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A proposed model for enhancing e-bank transactions: an experimental comparative study.
- Source :
- Indonesian Journal of Electrical Engineering & Computer Science; May2024, Vol. 34 Issue 2, p1268-1279, 12p
- Publication Year :
- 2024
-
Abstract
- In this paper, we introduce a novel approach to address the dynamic prediction of customer activity in electronic payment transactions for individual clients. Our approach is founded on customer online payment transaction records from registered UK-based online retailers between 01/12/2009 and 09/12/2011. These retailers primarily specialize in unique gift items for various occasions, catering to a wide range of clients, including wholesalers. We used classification analysis based on the correlation coefficient to measure and describe a customer's electronic payment capability based on the quality of products they purchase. Furthermore, we trained multi-layered models (linear model, deep learning, random forest, and support vector machines (SVM)) to capture the dynamics of e-bank transaction reinforcement for retail customers using machine learning. Real transaction data from a UK online retailer was employed in our study. The experimental results consistently demonstrated the effectiveness of our proposed strategy. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 25024752
- Volume :
- 34
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Indonesian Journal of Electrical Engineering & Computer Science
- Publication Type :
- Academic Journal
- Accession number :
- 176826213
- Full Text :
- https://doi.org/10.11591/ijeecs.v34.i2.pp1268-1279