Using Anonymized First-Party Financial Transactions Data to Generate Personas

Every grocery run, utility payment, or late-night food order leaves behind a tiny clue about how someone lives, what they worry about, and what they value. The same logic applies to a company’s customers; their financial behaviors quietly reveal what they need and how they make decisions.

For a UK fintech operating in the consumer finance space, those clues were already there, just hidden inside an anonymized open-banking dataset of tens of thousands of customers. The challenge was turning that raw transaction data into something the business could actually use.

Follow along to see how this fintech company used Customer Persona by Delve AI to transform its anonymized first-party transactions and balances data into actionable customer personas, without sharing any personally identifiable customer information.

About the Company

The client is a UK-based fintech company, a responsible alternative to high-cost short-term credit. Its mission is to make borrowing fairer, more transparent, and more accessible for people who are often underserved by normal banks.

The company operates fully online and uses open-banking connections to understand customers’ financial behavior, assess affordability, and structure repayments. Over time, this created a large pool of anonymized transaction data, which was rich in behavioral signals but difficult to interpret at scale.

With a growing customer base and a strong focus on compliance and privacy, the team wanted a better way to understand who their customers were and how to effectively communicate with them.

The Problem

Like many fintech teams, they weren’t short on data. They were short on clarity. The company had access to a large volume of transaction data, but turning that information into meaningful customer insight was still a challenge.

Most business decisions were guided by internal assumptions and qualitative samples. While these inputs were helpful, they didn’t fully reflect how customers actually behaved with money in their day-to-day lives.

Customer segmentation was also fairly basic. People were grouped by credit bands or repayment behavior, which worked for risk assessment but didn’t explain motivations, financial stress patterns, or communication preferences.

On top of that, the company had strict privacy and compliance requirements. All transaction data was deliberately anonymized, and any persona-building solution had to work without personal identifiers, email addresses, names, or CRM records.

The Solution

To resolve their problems and get clarity on different customer segments, the team signed up for Customer Persona by Delve AI.

Customer Persona is an AI-powered persona generator that creates data-driven personas for brands by analyzing their first-party data (CRM data, e-commerce, or transaction data) and enriching it with publicly available information, like Voice of the Customer data (reviews, ratings, community forums, blogs, and news).

Users can either connect their CRM or marketing automation platform (HubSpot, Salesforce, Klaviyo) or upload a CSV file with their customer data. In this case, the fintech company took the CSV upload route. They uploaded a single anonymized file containing transaction-level data for tens of thousands of customers.

The interesting bit? The CSV file didn’t include any personally identifiable information, no names, no email addresses, just behavioral data.

Implementation

So how did the fintech company actually go from a spreadsheet full of transactions to detailed, usable customer personas? They kept it simple; uploaded their anonymized data into Delve AI, and let the platform do the heavy lifting.

Delve AI, for its part, leveraging the context and patterns gained from creating synthetic personas for tens of thousands of brands of varying sizes across the world over the past 5+ years, automatically generated multiple customer segments, with each persona offering a deep, structured view of a specific customer type. For each segment, the team received detailed insights, starting with Persona details, such as:

• Customer goals, challenges, and key needs
• Aspirations, factors influencing purchase decisions, and buying triggers
• Lifestyle, interests, values, and hobbies
• Personality traits
• Preferences (news, sports, travel, music, and television)
• Influential resources, media/news sources, brands, websites, and social networks
• Preferred content types
• Resonating products and topics

In addition to these details, Delve AI also offered industry-specific insights for each segment. These are structured industry-specific attributes derived from behaviors and purchases.

Together, these signals gave the fintech company a clearer picture of how different personas interacted with the broader consumer finance and lending ecosystem, not just with the brand itself.

The second, Distribution tab showed how each persona segment was spread across five dimensions: how, who, where, when, and what. This included data on device types, marketing channels, age/gender, locations, time-of-day activity, and the topics that resonated most with each segment.

The visual breakdown made it easy to spot behavioral patterns and engagement peaks at a glance.

Furthermore, the Influencers section listed the individuals and organizations each customer group followed online. For each source of influence, the team could see a short description, plus their website and social media channels, making it easier to spot partnership and influencer marketing opportunities.

Alongside all of this, the fintech company could also view likely Journey maps for each persona segment to see how buyers typically discovered and interacted with the brand.

The Results

Since the personas were built on real transaction behavior, they carried far more credibility than traditional customer profiles.

Thus, the fintech company was able to:

• Gain a clearer picture of who their most valuable customer segments really are.
• Align marketing, product, and CX teams around shared, data-driven customer archetypes.
• Improve targeting and messaging relevance without compromising privacy.

Most importantly, the entire process worked without connecting to a CRM or breaking internal compliance rules.

Final Thoughts

In regulated industries such as financial services and healthcare, in-depth customer personas can be derived using anonymized data. With clear, actionable customer personas built from real transaction data, the fintech client was able to move away from guesswork and toward real insight. It created a strong foundation for smarter marketing and product decisions, along with empathetic customer interactions.

Want to see what your own CRM, e-commerce, or transaction data can reveal about your customers? Get started with Customer Persona by Delve AI today.