Real-Time Fraud Detection Using Machine Learning

This paper uses a classifier named random forest to detect credit card fraud. Credit card fraud is one of the main issues in the economic industry. To construct a credit card fraud detection system, a certain amount of samples is required. In this paper, a dataset containing 284,807 credit card transactions is used. This dataset has gone through the PCA transformation and includes 492 frauds out of 284,807 transactions. Based on the huge amount of data and imbalanced samples, this paper compresses the dataset and uses the synthetic minority over-sampling technique (SMOTE) to address the problem of imbalanced samples. Also, in this paper, we use random forest as a classification model while constructing the fraud detection system/method.

Financial fraud is a serious threat that is expanding effects on the financial sector. The use of credit cards is growing as digitization and internet transactions advance daily. The most common issues in today's culture are credit card scams. This kind of fraud typically happens when someone uses someone else's credit card details. Credit card fraud detection uses transaction data attributes to identify credit card fraud, which can save significant financial losses and affluence the burden on the police. The detection of credit card fraud has three difficulties: uneven data, an abundance of unseen variables, and the selection of an appropriate threshold to improve the models' reliability. This study employs a modified Logistic Regression (LR) model to detect credit card fraud in order to get over the preceding difficulties. The dataset sampling strategy, variable choice, and detection methods employed all have a significant impact on the effectiveness of fraud detection in credit card transactions. The effectiveness of naive bayes, k-nearest neighbour, and logistic regression on highly skewed credit card fraud data is examined in this research. The accuracy of the logistic regression technique will be closer to 0.98%; with this accuracy, frauds may be easily detected. The fact that LR receives the highest classifier score illustrates how well LR predicts credit card theft.

The increase of Credit Card (CC) fraud in recent years requires the development of fraud detection systems that are both efficient and robust. This paper explored the utilization of machine learning models, with a particular emphasis on ensemble methods, to advance the detection of CC fraud. We present an ensemble model that incorporates different classifiers to address the dataset imbalance issue that is present in most CC datasets. We employed synthetic over-sampling and under-sampling techniques in certain machine learning algorithms to tackle the same issue. To address the issue of under sampling, we implemented ensemble random sampling, which was subsequently followed by Two-Stage Thresholding (TST) to eliminate outliers. The well-known Synthetic Minority Over-sampling Technique (SMOTE) was used for over-sampling. Data was utilized to train the model and subsequently generate predictions by utilizing testing data following the pre-processing of the dataset. The proposed ensemble model outperformed conventional methods in addressing the challenges associated with CC fraud detection, as demonstrated by a comparative analysis. Our findings suggest that ensemble approaches are effective at fighting fraud, paving the way for more adaptive and resilient fraud detection systems.

Credit card fraud encompasses illicit activities aimed at unlawfully obtaining confidential information to enable unauthorized individuals to engage in illegal transactions. As technology advances, fraudsters have honed their skills in evading security measures, presenting a formidable challenge in fraud detection. To address this issue, an array of algorithms and analytical techniques has emerged to identify and mitigate instances of fraud. This research aimed to identify the most appropriate supervised machine learning algorithm for credit card fraud detection. Logistic Regression, Random Forest, Support Vector Machine, and Decision Trees were implemented and compared. Due to the imbalanced nature of the dataset, the SMOTE (Synthetic Minority Oversampling Technique) technique was employed to rectify the data imbalance by oversampling the minority class. The performance of the trained models was evaluated using various metrics, including the confusion matrix, accuracy, precision, recall, f1-score, Matthews Correlation Coefficient (MCC), and Area Under the Curve (AUC). The results of the analysis revealed that Random Forests exhibited exceptional performance, achieving an impressive recall score of 84% and surpassing other algorithms. This research provides the groundwork for future investigations involving diverse deep-learning techniques applied to real-time and dynamic datasets, enabling continuous enhancements in fraud detection and prevention mechanisms.

A method for accurately classifying donkey breeds has been developed by integrating single nucleotide polymorphism (SNPs) data with machine learning algorithms. The approach includes preprocessing donkey genomic sequencing data, addressing data imbalance with the Synthetic Minority Over-sampling Technique (SMOTE), and utilizing an improved Leave-One-Out Cross-Validation (LOOCV) for dataset partitioning. Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) models were constructed and evaluated. The results demonstrated that different chromosomes significantly influence classifier performance. For instance, chromosome Chr2 showed the highest classification accuracy with KNN, while chromosome Chr19 performed best with SVM and RF models. After enhancing data quality and addressing imbalances, classification performance improved substantially, with accuracy, precision, recall, and F1 score showing increases of up to 15% in certain models, particularly on key chromosomes. This method offers an effective solution for donkey breed classification and provides technical support for the conservation and development of donkey genetic resources.

