Credit Card Fraud Research Articles

A new fusion neural network model and credit card fraud identification.

Credit card fraud identification is an important issue in risk prevention and control for banks and financial institutions. In order to establish an efficient credit card fraud identification model, this article studied the relevant factors that affect fraud identification. A credit card fraud identification model based on neural networks was constructed, and in-depth discussions and research were conducted. First, the layers of neural networks were deepened to improve the prediction accuracy of the model; second, this paper increase the hidden layer width of the neural network to improve the prediction accuracy of the model. This article proposes a new fusion neural network model by combining deep neural networks and wide neural networks, and applies the model to credit card fraud identification. The characteristic of this model is that the accuracy of prediction and F1 score are relatively high. Finally, use the random gradient descent method to train the model. On the test set, the proposed method has an accuracy of 96.44% and an F1 value of 96.17%, demonstrating good fraud recognition performance. After comparison, the method proposed in this paper is superior to machine learning models, ensemble learning models, and deep learning models.

Improvement The Accuracy of Convolutional Neural Network with Using Undersampling Method on Unbalanced Credit Card Dataset

In this study, we address the challenge of imbalanced data in credit card fraud detection by proposing a novel approach that leverages Convolutional Neural Networks (CNNs) and undersampling techniques. The imbalance in the dataset, typical of real-world financial transactions, often leads to biased models favoring the majority class. To mitigate this, we employ undersampling to balance the classes, thereby enhancing the CNN's ability to learn from minority instances crucial for fraud detection. Our method is validated on a large unbalanced credit card dataset, demonstrating significant improvements in accuracy compared to traditional CNN models trained on imbalanced data. We evaluate our approach using standard performance metrics, including precision, recall, and F1-score, showcasing its effectiveness in accurately identifying fraudulent transactions while minimizing false positives. Furthermore, we pro-vide insights into the CNN's decision-making process through visualization techniques, shedding light on its ability to discern fraudulent patterns within the data. Our findings highlight the importance of addressing class imbalance in fraud detection tasks and underscore the efficacy of undersampling in enhancing the performance of deep learning models, particularly CNNs, in handling imbalanced datasets.

A Supervised Ml Algorithm for Detecting and Predicting Fraud Credit Card Transactions

A Credit card fraud presents an escalating threat in today's digital economy, leading to significant financial losses for consumers, businesses, and financial institutions. Traditional detection methods are increasingly inadequate due to the rise in online transactions and the evolving complexity of fraudulent activities. This research aims to create a reliable supervised machine learning model designed to detect and predict fraudulent credit card transactions in real-time. Our approach leverages advanced algorithms and extensive datasets to enhance transaction security, thereby mitigating financial risks. Current systems, like FICO's Fraud Detection System (FDS), utilize a range of supervised learning techniques, including Decision Trees, Random Forests, and Neural Networks. However, they face limitations, including data quality issues, high false positive rates, and the need for continuous retraining to adapt to evolving fraud patterns. To address these challenges, we propose an innovative anomaly detection method based on the Isolation Forest algorithm. Unlike traditional methods that rely on distance and density measures, Isolation Forests isolate anomalies by randomly selecting features and split values, making it more efficient in identifying fraudulent transactions, especially in imbalanced datasets. The proposed system boasts low linear time complexity and minimal memory requirements, enabling the construction of high-performing models even with large datasets. The research demonstrates that the Isolation Forest approach significantly outperforms traditional models in detecting credit card fraud, offering a promising solution for enhancing security in the financial ecosystem.

A Review on Robust Credit Card Fraud Detection System Leveraging Big Data and Machine Learning

This review offers a detailed strategy to address the growing threat of credit card fraud in today's digital landscape. By utilizing Big Data analytics alongside machine learning methods, the system aims to transform fraud detection processes. It tackles the challenges arising from the increasing volume and complexity of credit card transactions, enabling the real-time detection and prevention of fraudulent actions. The system employs sophisticated machine learning algorithms to identify patterns and anomalies linked to fraudulent activities, allowing for proactive responses to emerging fraud tactics. Additionally, the system is optimized to handle and analyze large datasets efficiently, ensuring timely and precise detection of fraud. It also incorporates strong security protocols to protect sensitive customer data while adhering to privacy regulations. This review ultimately seeks to enhance the safety and reliability of electronic payments, protecting financial institutions and consumers from the harmful effects of credit card fraud.

