Banks Can Convert Messy Data into Unstoppable Growth
By Suman Bhattacharyya, Contributor at The Financial Brand
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Executive Summary
- Banks can only realize AI’s potential when they clean up fragmented and unstructured data that currently undermines accuracy and trust.
- Establishing strong data governance, prioritizing high-impact use cases and choosing the right mix of in-house and partner solutions enable scalable progress.
- Financial institutions that turn unified data into real-time insights will unlock personalization, accelerate growth and secure long-term competitive advantage.
Banks are rushing to answer the call to adopt AI or risk falling behind. But for many financial institutions, the challenge is more fundamental than simply being ready: the data needed to feed AI systems is messy, fragmented and siloed. Without clean, integrated data, AI systems can’t deliver reliable results.
Across many companies, “there’s nobody owning the data, and nobody managing the data … sometimes the data is not up-to-date or complete,” says Mauricio Deutsch, senior vice president of banking and capital markets at GFT Technologies Canada. “If you don’t have the right input, the output won’t be very trustworthy.”
Industry research backs this up. In a survey of 509 financial-services technology and operations leaders conducted earlier this year, Broadridge Financial Solutions found that 47% of firms are dealing with data silos and 40% have data quality issues. Banks have grappled with data management for decades, Deutsch says, though the stakes are higher now, as the issue runs deeper than technology and is often a product of culture and decision making.
Banks recognize the potential in tapping a trove of customer data, much of it unstructured, as a tool to personalize interactions and become more proactive. They are sitting on a goldmine of unstructured information hidden in PDFs, scanned forms, call notes and emails — data that, once cleaned and organized, can unlock new business opportunities, says Drew Singer, head of product at Middesk. To achieve business impact, banks and credit unions need to start with a clear data governance structure, act on what works, and scale the model. Here are four practical steps experts say can help them get there.
Want more insights like these? Check out MX’s content hub: Data in Action
Start With the Problem You’re Trying to Solve
The process starts with defining the data strategy. Banks often talk about data being a critical asset, but they often struggle to determine where to focus.
A starting point, says BankTech Ventures Managing Director Carey Ransom, is to begin asking the right questions and then zero in on high-impact use cases. For many FIs, customer sentiment data spread out across touchpoints is a good place to start: it’s rich in insights but is often disconnected and underused.
“You peel the onion until you get to the details,” says Deutsch. “Are we talking about customer complaints, or is it that I don’t have the intelligence to sell more to the client?”
In 2023, Truist set out to build Truist Client Pulse, a real-time customer insights platform designed to go beyond surveys and feedback collected across disconnected systems, which can be lagging indicators and offer only a partial view of the client experience. By tapping into more real-time signals, Truist sought to understand how customers were feeling in the moment.
The goal was to gain a horizontal view of customer sentiment data across call transcripts, surveys and other feedback channels and equip teams with insights they can act on immediately, explains Sherry Graziano, head of digital, client Experience and marketing at Truist.
“We have surveys from clients, social media insights, call conversations and digital engagement,” Graziano says. “Bringing all that data into a place that we truly would have real time insights and then be able to act upon them and take action…that was at the core of the problem to be solved.”
Truist Client Pulse comprises two components: an insights tool based on client conversations across channels, and the Truist Client Pulse Score, an indicator of client sentiment across client segments, channels and products. The insights tool is currently in use by select teams, while the Client Pulse Score has not yet been rolled out organization-wide.
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Decide to Build or Partner
Truist opted to develop its Client Pulse platform in-house — the bank earlier this year reported cost avoidance of up to $10 million. But other FIs may need to partner with third-party fintechs to advance their data strategies.
Glenn Kurban, a partner at Capco, notes that smaller institutions may not be at the point of maturity to build internally. Meanwhile, he notes that some larger FIs don’t always have the niche skills available to build products themselves.
Many FIs lack the data scientists to manage daily data workloads that come from multiple sources — such as account holders’ transactions and digital banking activity — streams that are often riddled with errors and inconsistencies, says Loni Luna, senior product marketing manager at Alkami.
The decision to build or partner, Singer says, depends on four factors: its consistency with the institution’s digital modernization strategy, the financial institution’s data governance and risk framework, ease of technical integration, and, if the FI decides to partner, the fintech’s track record and cultural fit.
Develop a Clear Data Governance Approach — With “Toll Gates” Along the Way
The ability to successfully turn data into insights often depends on clear parameters for how data is handled. This includes a shared understanding of who owns the data, how it will be managed and stored, and a defined governance structure — possibly through committees — for overseeing its use, Deutsch says.
“If you don’t set these rules, once data starts flowing, you will lose control of it. You will most likely lose quality,” he says.
At Truist, for example, the bank has set up a data governance structure that includes multiple working groups and approval processes.
“You aren’t going to go from … an idea to driving to production at scale without moving through multiple toll gates along the process,” says Graziano. “Some of that is looking at … the data quality, the inputs, the outputs, pre-production, post-production [and] a lot of rigorous testing.”
Turn Insights into Action and Champion Internal Adoption
With the data governance structure firmly in place, FIs are positioned to use additional tools to garner action-oriented insights across the organization. Truist Client Pulse, for example, uses AI and machine learning to analyze customer feedback across channels. It’s currently used by product teams, digital product owners and by field leaders, says Graziano.
“We’ve got a population of teammates using the tool as it stands today, to better understand regional performance opportunities …what’s going well with certain solutions that we have, and where there are areas of opportunity to enhance experience and elevate satisfaction to drive to client loyalty,” says Graziano. The bank’s Client Pulse platform analyzes more than 30 million client interactions across channels.
Smaller institutions are also finding new ways to turn data into action. Meritrust Credit Union, for example, used Alkami’s marketing automation platform to identify target audiences in under an hour, a process that once took weeks. The result is the ability to enable data-driven cross-selling and personalized banking offers, according to Alkami.
Once FIs establish frameworks to manage their data, AI can streamline what comes next, by automating analysis, revealing patterns, and showing insights faster than manual processes ever could, analysts say. Looking ahead, Mazen Letayf, Alkami’s vice president of customer lifecycle management and data solutions, is confident that AI will help banks make sense of unstructured data and turn it into actionable insights.
“AI will convert unstructured data — behaviors, clicks, content interactions — into richer audience understanding, powering predictive models and lookalike algorithms that automatically surface high‑value prospects and cross‑sell candidates,” he says.