In today's socio-economic scenario, people rely heavily on credit cards.Moreover, credit cards are a requisite financial tool that enables its holders to make assets.It is true that credit cards, as a new method of payment, have become socially amenable to the masses.But nowadays, improvements in technology lead to growth in illegal activities.During credit card transactions many fraudsters can breach security and make fraudulent transactions to withdraw or transfer funds from one's account or e-wallets.The relevant literature presents many machine learning-based approaches for credit card detection, such as Logistic Regression, Decision Tree, CatBoost, Random Forest, Support Vector Machine, KNN, RNN, and CNN.The main focus has been to apply the recent development of deep learning algorithms for this purpose.Comparative analysis of both machine learning and deep learning algorithms was performed to find efficient outcomes.A machine learning algorithm was first applied to the dataset, which improved the accuracy of detection of the fraud to some extent.The proposed approaches can be implemented effectively for the real-world detection of credit card fraud.

Fraud detection is a crucial aspect of risk management in the financial sector, where early identification of fraudulent activities—such as credit card fraud, money laundering, and identity theft—can prevent significant financial losses. Traditional detection methods often struggle with evolving fraud patterns, necessitating more advanced approaches. This chapter explores the application of Random Forest (RF) and K-Nearest Neighbours (KNN) in fraud detection. The Random Forest model is trained on diverse transaction datasets, analyzing key features such as transaction frequency, amount, location, and device usage. By employing an ensemble learning approach, RF builds multiple decision trees using random subsets of data and features, improving accuracy and robustness in detecting subtle fraud patterns. In contrast, KNN is an instance-based learning algorithm that classifies new transactions based on the majority class of their nearest neighbours.

Online credit card transactions have witnessed a steady year-to-year growth, making fraud detection an increasingly critical challenge. Credit card fraud detection is particularly difficult due to the extreme class imbalance between fraudulent and non-fraudulent transactions. Imbalanced data, where one class heavily dominates the other, must be addressed before effective fraud detection can be achieved. Traditional oversampling techniques such as the Synthetic Minority Over-sampling Technique (SMOTE) and its variants often generate synthetic samples without considering the importance of ranking performance in the minority class distribution, leading to suboptimal classification performance. To address this, an adaptive Weight and rank-based Minority (WRM-SMOTE) oversampling method is proposed that prioritizes the performance and its relative ranking. To the best of our knowledge, WRM-SMOTE is among the first method to combine performance-based weighting and ranking across multiple SMOTE variants for adaptive oversampling. This method transforms imbalanced datasets into balanced ones through adaptive synthetic sample generation. The process begins by computing a weight based on two componentsabsolute performance and relative ranking. This is done to provide a distinction between two variants with extremely similar performance when one outperforms the other. Our experimental results on real-world credit card fraud datasets and eleven other benchmark datasets demonstrate that WRM-SMOTE significantly improves fraud detection performance, achieving higher precision and recall while maintaining a balanced false-positive rate compared to traditional oversampling methods. The proposed method effectively enhances classification accuracy by generating synthetic samples in high-impact regions thereby improving the robustness of fraud detection models.

Introducing two Random Forest based methods for cloud detection in remote sensing images

This study investigates the use of machine learning (ML) models combined with a Synthetic Minority Over-sampling Technique (SMOTE) and its variants to predict perioperative pressure injuries (PIs) in an imbalanced dataset. PIs are a significant healthcare problem, often leading to prolonged hospitalization and increased medical costs. Conventional risk assessment scales are limited in their ability to predict PIs accurately, prompting the exploration of ML techniques to address this challenge.We utilized data from 7,292 patients admitted to a tertiary care hospital in Shanghai between May 2017 and July 2023, with a final dataset of 2,972 patients, including 158 with PIs. Seven ML algorithms-Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Extra Trees (ET), K-Nearest Neighbors (KNN), and Decision Trees (DT)-were used in conjunction with SMOTE, SMOTE+ENN, Borderline-SMOTE, ADASYN, and GAN to balance the dataset and improve model performance.Results revealed significant improvements in model performance when SMOTE and its variants were used. For instance, the XGBoost model hadan AUC of 0.996 with SMOTE, compared to 0.800 on raw data. SMOTE+ENN and Borderline-SMOTE further enhanced the models' ability to identify minority classes. External validation indicatedthat XGBoost, RF, and ET exhibited the highest stability and accuracy, with XGBoost having an AUC of 0.977. SHAP analysis revealed that factors such as anesthesia grade, age, and serum albumin levels significantly influenced model predictions.In conclusion, integrating SMOTE with ML algorithms effectively addressed a data imbalance and improved the prediction of perioperative PIs. Future work should focus on refining SMOTE techniques and exploring their application to larger, multi-center datasets to enhance the generalizability of these findings, and especially for diseaseswith a lowincidence.

