Navigating Financial Systems: Addressing Digital Banking Barriers for East Asian Immigrants in Canada
Bibliographic record
Abstract
This research explores the mobile banking experiences of Mandarin-speaking newcomers in Toronto, with a focus on the role of in-app chatbots in supporting or hindering their financial interactions. Using an inclusive design research approach, the study combined semi-structured interviews and a participatory co-design session to identify usability challenges and explore design opportunities. Thematic analysis of five interviews revealed four major pain points: uncertainty about chatbot capacity, confusion caused by irrelevant information, cognitive overload from complex app interfaces, and a lack of support in understanding Canada’s credit system. These themes guided a co-design workshop involving seven participants who contributed reflective feedback and proposed improvements to current user experience designs. Findings suggest that the information architecture of the current banking app is overwhelming for newcomers, and the information is often overflowed. Also, while chatbots hold potential to streamline banking workflows, their current implementations often fall short due to generic responses, poor contextual understanding, and lack of multilingual support. The research highlights the need for more transparent, culturally responsive, and linguistically inclusive design strategies. The resulting prototype proposes a simplified information architecture and a context-aware chatbot flow that together aim to enhance user confidence and financial autonomy among Mandarin-speaking newcomers. These insights contribute to the broader discourse on inclusive fintech design and call for deeper engagement with marginalized users in the development of digital financial services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".