An AutoEncoder enhanced light gradient boosting machine method for credit card fraud detection

Online financial transactions bring convenience to people’s lives, but also present vulnerabilities for criminals to embezzle users’ accounts and trick users into credit card fraud. Although machine learning methods have been adopted to detect anomalous transactions, it’s hard for a single machine learning method to achieve satisfying results with the increasing scale and dimensionality of financial datasets. In addition, for anomaly detection of financial data, there is an obvious imbalance between normal records and abnormal. In this situation, the experimental results cannot be objectively evaluated only by the traditional metrics, such as precision, recall, and accuracy. This paper proposes an AutoEncoder enhanced LightGBM method for credit card detection. The method inherits the advantages of each component, using an AutoEncoder for feature reconstruction on the dataset, and integrating the LightGBM algorithm for improving the GBDT (Gradient Boosting Decison Tree) to detect abnormal data more accurately and efficiently. Besides the traditional evaluation metrics, F-measure, area under curve (AUC), Matthew’s correlation coefficient (MCC), and balanced classification rate (BCR) are also adopted as the evaluation metrics. Two financial datasets were used to validate the performance and robustness of the proposed model. Results obtained from the credit card fraud dataset containing 31 features indicate that our model significantly outperforms other models with a recall of 94.85%, representing a 10.70% improvement compared to the best detection performance model with a recall of only 86%. Additionally, our model’s BCR score is also significantly better than other models, with a BCR score of 97%, as opposed to the best detection performance model’s BCR score of 92%, representing a 5% improvement by our model. Various sampling methods and model combinations were considered in this study. It was found that the SMOTE algorithm combined with the proposed model produced the best results, with an AUC value of 96.83% and an F-measure score of 80.27%. The Santander bank transaction record dataset is a high dimensional large dataset containing 200 features. Experimental results on this dataset reveal that compared to other models, our model significantly improved recall and F-measure results, raising the recall to 94.14% and the F-measure score by 11.51%, surpassing the second-best-performing model. Overall, these findings demonstrate the robustness and superiority of our model in detecting fraudulent transactions and highlight the effectiveness of the SMOTE algorithm in combination with the proposed model.

Efficient Credit Card Fraud Detection System Using Big Data and Machine Learning

This review offers a detailed strategy to address the growing threat of credit card fraud in today's digital landscape. By utilizing Big Data analytics alongside machine learning methods, the system aims to transform fraud detection processes. It tackles the challenges arising from the increasing volume and complexity of credit card transactions, enabling the real-time detection and prevention of fraudulent actions. The system employs sophisticated machine learning algorithms to identify patterns and anomalies linked to fraudulent activities, allowing for proactive responses to emerging fraud tactics. Additionally, the system is optimized to handle and analyze large datasets efficiently, ensuring timely and precise detection of fraud. It also incorporates strong security protocols to protect sensitive customer data while adhering to privacy regulations. This review ultimately seeks to enhance the safety and reliability of electronic payments, protecting financial institutions and consumers from the harmful effects of credit card fraud.

Deep adaptive ensemble learning for imbalanced credit card fraud detection

ABSTRACT Traditional techniques for credit card fraud detection often struggle with highly imbalanced datasets, which compounds the complexity of fraudulent transaction detection. To tackle this issue, the current paper puts forth a novel technique known as deep adaptive ensemble learning (DAEL). Initially, we employ the ensemble-based under-sampling method to bring equilibrium to the data. This is followed by the application of a deep autoencoder for the purpose of feature extraction and dimensionality reduction. Then, a bidirectional long short-term memory model is trained using the features extracted from the autoencoder. Finally, an adaptive weighting technique is applied to unify multiple models. The empirical results demonstrate that the proposed DAEL technique significantly surpasses individual models and conventional ensemble learning techniques in performance.

FRAUD DETECTION IN FINANCIAL TRANSACTIONS THROUGH DATA SCIENCE FOR REAL-TIME MONITORING AND PREVENTION

This study presents a comprehensive review of the use of advanced technologies in credit card fraud detection, with a focus on machine learning, blockchain, and federated learning, to understand their transformative impact on the field. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 97 articles were systematically reviewed and analyzed. The findings reveal that machine learning models, such as decision trees, support vector machines, and neural networks, have significantly improved fraud detection accuracy, reducing false positives and enhancing the ability to detect complex fraud patterns in real-time. Blockchain technology also plays a critical role by providing a decentralized, secure, and transparent framework for fraud detection, ensuring the integrity of transaction records and making fraudulent activities harder to conceal. Federated learning offers a privacy-preserving solution, enabling institutions to collaborate on fraud detection without sharing sensitive data, which is increasingly important in light of stringent regulatory requirements. Additionally, the study highlights the growing use of predictive analytics in forecasting potential fraud, allowing financial institutions to proactively prevent fraud before it occurs. Moreover, feedback loops integrated into fraud detection models allow for continuous improvement, ensuring that detection systems can adapt to new and evolving fraud tactics. Overall, the review underscores the importance of adopting these advanced technologies to build more secure, efficient, and adaptive fraud detection systems capable of safeguarding financial transactions in the modern digital economy.

