An intelligent credit card fraud detection model using data mining and ensemble learning

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.

Along with increasing credit cards and growing trade volume in China, credit card fraud rises sharply. How to enhance the detection and prevention of credit card fraud becomes the focus of risk control of banks. This paper proposes a credit card fraud detection model using outlier detection based on distance sum according to the infrequency and unconventionality of fraud in credit card transaction data, applying outlier mining into credit card fraud detection. Experiments show that this model is feasible and accurate in detecting credit card fraud.

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.

With increased internet usage, online transactions have been on the rise. One of the most prevalent problems faced is credit cards frauds. While web applications and mailing services are heavily spammed, the upsurge of handheld mobile devices has led to an outburst of heavy mobile credit card spamming. The matter is more severe in mobile devices due to lesser sophisticated filtering mechanisms in built in mobile operating systems. Recent advancements in electronic commerce and communication systems have significantly increased the use of credit cards for both online and regular transactions. However, there has been a steady rise in fraudulent credit card transactions, costing financial companies huge losses every year. The development of effective fraud detection algorithms is vital in minimizing these losses, but it is challenging because most credit card datasets are highly imbalanced. This work proposes a supervised machine learning algorithm to be trained to detect credit card frauds based on the Bayes Net with penalty based regularization. It is shown that the proposed approach attains higher classification accuracy compared to existing work. Keywords— Credit Card Fraud Detection, Machine Learning, Feature Selection, Imbalanced Datasets, Probabilistic Classifier, Classification Accuracy

Credit card fraud is a common occurrence in today’s society because the majority of us use credit cards as a form of payment more frequently. This is the outcome of developments in technology and an increase in online transactions, which have given rise to frauds that have caused significant financial losses. In order to detect fraud in credit card transactions, efficient and effective approaches are needed. In this study, we developed a hybrid CNN‐SVM model for detecting fraud in credit card transactions. The effectiveness of our suggested hybrid CNN‐SVM model for detecting fraud in credit card transactions was tested using real‐world public credit card transaction data. The architecture of our hybrid CNN‐SVM model was developed by replacing the final output layer of the CNN model with an SVM classifier. The first classifier is a fully connected layer with softmax that is trained using an end‐to‐end approach, whereas the second classifier is a support vector machine that is piled on top by deleting the final fully connected and softmax layer. According to experimental results, our hybrid CNN‐SVM model produced classification performances with accuracy, precision, recall, F1‐score, and AUC of 91.08%, 90.50%, 90.34%, 90.41, and 91.05%, respectively.

Credit card fraud is a widespread cybercrime that threatens financial security. Effective cybersecurity measures are essential to mitigate these risks. Machine learning has shown promising results in detecting credit card fraud by analyzing transaction data and identifying patterns of suspicious behavior. Feature selection is crucial in machine learning because it simplifies the model, improves its performance, and prevents overfitting. This research introduces a machine learning model designed for credit card fraud detection. The model makes use of three types of correlations. Pearson, Spearman, and Kendall, to identify features and enhance the fraud detection process. Testing on datasets yielded impressive results achieving category accuracies of 99.95% and 99.58% surpassing alternative approaches. Also, the results showed that Kendall correlation is the best among the three types of correlation in selecting attributes in all approved datasets.

With the continuous development of the economy, the widespread use of credit cards shows a growing trend. However, simultaneously, the risk of credit card fraud still exists and cannot be ignored. This article utilizes bank card data for model training, with a particular focus on addressing the high dimensionality and imbalance of data in credit card transactions. In terms of model construction, it introduces a reinforcement learning-based Long Short-Term Memory (LSTM) network. Additionally, to improve the classification and recognition performance of the model, ensemble learning algorithms and convolutional neural networks are employed for enhancement. The research results indicate that, compared to other methods, the approach proposed in this article demonstrates a higher level of performance in the AUC (Area Under the Curve) metric of 0.92, effectively preventing financial risks, and providing a more reliable solution for credit card fraud detection. In conclusion, after comparing numerous models, it is evident that LSTM is an effective algorithm choice, particularly suitable for credit card fraud detection tasks.

Nowadays, in the digital era, the use of credit cards is widespread. People buy and sell goods from home. E-commerce brings a boom to the lifestyle of the people, for the working family, it is a preferable tool to purchase and pay through online mode in order to save the time and visiting hours. However, with the increasing use of credit cards, there have also been a lot of cases of credit card frauds that augmented much in the corona period. Fake credit card transactions significantly affect financial institutions, banks, and clients, whereas fraudsters try to develop new ways of committing fraud daily. To avoid credit card fraud and maintain the credibility of our clients, we need to build a model for credit card detection. There are the various machine learning tools that can be applied in detection of such frauds, these tools can be used to design an enhanced model to detect credit card frauds efficiently. In this view, the study presents a feature selection technique approach of machine learning with different classifiers. The study proposed an optimization algorithm using ‘Firefly’ for feature selection with four different classifiers: neural network, k-nearest neighbors, Support Vector Machine, and decision tree. The performance of all these four classifiers is compared on the basis of accuracy parameter.

