A Multi-layered Hidden Markov Model for Real-Time Fraud Detection in Electronic Financial Transactions
Publish Year: 1402
Type: Journal paper
Language: English
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Document National Code:
JR_JADM-11-4_009
Index date: 31 December 2024
A Multi-layered Hidden Markov Model for Real-Time Fraud Detection in Electronic Financial Transactions abstract
Hidden Markov Models (HMMs) are machine learning models that has been applied to a range of real-life applications including intrusion detection, pattern recognition, thermodynamics, statistical mechanics among others. A multi-layered HMMs for real-time fraud detection and prevention whilst reducing drastically the number of false positives and negatives is proposed and implemented in this study. The study also focused on reducing the parameter optimization and detection times of the proposed models using a hybrid algorithm comprising the Baum-Welch, Genetic and Particle-Swarm Optimization algorithms. Simulation results revealed that, in terms of Precision, Recall and F1-scores, our proposed model performed better when compared to other approaches proposed in literature.
A Multi-layered Hidden Markov Model for Real-Time Fraud Detection in Electronic Financial Transactions Keywords:
A Multi-layered Hidden Markov Model for Real-Time Fraud Detection in Electronic Financial Transactions authors
Abdul Aziz Danaa Abukari
Department of Computer Science, Tamale Technical University, Tamale, Ghana.
Mohammed Ibrahim
Department of Computer Science, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana.
Alhassan Abdul-Barik
Department of Computer Science, University for Development Studies, Tamale, Ghana.
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