A new digital wallet infrastructure was developed to improve security, efficiency, and cost-effectiveness of digital asset transactions.
Researchers created a digital wallet system that provides local solutions, secure storage, and user-friendly applications. The system aims to meet the needs of Financial Technology companies and their partners. This study aims to contribute to the expansion of the digital wallet ecosystem and improve user experiences in digital asset management.
Abstract
This study seeks to develop an innovative infrastructure for digital wallets that addresses the diverse requirements of both corporate and individual users. The main focus is on real-time monitoring of users' interactions on digital asset management software and generating instant actions through the analysis of this data. In this context, the main objectives of the wallet include providing local solutions as an alternative to high transaction fees, ensuring that assets are securely stored on local cloud systems with digital wallets, and developing applications that will meet the needs of Financial Technology companies and its business partners. Thus, it is aimed to increase the security, efficiency, and cost-effectiveness of digital asset transactions and to improve the user experience in digital wallet use in general. As a result, this study aims to provide a comprehensive solution that aims to offer significant advantages such as security, usability, and cost-effectiveness in the field of digital asset management and storage. It is expected that this study will contribute significantly to the expansion of the digital wallet ecosystem and to users' experiences in digital asset management.
References
- 1.Matthew Boyle & Sophie Barber from Finder - Digital wallet statistics: Usage and market size
- 2.Individuals Using the Internet from www.itu.int
- 3.Petroc Taylor from Statista - Forecast number of mobile users worldwide from 2020 to 2025
- 4.Rowley, J. (2005), "The four Cs of customer loyalty", Marketing Intelligence & Planning, Vol. 23 No. 6, pp. 574-581. DOI: 10.1108/02634500510624138DOI
- 5.Paris Carbone, Stephan Ewen, Gyula Fóra, Seif Haridi, Stefan Richter, and Kostas Tzoumas. (2017). State management in Apache Flink®: consistent stateful distributed stream processing. Proc. VLDB Endow. 10, 12 (August 2017), 1718–1729. DOI: 10.14778/3137765.3137777DOI
- 6.Saket, S., Chandela, V., & Kalim, M. D. (2024). Real-time event joining in practice with Kafka and Flink. *arXiv*. DOI: 10.48550/arXiv.2410.15533DOI
- 7.Tuğçe Süheyla Kaya, Murat Gezer, Sevinç Gülseçen. Application of Recommender System for Spending Habits Based Campaign Management. DOI: 10.3390/proceedings2021074007DOI
- 8.Al Karim, R.; Habiba, W. Effects of CRM Components on Firm’s Competitive Advantage: A Case on Bangladesh Banking Industry. Manag. Res. 2020, 10, 1–7.
- 9.Pokharel, B. Customer Relationship Management: Related Theories, Challenges and Application in Banking Sector. Bank. J. 1970, 1, 19–28. DOI: 10.3126/bj.v1i1.5140DOI
- 10.Isinkaye, F.O.; Folajimi, Y.O.; Ojokoh, B.A. Recommendation systems: Principles, methods and evaluation. Egypt. Inform. J. 2015, 16, 261–273. DOI: 10.1016/j.eij.2015.06.005DOI
- 11.Asterios Katsifodimos and Sebastian Schelter. 2016. Apache Flink: Stream Analytics at Scale. In 2016 IEEE International Conference on Cloud Engineering Workshop (IC2EW). 193–193. DOI: 10.1109/IC2EW.2016.56DOI
- 12.O. -C. Marcu, A. Costan, G. Antoniu and M. S. Pérez-Hernández, "Spark Versus Flink: Understanding Performance in Big Data Analytics Frameworks," 2016 IEEE International Conference on Cluster Computing (CLUSTER), Taipei, Taiwan, 2016, pp. 433-442, DOI: 10.1109/CLUSTER.2016.22DOI
- 13.Hueske, F., & Kalavri, V. (2019). Stream processing with Apache Flink: fundamentals, implementation, and operation of streaming applications. O'Reilly Media.