The recent advances of e-commerce and e-payment systems have sparked an increase in financial fraud cases such as credit card fraud. It is therefore crucial to implement mechanisms that can detect the credit card fraud. Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a machine learning (ML) based credit card fraud detection engine using the genetic algorithm (GA) for feature selection. After the optimized features are chosen, the proposed detection engine uses the following ML classifiers: Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), and Naive Bayes (NB). To validate the performance, the proposed credit card fraud detection engine is evaluated using a dataset generated from European cardholders. The result demonstrated that our proposed approach outperforms existing systems.

The advance in technologies such as e-commerce and financial technology (FinTech) applications have sparked an increase in the number of online card transactions that occur on a daily basis. As a result, there has been a spike in credit card fraud that affects card issuing companies, merchants, and banks. It is therefore essential to develop mechanisms that ensure the security and integrity of credit card transactions. In this research, we implement a machine learning (ML) based framework for credit card fraud detection using a real world imbalanced datasets that were generated from European credit cardholders. To solve the issue of class imbalance, we re-sampled the dataset using the Synthetic Minority over-sampling TEchnique (SMOTE). This framework was evaluated using the following ML methods: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Decision Tree (DT), and Extra Tree (ET). These ML algorithms were coupled with the Adaptive Boosting (AdaBoost) technique to increase their quality of classification. The models were evaluated using the accuracy, the recall, the precision, the Matthews Correlation Coefficient (MCC), and the Area Under the Curve (AUC). Moreover, the proposed framework was implemented on a highly skewed synthetic credit card fraud dataset to further validate the results that were obtained in this research. The experimental outcomes demonstrated that using the AdaBoost has a positive impact on the performance of the proposed methods. Further, the results obtained by the boosted models were superior to existing methods.

Credit risk prediction is a crucial task for financial institutions. The technological advancements in machine learning, coupled with the availability of data and computing power, has given rise to more credit risk prediction models in financial institutions. In this paper, we propose a stacked classifier approach coupled with a filter-based feature selection (FS) technique to achieve efficient credit risk prediction using multiple datasets. The proposed stacked model includes the following base estimators: Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB). Furthermore, the estimators in the Stacked architecture were linked sequentially to extract the best performance. The filter- based FS method that is used in this research is based on information gain (IG) theory. The proposed algorithm was evaluated using the accuracy, the F1-Score and the Area Under the Curve (AUC). Furthermore, the Stacked algorithm was compared to the following methods: Artificial Neural Network (ANN), Decision Tree (DT), and k-Nearest Neighbour (KNN). The experimental results show that stacked model obtained AUCs of 0.934, 0.944 and 0.870 on the Australian, German and Taiwan datasets, respectively. These results, in conjunction with the accuracy and F1-score metrics, demonstrated that the proposed stacked classifier outperforms the individual estimators and other existing methods.

The rising volume of online transactions has concurrently increased the incidence of credit card fraud, presenting severe challenges to financial institutions and consumers alike. Traditional fraud detection techniques often fail to adapt to the sophisticated and evolving strategies employed by fraudsters, resulting in significant financial losses. This study introduces a novel hybrid deep learning ensemble model to enhance credit card fraud detection. The proposed model integrates Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and Transformers as base learners, with Extreme Gradient Boosting (XGBoost) serving as the meta-learner. The experimental results demonstrate that the hybrid ensemble significantly outperforms individual base learners and traditional methods, with a sensitivity of 0.961, specificity of 0.999, and area under the receiver operating characteristic curve (AUC-ROC) of 0.972 using the European Credit Card Dataset. This research demonstrates the effectiveness of hybrid deep learning techniques in addressing the complexities of credit card fraud detection, thereby contributing to the development of more secure and reliable financial systems.

Online card transactions have become more frequent due to the growth of e-commerce and financial technology apps. However, this also means more opportunities for credit card fraud, which affects banks, retailers, and card issuers. Therefore, we need systems that can protect the security and integrity of credit card transactions. In this study, we use the Adaptive Synthetic Minority Oversampling Technique to balance an imbalanced dataset; then we combine that with the Recursive Feature Elimination with the validation technique to enhance the performance of credit card fraud detection systems. We compare the results of several models, which are Decision Tree, Random Forests, Extreme Gradient Boosting, Light Gradient Boosting Machines, and Linear Regression, on the original imbalanced dataset and the resampled dataset using the Adaptive Synthetic Minority Oversampling Technique before finally applying the Recursive Feature Elimination with Cross Validation technique. We aim to find the best model and method for detecting credit card fraud. During training, k-fold cross-validation is applied to both sets of models in order to prevent overfitting and improve the classification. Our results show that the Adaptive Synthetic Minority Oversampling Technique and Recursive Feature Elimination with Cross Validation modified dataset improved overall classification errors over the baseline dataset. Specifically, the best performing models were the Extreme Gradient Boosting and Random Forests with a Matthew’s Correlation Coefficient of 0.8794 and 0.8622, respectively, when used with the baseline dataset, and when used with Adaptive Synthetic Minority Oversampling Technique and Recursive Feature Elimination with Cross Validation dataset, we recorded a Matthew’s Correlation Coefficient of 0.9994 and 0.9991 for the Extreme Gradient Boosting and Random Forest, respectively. Furthermore, our results show that the Light Gradient Boosting Machines model recorded the most improvement in Matthew’s Correlation Coefficient from 0.3394 to 0.9980 when used with baseline and Adaptive Synthetic Minority Oversampling Technique modified datasets, respectively. This represents an increase of 194% in Matthew’s Correlation Coefficient.

Cervical cancer is a common and deadly disease that affects women worldwide. Early diagnosis and treatment can improve the survival and quality of life of patients. Machine learning techniques can help to analyze complex and high-dimensional data related to cervical cancer and provide accurate and reliable predictions. However, selecting the most relevant and informative features from the data is a challenging task that affects the performance and interpretability of machine learning models. This paper proposes a novel method that uses particle swarm optimization (PSO) to perform feature selection and optimization for cervical cancer prediction. PSO is a bio-inspired algorithm that mimics the social behavior of a swarm of particles that search for the optimal solution in the feature space. The use of PSO to select the best subset of features that maximize the classification accuracy of eight machine learning models: Support Vector Machines (SVM), Gaussian Naive Bayes (GNB), Random Forests (RF), Decision Trees (DT), Extreme Gradient Boosting (XGB), Linear Regression (LR), Adaptive Boosting (AdaBoost), and K-nearest neighbor (KNN).To evaluate the method, a publicly available dataset was used, the Cervical Cancer Risk Factors Dataset (CCRFD). Then, compare the results with several state-of-the-art methods that use different feature selection techniques and ML algorithms. The experimental results show that the method achieves superior performance in terms of feature reduction rate, accuracy, precision, and AUC. Specifically, the Adaboost-PSO model performed best in terms of feature reduction rate with a reduction of rate 100% while the RF-PSO model performed best in terms of accuracy and precision, with an accuracy of 98% and precision of 100%.