An Integrated Machine Learning Framework for Fraud Detection
Karim Ouazzane, Thekla Polykarpou, Yogesh Patel, Jun Li · International Journal of Information Security and Privacy · 2022
The research develops a practical Machine Learning framework with a comparative and comprehensive approach to sequence-learn and then detect the online banking payment fraud. The integrated framework introduces exploratory analysis and feature engineering, multiple modelling and performance comparison, and model robustness, uncertainty and sensitivity analysis toward a systematic approach for Machine Learning applications. For demonstration purpose, the framework is implemented on a set of real-life online banking transaction datasets obtained from a UK-based bank through three models, i.e., Support Vector Machine, Markov Model and LSTM model, with various combinational features of the datasets evidenced in the exploratory analysis and modelling with noise ratios of datasets, range values of model parameters and confidence intervals of prediction results. The modelling results show that overall, the LSTM model achieves the best performance, with outcome accuracy of 97.7%, indicating its advantage in modelling sequential data such as customer behaviours.