📅 12 October 2023
DOI: 10.33633/jcta.v1i2.9259
Forging a User-Trust Memetic Modular Neural Network Card Fraud Detection Ensemble: A Pilot Study
Journal of Computing Theories and Applications
Universitas Dian Nuswantoro
📄 Abstract
The advent of the Internet as an effective means for resource sharing has consequently, led to proliferation of adversaries, with unauthorized access to network resources. Adversaries achieved fraudulent activities via carefully crafted attacks of large magnitude targeted at personal gains and rewards. With the cost of over $1.3Trillion lost globally to financial crimes and the rise in such fraudulent activities vis the use of credit-cards, financial institutions and major stakeholders must begin to explore and exploit better and improved means to secure client data and funds. Banks and financial services must harness the creative mode rendered by machine learning schemes to help effectively manage such fraud attacks and threats. We propose HyGAMoNNE – a hybrid modular genetic algorithm trained neural network ensemble to detect fraud activities. The hybrid, equipped with knowledge to altruistically detect fraud on credit card transactions. Results show that the hybrid effectively differentiates, the benign class attacks/threats from genuine credit card transaction(s) with model accuracy of 92%.
🔖 Keywords
#HyDelMoNNE; credit-card; fraud detection; deep learning ensemble; reinforcement model
ℹ️ Informasi Publikasi
Tanggal Publikasi
12 October 2023
Volume / Nomor / Tahun
Volume 1, Nomor 2, Tahun 2023
📝 HOW TO CITE
Ojugo, Arnold Adimabua; Akazue, Maureen Ifeanyi; Ejeh, Patrick Ogholuwarami; Ashioba, Nwanze Chukwudi; Odiakaose, Christopher Chukwufunaya; Ako, Rita Erhovwo; Emordi, Frances Uche, "Forging a User-Trust Memetic Modular Neural Network Card Fraud Detection Ensemble: A Pilot Study," Journal of Computing Theories and Applications, vol. 1, no. 2, Oct. 2023.
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