Machine learning has been increasingly applied in identification of fraudulent transactions. However, most application systems can only detect the incidents after they have already occurred, not at or near real time. As spurious transactions are far fewer than the normal ones, the highly imbalanced data makes fraud detection even more challenging. This study has proposed a detection framework and implemented it using Support Vector Machine (SVM) enhanced with quantum annealing solvers. To evaluate its detection performance and examine the impact of feature selection, we have applied this quantum machine learning (QML) system along with systems built with twelve traditional machine learning methods on two datasets: Israel credit card transactions (non-time series) which is moderately imbalanced and a bank loan dataset (time series) that is highly imbalanced. The result shows that, the quantum enhanced SVM has categorically outperformed the rest in both speed and accuracy with the bank loan dataset. However, it’s detection accuracy is similar to others with Israel credit card transactions data. Furthermore, for both datasets, feature selection has been shown to significantly improve the detection speed, although the improvement on accuracy is marginal. These findings have demonstrated the potential of QML applications on time series based, highly imbalanced data, and the merit of traditional machine learning approaches in non-time series data. This study provides insight on selecting appropriate approach with different types of datasets while taking the tradeoffs of speed, accuracy, and cost into consideration.

Global markets are continuously changing and demanding customised, low cost, and high quality products and services; organisations that offer these products and services are dealing with these issues on a daily basis to stay competitive. Among the quality tools used to achieve competitive advantage, the 5S practice plays an important role facilitating the introduction and development of other quality and continuous improvement methods and techniques. We present results of an empirical study applied to some Mexican organisations with the aim of understanding their implementation experience, empirical relationships, and ongoing challenges associated with the 5S practice.