Summary

Poor customer data poses regulatory and operational risks to banks and privacy and security risks to customers. I developed a Data Quality Indicator (DQI), grounded in Fogg's Behaviour Model, to motivate customers to update their personal information, reducing employee effort in data remediation. The research employed an iterative process, including codesign, preference testing, value evaluations, and Rapid Iterative Testing and Evaluation (RITE).

Participants included non-Triodos customers (19) and Triodos employees (2) and customers (1). Findings indicate generally positive sentiment towards the DQI and some privacy concerns. Participants confirmed key values were (sustainability, efficiency, simplicity, and transparency) successfully integrated in the DQI design and development.

The DQI is expected to effectively prompt Triodos bank customers to review their personal information for accuracy, completeness, and up-to-dateness.

Figma | GitHub

Problem

Poor customer data in banking leads to regulatory and operational risks for banks, and privacy and security risks for customers. Manual data remediation is costly and sometimes mistaken for phishing scams by customers

Triodos Challenges

Triodos Bank, a pioneer in sustainable banking with operations across five European countries, faces a significant challenge in maintaining high-quality customer data. While newer customer onboarding processes incorporated automated validation, there are still substantial issues with historical data.

Key Issues:

• Existing tools primarily checked for data completeness, not accuracy (e.g., postcode-city mismatches in address data).
• Additional data checks were manual, inefficient, and time-consuming.
• Customers were not consistently updating their information, leading to undelivered emails and letters, and security risks (e.g., debit cards sent to old addresses).
• Inability to validate existing data.

Project Goal & Research Questions

The project's overarching goal was to address customer data quality issues by exploring solutions for the bank's internal processes.

Initial research questions focused on understanding the causes, detection, and resolution of customer data quality issues at Triodos, identifying stakeholders and their roles, pain points in the current approach, and key considerations for a data quality assessment tool.

During discovery, it was clear that the initial focus largely ignored the customer's pivotal role in data quality. Customers often overlooked or ignored in-app prompts to update their data, revealing a significant motivational gap. This insight led to a critical evolution of the problem statement: "How might we encourage customers to update their data?" This reframed the challenge from a purely internal remediation task to one that actively involved and empowered the customer.