Securing Digital Transactions: The Role of Identity Verification in Reducing E-Commerce Fraud
Keywords:
Mobile identity verification, e-commerce fraud, digital transaction security, biometrics, multi-factor authenticationAbstract
The exponential growth of e-commerce has revolutionized how transactions are conducted, offering unprecedented convenience and global reach. However, this transformation has also attracted sophisticated cyber threats, including identity theft, account takeovers, and phishing scams, resulting in significant financial and reputational losses. Mobile identity verification has emerged as a pivotal solution, leveraging technologies such as biometrics, multi-factor authentication (MFA), and AI-driven analytics to combat fraud effectively. This paper explores the role of mobile identity verification in enhancing the security of digital transactions in e-commerce. Through a review of recent advancements, case studies, and data analysis, we demonstrate how mobile verification systems reduce fraud, foster consumer trust, and streamline compliance with regulatory frameworks. Despite its efficacy, challenges such as usability concerns, cost barriers, and technical vulnerabilities persist. We propose innovative solutions, including the integration of blockchain technology and advanced behavioral analytics, to address these limitations and outline future research directions for optimizing transaction security in the digital age.
References
Prisha, P., Neo, H. F., Ong, T. S., & Teo, C. C. (2017). E-commerce security and identity integrity: the future of virtual shopping. Advanced Science Letters, 23(8), 7849-7852.
Tan, F. T. C., Guo, Z., Cahalane, M., & Cheng, D. (2016). Developing business analytic capabilities for combating e-commerce identity fraud: A study of Trustev’s digital verification solution. Information & Management, 53(7), 878-891.
Shah, M., Ahmed, J., & Soomro, Z. (2016, December). Investigating the identity theft prevention strategies in m-commerce. In International Conferences on Internet Technologies & Society (ITS), (pp. 59-66).
Carmi, G., & Segal, S. Y. (2014). Mobile security: A review of new advanced technologies to detect and prevent e-payment mobile frauds. Int. J. Comput. Syst, 292(4), 2394-1065.
Kiselichki, M., Kirovska, Z., Anastasovski, M., & Jovevski, D. (2022). Security Aspects Of Digital Transactions E-Commerce And M-Commerce Implementations.
Rodrigues, V. F., Policarpo, L. M., da Silveira, D. E., da Rosa Righi, R., da Costa, C. A., Barbosa, J. L. V., ... & Arcot, T. (2022). Fraud detection and prevention in e-commerce: A systematic literature review. Electronic Commerce Research and Applications, 56, 101207.
Basu, A., & Muylle, S. (2003). Authentication in e-commerce. Communications of the ACM, 46(12), 159-166.
JOSHI, D., SAYED, F., BERI, J., & PAL, R. (2021). An efficient supervised machine learning model approach for forecasting of renewable energy to tackle climate change. Int J Comp Sci Eng Inform Technol Res, 11, 25-32.
Hassan, M. A., Shukur, Z., & Hasan, M. K. (2020). An efficient secure electronic payment system for e-commerce. computers, 9(3), 66.
Alqethami, S., Almutanni, B., & AlGhamdi, M. (2021). Fraud detection in E-commerce. International Journal of Computer Science & Network Security, 21(6), 312-318.
Schwiderski-Grosche, S., & Knospe, H. (2002). Secure mobile commerce. Electronics and Communication Engineering Journal, 14(5), 228-238.
Joshi, D., Sayed, F., Saraf, A., Sutaria, A., & Karamchandani, S. (2021). Elements of Nature Optimized into Smart Energy Grids using Machine Learning. Design Engineering, 1886-1892.
Challenges and Prospects of the National Health Insurance Scheme and Medical Service Delivery in The Nigerian Navy. (2023). International Journal of Scientific Research and Management (IJSRM), 11(04), 844-850. https://doi.org/10.18535/ijsrm/v11i04.mp08
Predicting Foot Salvageability in Diabetic Foot Lesion: Comparison of Benin Diabetic Foot Severity Score and Wagner System. (2023). International Journal of Scientific Research and Management (IJSRM), 11(05), 851-856. https://doi.org/10.18535/ijsrm/v11i05.mp1
Joshi, D., Parikh, A., Mangla, R., Sayed, F., & Karamchandani, S. H. (2021). AI Based Nose for Trace of Churn in Assessment of Captive Customers. Turkish Online Journal of Qualitative Inquiry, 12(6).
Sherif, M. H. (2003). Protocols for secure electronic commerce. CRC press.
Challenges and Prospects of the National Health Insurance Scheme and Medical Service Delivery in The Nigerian Navy. (2023). International Journal of Scientific Research and Management (IJSRM), 11(04), 844-850. https://doi.org/10.18535/ijsrm/v11i04.mp08
Kenneth, E., & Ohia, P. (2021). Integrating Real-Time Drilling Fluid Monitoring and Predictive Analytics for Incident Prevention and Environmental Protection in Complex Drilling Operations. Journal of Artificial Intelligence Research, 1(1), 157-185.
Soomro, Z. A., Ahmed, J., Shah, M. H., & Khoumbati, K. (2019). Investigating identity fraud management practices in e-tail sector: a systematic review. Journal of Enterprise Information Management, 32(2), 301-324.
