Frauds and default payments are two major anomalies in credit card transactions. Researchers have been vigorously finding solutions to tackle them and one of the solutions is to use data mining approaches. However, the collected credit card data can be quite a challenge for researchers. This is because of the data characteristics that contain: (i) unbalanced class distribution, and (ii) overlapping of class samples. Both characteristics generally cause low detection rates for the anomalies that are minorities in the data. On top of that, the weakness of general learning algorithms contributes to the difficulties of classifying the anomalies as the algorithms generally bias towards the majority class samples. In this study, we used a Multiple Classifiers System (MCS) on these two data sets: (i) credit card frauds (CCF), and (ii) credit card default payments (CCDP). The MCS employs a sequential decision combination strategy to produce accurate anomaly detection. Our empirical studies show that the MCS outperforms the existing research, particularly in detecting the anomalies that are minorities in these two credit card data sets. INDEX TERMS Anomaly detection, credit card, multiple classifiers.

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The increase in credit card transactions has inevitably caused an increase in credit card fraud. A total of 157,688 fraud cases occurred in 2018 worldwide, causing a total loss of $24.26 billion. This paper proposes using two types of autoencoder models to detect credit card fraud. The first type uses reconstruction error to detect anomalies in the data. The model detects fraud by defining a threshold in the reconstruction error to flag the transactions as legitimate or fraud. The second type performs dimensionality reduction to encode the data and removes noises. The encoded data were then used to train three other models: K-nearest neighbours (KNN), logistic regression (LR), and support vector machine (SVM). We then applied these models to a European bank's imbalanced credit card data set. A comparison was made between the two autoencoder types and three baseline models: KNN, LR and SVM. The results showed that both autoencoders gave a good and comparable performance in detecting credit card frauds.

Since 2020, the Covid-19 pandemic has spread like wildfire across many countries, including Malaysia. The disease has caused disastrous impacts on the country's economy, public health system, and the livelihoods of its citizens. Hence, there is an urgent need to investigate and determine the underlying factors attributed to the high infection rate of Covid-19. This research aims to study and identify demographic factors attributed to the high infection rate of Covid-19 in Malaysia using regression models. The preliminary results show that the labour force participation rate, unemployment rate, and average household income contribute to Malaysia's high COVID-19 infection rates.

Background: Credit cards remain the preferred payment method by many people nowadays. If not handled carefully, people may face severe consequences such as credit card frauds. Credit card frauds involve the illegal use of credit cards without the owner’s knowledge. Credit card fraud was estimated to exceed a $35.5 billion loss globally in 2020, and results in direct or indirect financial loss to the owners. Hence, a detection system capable of analysing and identifying fraudulent behaviour in credit card activities is highly desirable. Credit card data are not easy to handle due to their inherited problems: (i) unbalanced class distributions and (ii) overlapping classes. General learning algorithms may not be able to address and handle the problems well. Methods: This study addresses these problems using an Enhanced Stacking Classifiers System (ESCS) that comprises two sequential levels. The first level is an excellent classifier for detecting normal credit card transactions (the majority class), while the second level contains stacking classifiers that distinguish credit card frauds (the minority class). The ESCS can improve the fraud detection via the second level, which contains sensitive classifiers to identify the misclassified fraud transactions as normal transactions from the first classifier. The meta-classifier then combines the decisions of the base classifiers from the levels to produce the final detections. Results: We evaluated the ESCS using the benchmark credit card fraud dataset (CCFD) that exhibits the two problems. The highest true positive rate (TPR) for detecting credit card frauds was 0.8841, which outperformed the single classifiers, bagging, boosting, and other researchers’ works. Conclusions: This study proves that the ESCS, with an additional level added to the stacking classifiers, can improve fraud detection on credit card data.