Behind the AI-Scenes: How FinTech Professionals Navigate Regulations and Privacy Concerns to Enhance User Experience
Bibliographic record
Abstract
By 2030, global financial technology (fintech) revenues are expected to surpass $1.5 trillion US dollars, driven by the increasing adoption of digital financial services worldwide (eMarketer, 2023). The rapid advancement of artificial intelligence (AI) has significantly contributed to the fintech industry thrust (Kasmon et al., 2024) which in turn, has radically transformed the financial sector (Sahabuddin et al., 2023; Jang et al., 2021). The literature has established that fintech can be effective in improving how customers experience financial service and product offers (Gupta et al., 2023). Despite the hype over fintech technologies, the successful design and development of fintech solutions is still a challenge for many B2B and B2C businesses (Kasmon et al.,2024), and even more so because ethical and regulation requirements for user protection are key to adoption (Israfilzade and Sadili, 2024; Heeks et al., 2023). Fintech professionals involved with product management play a crucial role as intermediaries between developers and clients in the successful implementation of such digital innovations (Jang et al., 2021; Mogaji and Nguyen, 2021). While financial interactions involve a great deal of sensitive information sharing (e.g., credit card, account number, investments), users become increasingly vulnerable and concerned with their privacy when using fintech applications (Rjoub et al., 2023; Siddik et al., 2023). Several studies have examined the consumer’s perspective when adopting fintech products or services, but very few have investigated the perspective of fintech professionals (Hassan et al., 2023). This research aims to better guide fintech professionals in the design and development of digital fintech solutions, while ensuring adherence to legal requirements for customer protection considering the Canadian financial environment. To do so, this project aims to understand the practices and elements that define the relationship between fintech companies and their customers. This study relied on semi-structured interviews conducted with six fintech professionals involved with the design, development, regulation compliance, and governance of AI/digital solutions in the financial sector (4 in B2B and 2 in B2C; 4 men and 2 women). Participants were professionally titled either as CEOs, lawyers specialized in AI digital governance and fintech director. The discussion guide covered three main topics: their relationship with their clients, regulations constraints and best practices. Interviews were virtually conducted and transcribed. NVIVO was used for data categorization and coding and the qualitative analysis followed the procedure advocated by Gioia et al. (2013) to ensure qualitative rigor. The findings show that (1) compliance is central to fintech, with significant resources being invested in ensuring legal adherence and transparency (2) striking a balance between innovation and reliability is a challenge to maintain customer relationship and (3) focusing on privacy by design is a key concern, since customers are demanding higher levels of clarity, transparency and control over their personal data without compromising on the user experience. This study makes a significant contribution to the understanding of the fintech specific practices and challenges, recommending that fintech firms adopt detailed privacy policies to govern, manage, share and properly secure data to meet regulatory as well as customer expectations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".