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All Journal Journal of Computing Theories and Applications
Nwanze Chukwudi Ashioba
Dennis Osadebay University Anwai-Asaba
Computer Science & IT Decision Sciences, Operations Research & Management

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Forging a User-Trust Memetic Modular Neural Network Card Fraud Detection Ensemble: A Pilot Study Arnold Adimabua Ojugo; Maureen Ifeanyi Akazue; Patrick Ogholuwarami Ejeh; Nwanze Chukwudi Ashioba; Christopher Chukwufunaya Odiakaose; Rita Erhovwo Ako; Frances Uche Emordi
Journal of Computing Theories and Applications Vol 1, No 2 (2023): (in Process)
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9259

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%.
Co-Authors Arnold Adimabua Ojugo Christopher Chukwufunaya Odiakaose Frances Uche Emordi Maureen Ifeanyi Akazue Patrick Ogholuwarami Ejeh Rita Erhovwo Ako