All code details in Jupyter Notebook
Interviews, Competitive Testing, RITE
I developed a Data Quality Indicator (DQI), grounded in Fogg's Behaviour Model and the Personal Data Literacies Framework, to motivate customers to review and update their personal details (phone number, address, email) within their banking app while educating them on their personal data, its uses, and the benefits of updated information
The research employed an iterative design process, including interviews, value evaluations codesign, preference testing, and Rapid Iterative Testing and Evaluation (RITE), with participants including non-Triodos customers, Triodos employees, and one Triodos customer.
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
Goal: to improve customer data quality at Triodos Bank, initially focusing on internal processes, and later expanding to customer involvement with epected benefits for Triodos (regulatory compliance, operational efficiency, reduced employee effort) and for customers (secure communication, privacy
The DQI prototype evolved through several iterations based on participant feedback, focusing on visual clarity (e.g., progress bars), clear explanations, and user-aligned terminology. Machine learning approaches for data validation were also explored, with "Lookup with FuzzyMatching" selected for its accuracy and efficiency.