In today’s context, the term “fraud” has become closely intertwined with credit card-related deceit. Recent years have witnessed a notable surge in both credit card utilization and fraudulent activities. Detecting and thwarting fraud necessitates a meticulous analysis of customers’ spending patterns. The ubiquity of credit card use for both online and in-store transactions has un-fortunately led to a parallel rise in recognition valentine scam occurrences. While the primary area of deception discovery is the documentation of sham incidents, the urgency of promptly flagging such events cannot be overstated. The con-temporary landscape heavily favors credit card usage, a trend that inadvert-ently contributes to the annual expansion of fraudulent gains. This unlawful practice exerts a pernicious influence on the global economy at large, exac-erbating its impact year after year. Numerous cutting-edge methods, including as data mining, machine learning, fuzzy logic, genetic programming, sequence alignment, artificial intelligence, and fuzzy logic, have become indispensable in the fight against this threat when it comes to identifying credit card fraud. This study delves into the intricate integration of data mining methodologies, showcas-ing their robust potential to provide comprehensive coverage against fraudu-lent activities while maintaining a controlled balance between false alarms and detection accuracy. Within the financial sector, the challenge of credit card fraud detection remains both persistent and pressing. This paper intro-duces an innovative paradigm aimed at fortifying credit card fraud detection. This is achieved by synergistically harnessing the capabilities of the Artificial Underground Over-sampling Practice (SMOTE), the potency of Adaptive Increasing (ADABoost), and the privacy-enhancing attributes of Federated Learning. The incorporation of federated learning serves a dual purpose: not only does it address prevailing data privacy concerns, but it also significantly augments the precision of fraud detection across a diverse array of geograph-ically distributed data sources

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.

The area of fraud detection has been traditionally correlated with data mining and text mining. Even before the "big data" phenomena started in 2008, text mining and data mining were used as instruments of fraud detection. However, the limited technological capabilities of the pre-big data technologies made it very difficult for researchers to run fraud detection algorithms on large amounts of data. This paper reviews various existing learning methods for financial fraud detection of credit card fraud across different areas and find out their strengths and weaknesses. This paper has developed a credit card fraud detection system using XGBoost (eXtreme Gradient Boosting) method. In the proposed system, data imbalance problem has also been resolved by using SMOTE. Results are evaluated and compared with many states of art methods & found that the presented method performs well in achieving more accurate results after resolving data imbalance.

The aim of this research is to identify the credit card fraud detection model using an improved K-nearest neighbor classifier method to find the credit card fraudulent transactions with accuracy percentage compared with Logistic regression algorithm. Materials and Methods: Logistic regression algorithm with sample size is 26 and K-nearest neighbor classifier method with sample size is 26 was iterated for predicting accuracy percentage of credit card fraud detection. The study contains two groups where the K-nearest neighbor classifier method algorithm is analysed in the first group, the logistic regression algorithm is analysed in the second group. The G power test value is 0.8 and the accuracies of each algorithm are compared. Results: Logistic regression algorithm is significantly better accuracy 92% compared to K-nearest neighbor 90% with the power value of 80% The two algorithms KNN and logistic regression are statistically not satisfied with the independent sample T-Test <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${(\alpha=.881)}$</tex> value <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${(\mathrm{p}=0.05)}$</tex> with a confidence level of 95%. Conclusion: The outcome of the proposed algorithm was compared with the K-nearest neighbor classifier method algorithm and the proposed algorithm has significantly better accuracy of 92% compared with Logistic regression algorithm.

The rapid growth of e-commerce and internet lead to a wide increase in the credit card use. Due to increase in the usage of credit cards there is vast increase in the credit card frauds in the recent years. Artificial neural network considers effectiveness of neural networks in the detecting credit card frauds. This system trains the neural network based on the data of the customer from the past. Training artificial neural network is difficult because every time the activity is different in each of the transaction. So, we can conFigure an artificial neural network with a genetic algorithm for this task. We create a combination of simulated annealing and genetic algorithm to get better results. Genetic algorithm uses a biological way to find the fittest solution from various solutions. Simulated annealing has effective algorithm which on combining with Genetic algorithm proves efficient in the detecting credit card frauds. Simulated annealing after processing credit card details of owner passes through ANN network for detection.

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.

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