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Article type: Research Article
Authors: Tekkali, Chandana Gouria; * | Natarajan, Karthikab
Affiliations: [a] Computer Science and Engineering, Raghu Engineering College, Dakamarri, Visakhapatnam, Andhra Pradesh, India | [b] School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India
Correspondence: [*] Corresponding author: Chandana Gouri Tekkali, Computer Science and Engineering, Raghu Engineering College, Dakamarri, Visakhapatnam, Andhra Pradesh, India. E-mail: chandana.tekkali@raghuenggcollege.in.
Abstract: This article proposes an artificial intelligence-empowered and efficient detection approach for customers with Severe Failure in Digital Transactions (SFDT) through a deep transfer network learning approach from discretized fraud data. Presently, the Real-time global payment system is suffered primarily by fraudsters based on customer behavior. For the identification of fraud, scientists used many techniques. However, identifying and tracking the customers infected by the fraud takes a significant amount of time. The proposed study employs pre-trained convolution neural network-based (CNN) architectures to find SFDT. CNN is pre-trained on the various network architectures using fraud data. This article contributed to pre-trained networks with newly developed versions ResNet152, DenseNet201, InceptionNetV4, and EfficientNetB7 by integrating the loss function to minimize the error. We run numerous experiments on large data set of credit payment transactions which are public in nature, to determine the high rate of SFDT with our model by comparing accuracy with other fraud detection methods and also proved best in evaluating minimum loss cost.
Keywords: Digital transaction, pre-trained models, convolutional neural networks, transfer learning
DOI: 10.3233/KES-230067
Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 28, no. 3, pp. 571-580, 2024
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