a multi-layered hidden markov model for real-time fraud detection in electronic financial transactions

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.