Behind the AI-Scenes: How FinTech Professionals Navigate Regulations and Privacy Concerns to Enhance User Experience
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,011 | 0,011 |
| Communication savante | 0,017 | 0,016 |
| Science ouverte | 0,003 | 0,011 |
| Intégrité de la recherche | 0,006 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».