and the different seats at the table in that conversation are approaching that same conversation together.” —Kjersten Moody, Chief Data Officer at Prudential Financial

For Molez, the best approach is a “co-construction in the design of the value framework and of the platform.” He recommends that data leaders avoid a purely top-down model, but instead investing time upfront to understand the constraints and challenges of each group of stakeholders.

The process Molez described enables data teams to create an environment where they can operate freely within previously agreed boundaries—making your data function can be agile without violating the rules around data protection in the financial sector.   

Data has to evolve with your business 

Of course, even after you’ve led an initial overhaul of your business analytics function, it’s never really done, cautions Pascale Hutz, the Chief Data Officer at American Express. 

“I often get asked, ‘Well, when will we be done with this data migration?’ And I'm like, ‘We're never really done… and we shouldn't look at it as a failure or anything like that. It's just an evolving system that will keep growing and changing and transforming.’” —Pascale Hutz, the Chief Data Officer at American Express

Instead of thinking of data as a platform that you can set and forget, Hutz prefers to see data as a product–or even as a “living, breathing organism.” When building a data infrastructure for modern analytics, the goal is to evolve your approach to analytics as your industry and your organization evolves. A key part of that evolution is to be proactive about decommissioning legacy systems—never easy in the notoriously conservative financial sector.

“When you leave these legacy systems lying around, somebody's maintaining them or worse yet somebody's not maintaining them and then… bad things are happening.”  —Pascale Hutz, the Chief Data Officer at American Express

Her recommendation? Adopt the mindset that you are “decommissioning the past to make way for the future.” 

Driving change in banking and finance with business intelligence

When it comes to the finance industry, data can play a transformative role. With the right BI and analytics systems in place, you can harness self-service analytics to supply faster answers, personalize the customer experience, upsell opportunities, and optimize your operations. But you’ll need a modern BI and analytics stack to protect your sensitive data while allowing business users to derive insights quickly and easily. 

ThoughtSpot can help. Our self-service, AI-Powered Analytics can help financial businesses uncover critical insights to create real business value, all while providing the enterprise-grade data governance and security built to protect your cloud data from exposure to risk. 

Sign up for a ThoughtSpot free trial to put the ‘custom’ back into your customer relationships.