Financial Model Construction and Identification of Abnormal Activities in Mobile Networks for e-commerce Platform Using Machine Learning Algorithm

Canales H.B.G.; Reyes E.M.A.; Vásquez A.P.; Correa S.R.; Vela C.A.L.; Ramírez J.A.B.; Ruiz E.R.P.; Flores-Tananta C.A. · Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (2024) · DOI: 10.58346/jowua.2024.i3.015

Resumen

The advancement of informatization has significantly affected many sectors due to e-commerce, rendering the existing Financial Model (FM) inadequate for e-commerce customers. Artificial Intelligence (AI) substantially enhances the financial accounting capabilities and resource integration of systems using mobile networks. The insufficient FM of e-commerce tools and issues such as the administration of funds have significantly obstructed the advancement of standardized financial procedures, as the FM structure impacts managers' statistical evaluation of financial information. This research examines the financial hazards, management issues, and underlying causes of these issues inside e-commerce systems, subsequently employing AI to assess the FM operations of these websites in Peru. This research uses a Machine Learning (ML) approach to examine the clustering centers of FM information as well as the privacy aspect of the FM. This study ultimately presents ways for optimizing and constructing FMs to enhance data security and capital administration in e-commerce financing, hence fostering the continued growth of e-commerce systems using mobile networks. The experimental findings indicate that the FM’s classification value and security aspect for e-commerce systems progressively enhance under the ML. The average classification value is 1.23, whereas the average risk factor is around 1.41. AI and ML improve financial accounting requirements and the long-term growth of e-commerce systems in Peru.

Instituciones: Universidad Nacional de Tumbes; Universidad Nacional de San Martín; Universidad César Vallejo