Design and Development of Mobile Payment Platform Software Supported by Analytical Capabilities for Payment Systems
Abstract
The Paycell Mobile Payment application serves as a platform where users can benefit from various payment and shopping services. Each user explores services within the application based on their own interests and needs, and this process generates a significant amount of data. The data created has become an important resource for improving user experience and enhancing the services offered. The primary goal of our project is to enrich the user experience and offer personalized recommendations based on real transactions to meet financial needs more effectively. This approach represents a significant step in the mobile payment systems industry in terms of data analytics and personalization. Simultaneously, this approach aims to position the Paycell application as a 'super app' where users can personalize their financial transactions. Our project captures user-initiated transactions with the aim of providing personalized recommendations tailored to users' interests and needs. These data are analyzed using data processing methods to obtain meaningful results. The results are then used to offer personalized recommendations to users. This approach contributes to users having more tailored experiences and meeting their financial needs more effectively.
Keywords
References
- Aktas, D. E., & Aktas, M. S. (2020). Real-time pattern detection methodology for monitoring student behaviour on e-learning platform in the field of financial sciences: Case study. In 2020 28th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
- Aktas, M., Aydin, G., Donnellan, A., Fox, G., Granat, R., Lyzenga, G., ... & Sayar, A. (2005). Implementing geographical information system grid services to support computational geophysics in a service-oriented environment. NASA Earth-Sun System Technology Conference. University of Maryland, Adelphi, Maryland.
- Alaasam, A. B. A., Radchenko, G., & Tchernykh, A. (2019). Stateful stream processing for digital twins: Microservice-based Kafka stream DSL. In 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) (pp. 804-0809). Novosibirsk, Russia.
Abstract
References
- Aktas, D. E., & Aktas, M. S. (2020). Real-time pattern detection methodology for monitoring student behaviour on e-learning platform in the field of financial sciences: Case study. In 2020 28th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
- Aktas, M., Aydin, G., Donnellan, A., Fox, G., Granat, R., Lyzenga, G., ... & Sayar, A. (2005). Implementing geographical information system grid services to support computational geophysics in a service-oriented environment. NASA Earth-Sun System Technology Conference. University of Maryland, Adelphi, Maryland.
- Alaasam, A. B. A., Radchenko, G., & Tchernykh, A. (2019). Stateful stream processing for digital twins: Microservice-based Kafka stream DSL. In 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) (pp. 804-0809). Novosibirsk, Russia.
Details
| Primary Language | English |
|---|---|
| Subjects | Software Engineering (Other) |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | December 23, 2023 |
| Publication Date | November 30, 2023 |
| Published in Issue | Year 2023 Volume: 24 |
Cite