Advanced hydrogeochemical facies classification: A comparative analysis of Machine Learning models with SMOTE in the Tawi basin

Use of credit card is very common these days. And the number of frauds related to credit cards are also increasing. With the increase in the usage of internet, many organizations have shifted their work from offline to online. Same is the case with financial department. On one side, this thing has increased the ease of people but on the other hand, number of frauds have been tremendously increased. On one side, people are doing shopping without cash, paying bills without standing in long queues, doing booking online and on the other side fake accounts, scamming, credit card frauds have been increased resulting in huge amount of loss to financial system every year. Fraud is a criminal activity done by un authorized person. Credit card frauds are very common these days. There are many types of credit card frauds. Sometime they do fake calls or messages and sometimes they steal customer’s online information. Many techniques using machine learning models have been implemented in order to stop these types of frauds. But fraudsters are sometimes by pass theses traditional protective systems and make successful transaction. Traditional machine learning models are not capable enough to detect frauds using sequence of data. For this purpose, neural networks are recently used. In this paper, six machine learning algorithms are applied. Among them Random Forest and Extra trees classifier are best. And in case of neural networks, long short-term memory LSTM is best. Obtained results outperform the existed work that have been previously done in this field.

With the continuous expansion of the banks' credit card businesses, credit card fraud has become a serious threat to banking financial institutions. So, the automatic and real-time credit card fraud detection is the meaningful research work. Because machine learning has the characteristics of non-linearity, automation, and intelligence, so that credit card fraud detection can improve the detection efficiency and accuracy. In view of this, this paper proposes a credit card fraud detection model based on heterogeneous ensemble, namely CUS-RF (cluster-based under-sampling boosting and random forest), based on clustering under-sampling and random forest algorithm. CUS-RF-based credit card fraud detection model has the following advantages. Firstly, the CUS-RF model can better overcome the issue of data imbalance. Secondly, based on the idea of heterogeneous ensemble learning, the clustering under-sampling method and random forest model are fused to achieve a better performance for credit card fraud detection. Finally, through the verification of real credit card fraud dataset, the CUS-RF model proposed in this paper has achieved better performance in credit card fraud detection compared with the benchmark model.

This study aimed to develop a robust machine learning-based phishing detection system using algorithms such as K-nearest neighbour (KNN), artificial neural network (ANN), and random forest (RF). It utilised datasets from Ariyadasa et al. (2021) and UNB (2016) to discern patterns distinguishing legitimate from phishing websites. Furthermore, an objective was to integrate the optimal model into a Django-based web application, facilitating real-time phishing detection. A comprehensive literature review on phishing detection techniques was also undertaken. Datasets chosen underwent rigorous pre-processing to address missing values and imbalance. Feature selection was achieved manually and automatically using mutual information classification. Three machine learning algorithms, RF, KNN, and ANN, were explored. Their hyper-parameters were optimised using GridSearchCV. Performance results highlighted RF's accuracy at 99.78%, KNN's at 99.67%, and ANN's at 99.11%. While RF and KNN models perfectly identified legitimate websites, ANN showcased an impeccable detection of phishing websites. The RF model, with the highest accuracy, was integrated into a Django application, providing a user interface for real-time phishing detection. All models exhibited high accuracy rates, demonstrating their efficacy in phishing detection. While RF was integrated into the web application for this study, the choice between models depends on specific user or business requirements and priorities. Feedback mechanisms within the Django application further promise refinement in future recommendations. The study provides a foundational step toward enhancing web safety through effective phishing detection.

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, where early risk identification is critical for improving survival outcomes. While existing machine learning approaches for lung cancer prediction frequently rely on medical imaging, such methods are costly and often impractical in low-resource clinical settings. This study proposes an efficient and interpretable lung cancer risk prediction framework using demographic, lifestyle, and symptom-based data. A Genetic Algorithm (GA) is employed as a metaheuristic optimization strategy to jointly perform feature selection and hyperparameter tuning of a Random Forest (RF) classifier. To address the inherent class imbalance in the dataset, the Synthetic Minority Oversampling Technique (SMOTE) is applied exclusively to the training data to prevent information leakage and enhance minority-class learning. The proposed GA-optimized RF model is evaluated against several baseline classifiers, including Logistic Regression, Support Vector Machine, k-Nearest Neighbors, Decision Tree, standard Random Forest, and XGBoost, using accuracy, precision, recall, F1-score, and ROCAUC as evaluation metrics. Experimental results demonstrate that the optimized RF model achieves superior performance, with an accuracy of 90.6%, an F1-score of 0.885, and a ROC-AUC of 0.917, outperforming all baseline models. Feature importance analysis identifies smoking habit, breathing difficulties, and throat discomfort as the most influential predictors, aligning with established clinical knowledge. The findings highlight that a metaheuristic-driven optimization approach applied to non-imaging data can provide a cost-effective, reliable, and interpretable solution for early lung cancer risk screening, particularly in resource-constrained healthcare environments.