The rise of intelligent Conversational AI has transformed how people interact with technology. Powered by breakthroughs in Deep Learning, Big Data, and Natural Language Processing (NLP), assistants such as Amazon Alexa, Apple’s Siri, and Google Assistant have achieved remarkable mainstream success. This progress has sparked growing interest in applying conversational technologies across various sectors, particularly in financial services. Conversational User Interfaces (CUI) are increasingly regarded as a promising pathway toward more natural and accessible human-computer interaction, with some researchers speculating that they could become the dominant universal user interface in the near future.

In the mobile banking sector, which has become the preferred channel for financial services, Conversational Virtual Assistants (CVAs) offer substantial benefits. Powered by AI, Speech Recognition, and NLP, tools like Erica (Bank of America), Eno (Capital One), and others deliver personalised insights, transaction support, account information, and 24/7 assistance. These assistants enhance convenience while enabling banks to reduce operational costs and improve customer engagement. However, many current implementations remain generic and lack sufficient personalisation.

A growing body of research emphasises that customisation and personalisation — particularly of personality, voice, language, and interaction style — significantly boost user satisfaction, emotional connection, trust, and adoption of CVAs. Despite this evidence, banking CVAs often rely on fixed, predefined settings with limited customisation options. This gap underscores the need for user-centred research to better understand diverse user needs and preferences, paving the way for more tailored and effective CVA design in mobile banking.

How can the CVAs in mobile banking be designed to create a more personalised and convenient user experience?

In line with the secondary research, Qualitative research findings highlighted the importance of CVA customisation and personalization. Quantitative data demonstrates that research participants’ preferences differ significantly based on the user personality type

The research findings were translated into an interactive CVA prototype developed in Adobe XD. This artefact effectively demonstrates the practical application of the user-centred design framework by visually communicating key customisation features and user interaction flows. Adobe XD was chosen for its strong support of voice interactions, which aligns closely with the core focus of the project 

To evaluate the effectiveness of an AI-driven gamification approach, I developed two concepts of the savings feature prototype to compare user experience and satisfaction with each one.

Concept A utilizes a traditional gamification approach, incorporating familiar elements such as savings goals and a visualized progress tracker.

Concept B delivers a personalized, automated, and engaging savings experience tailored to user needs. It leverages AI technology to enable dynamic personalization and adaptive features.

Two concepts of the savings feature prototype were tested with a total of 54 randomly selected participants using an unmoderated prototype testing method, followed by satisfaction surveys. Quantitative data from the surveys was analyzed using the t-test statistical method, revealing that participants who tested Concept B demonstrated significantly higher satisfaction and engagement levels compared to those who evaluated Concept A. 

Users reported greater satisfaction and engagement with the savings feature prototype that employed personalized adaptive gamification compared to the prototype that used a non-personalized, non-adaptive approach. This research finding underscores the effectiveness of the AI-driven gamification approach in enhancing user satisfaction and engagement within personal finance management apps.