Khambaty, A., Joshi, D., Sayed, F., Pinto, K., & Karamchandani, S. (2022, January). Delve into the Realms with 3D Forms: Visualization System Aid Design in an IOT-Driven World. In Proceedings of International Conference on Wireless Communication: ICWiCom 2021 (pp. 335-343). Singapore: Springer Nature Singapore.
Nguyen, T. T., Nguyen, H. H., Sartipi, M., & Fisichella, M. (2023). Multi-vehicle multi-camera tracking with graph-based trac Pei, Y., Liu, Y., Ling, N., Liu, L., & Ren, Y. (2021, May). Class-specific neural network for video compressed sensing. In 2021 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.klet features. IEEE Transactions on Multimedia, 26, 972-983.
Pei, Y., Liu, Y., Ling, N., Ren, Y., & Liu, L. (2023, May). An end-to-end deep generative network for low bitrate image coding. In 2023 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.
Basu, A., & Muylle, S. (2003). Authentication in e-commerce. Communications of the ACM, 46(12), 159-166.
Pei, Y., Liu, Y., & Ling, N. (2023, December). MobileViT-GAN: A Generative Model for Low Bitrate Image Coding. In 2023 IEEE International Conference on Visual Communications and Image Processing (VCIP) (pp. 1-5). IEEE.
Pei, Y., Liu, Y., & Ling, N. (2020, October). Deep learning for block-level compressive video sensing. In 2020 IEEE international symposium on circuits and systems (ISCAS) (pp. 1-5). IEEE.
Pei, Y., Liu, Y., Ling, N., Ren, Y., & Liu, L. (2023, May). An end-to-end deep generative network for low bitrate image coding. In 2023 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.
Khambati, A. (2021). Innovative Smart Water Management System Using Artificial Intelligence. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(3), 4726-4734.
Nanduri, J., Jia, Y., Oka, A., Beaver, J., & Liu, Y. W. (2020). Microsoft uses machine learning and optimization to reduce e-commerce fraud. INFORMS Journal on Applied Analytics, 50(1), 64-79.
Joshi, D., Sayed, F., Jain, H., Beri, J., Bandi, Y., & Karamchandani, S. A Cloud Native Machine Learning based Approach for Detection and Impact of Cyclone and Hurricanes on Coastal Areas of Pacific and Atlantic Ocean.
Elgassim, M. A. M., Sanosi, A., & Elgassim, M. A. (2021). Transient Left Bundle Branch Block in the Setting of Cardiogenic Pulmonary Edema. Cureus, 13(11).
Rodrigues, V. F., Policarpo, L. M., da Silveira, D. E., da Rosa Righi, R., da Costa, C. A., Barbosa, J. L. V., ... & Arcot, T. (2022). Fraud detection and prevention in e-commerce: A systematic literature review. Electronic Commerce Research and Applications, 56, 101207.
Tan, F. T. C., Guo, Z., Cahalane, M., & Cheng, D. (2016). Developing business analytic capabilities for combating e-commerce identity fraud: A study of Trustev’s digital verification solution. Information & Management, 53(7), 878-891.
Elgassim, M. A. M., Saied, A. S. S., Mustafa, M. A., Abdelrahman, A., AlJaufi, I., & Salem, W. (2022). A Rare Case of Metronidazole Overdose Causing Ventricular Fibrillation. Cureus, 14(5).
Carmi, G., & Segal, S. Y. (2014). Mobile security: A review of new advanced technologies to detect and prevent e-payment mobile frauds. Int. J. Comput. Syst, 292(4), 2394-1065.
Patil, S. (2023). IDENTIFYING INVALID IDENTIFICATION DOCUMENTS [Review of IDENTIFYING INVALID IDENTIFICATION DOCUMENTS]. https://ppubs.uspto.gov/dirsearch-public/print/downloadBasicPdf/11625933?requestToken=eyJzdWIiOiJjYTNlZGM1MS01YmZhLTQ1YmQtYjBhNi1jMGRkMDU0Mjk5MDEiLCJ2ZXIiOiJkODAwMGZiMy1hOTYzLTRmNjUtYWExYy0yNmNmNGI4YTI0NmEiLCJleHAiOjB9
Alqethami, S., Almutanni, B., & AlGhamdi, M. (2021). Fraud detection in E-commerce. International Journal of Computer Science & Network Security, 21(6), 312-318.
Prisha, P., Neo, H. F., Ong, T. S., & Teo, C. C. (2017). E-commerce security and identity integrity: the future of virtual shopping. Advanced Science Letters, 23(8), 7849-7852.
Elgassim, M., Abdelrahman, A., Saied, A. S. S., Ahmed, A. T., Osman, M., Hussain, M., ... & Salem, W. (2022). Salbutamol-Induced QT Interval Prolongation in a Two-Year-Old Patient. Cureus, 14(2).
Shah, M., Ahmed, J., & Soomro, Z. (2016, December). Investigating the identity theft prevention strategies in m-commerce. In International Conferences on Internet Technologies & Society (ITS), (pp. 59-66).
Parhamfar, M. (2024). Towards Green Airports: Factors Influencing Greenhouse Gas Emissions and Sustainability through Renewable Energy. Next Research, 100060.
Scaria, B. A., & Megalingam, R. K. (2018, June). Enhanced E-commerce application security using three-factor authentication. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1588-1591). IEEE.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