A Hybrid Deep Learning Approach with Generative Adversarial Network for Credit Card Fraud Detection

Credit card fraud detection is a critical challenge in the financial industry, with substantial economic implications. Conventional machine learning (ML) techniques often fail to adapt to evolving fraud patterns and underperform with imbalanced datasets. This study proposes a hybrid deep learning framework that integrates Generative Adversarial Networks (GANs) with Recurrent Neural Networks (RNNs) to enhance fraud detection capabilities. The GAN component generates realistic synthetic fraudulent transactions, addressing data imbalance and enhancing the training set. The discriminator, implemented using various DL architectures, including Simple RNN, Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs), is trained to distinguish between real and synthetic transactions and further fine-tuned to classify transactions as fraudulent or legitimate. Experimental results demonstrate significant improvements over traditional methods, with the GAN-GRU model achieving a sensitivity of 0.992 and specificity of 1.000 on the European credit card dataset. This work highlights the potential of GANs combined with deep learning architectures to provide a more effective and adaptable solution for credit card fraud detection.

A Spatial–Temporal Gated Network for Credit Card Fraud Detection by Learning Transactional Representations

A Spatial–Temporal Gated Network for Credit Card Fraud Detection by Learning Transactional Representations

Fraud events in the coolest of neighbourhoods: A case study from Gothenburg, Sweden

AbstractIn the current study credit card frauds are analysed. Concepts are taken from crime pattern theory and place-based criminology. The empirical material originates from the urban nightlife of Gothenburg, Sweden, and combines quantitative and qualitative data from different sources. Instead of focusing on the individual’s specific motives for committing crimes, the study has its interest in how place can play a part in fraud events and be a necessary condition in the crime modus operandi. In the conducted analysis, we can clearly see that place matters in the crime series studied, and that the place was not randomly chosen by the offenders.

Machine Learning for Improved Detection and Prevention of Credit Card Fraud

Using machine learning to identify instances of credit card fraud is the primary goal of the research. In order to accomplish this, we will research and assess four distinct classification models: Decision Tree, Logistic Regression, K-Nearest Neighbours (KNN), and Support Vector Classifier (SVC). Included in the Kaggle dataset's "Time," "Amount," and "Class" sections are transaction data points that represent the likelihood of fraud. Disparities in class distribution are a big problem with this dataset, even though 99.83% of the data comes from legitimate transactions. Disparity like this has the potential to lead to biassed model training. We resolved the issue by implementing preprocessing techniques such as feature scaling and resampling. F1-Score, recall, accuracy, and precision are the measures that we use to assess the models. With scores ranging from 93% to 94%, the data demonstrate that all models are incredibly accurate. Having said that, Logistic Regression lacks precision. All things considered, Decision Tree, KNN, and SVC perform very well. The proposed model's accuracy peaked at 94%. As this study demonstrates, thorough evaluation criteria are necessary for finding the optimal model for fraud detection.

Cyber Bullying Prevention in Learning Institutions: Systematic Approach

Cyberbullying has been identified as a biggest problem for the last decade. In this paper we will discuss some recent findings and focus on general concepts within the area. This paper covers issues like repetition and power imbalance, types of cyberbullying, age, cyberbullying and traditional bullying, motives for and impact of cyber victimization, coping strategies, and prevention/intervention possibilities. Understanding the characteristics and methods of typical cybercrimes can help us better arm ourselves with the information and resources needed to reduce risks, improve cyber-security, and protect our online identities. However, hackers, phishing fraudsters, and other cybercriminals seized the chance to profit greatly from this chaos. Phishing assaults and cybercrime complaints have skyrocketed throughout the epidemic, putting a great deal of people & small organizations at danger of identity theft, credit card fraud, or data breaches.

The Data Analysis of Enterprise Operational Risk Prediction Under Machine Learning

In the digital age, the financial sector faces increasingly severe risk management challenges. Traditional methods often rely on historical data and statistical models, which struggle to cope with the high volatility of the market. These methods exhibit poor adaptability in rapidly changing financial markets and often fail to meet demands in terms of accuracy and reliability. To address these issues, this study proposes a law risk prediction model based on deep learning—the WBIF model. This model integrates Bidirectional Long Short-Term Memory (BiLSTM) and Fully Convolutional Networks (FCN) and employs the Whale Optimization Algorithm (WOA) for parameter optimization. Experimental results show that compared to traditional models, the WBIF model reduces the Mean Absolute Error (MAE) by 51.73% on the UCI machine learning library dataset and improves accuracy by 12% on the Kaggle credit card fraud detection dataset.

Leveraging graph-based learning for credit card fraud detection: a comparative study of classical, deep learning and graph-based approaches

Leveraging graph-based learning for credit card fraud detection: a comparative study of classical, deep learning and graph-based approaches

FBLearn: Decentralized Platform for Federated Learning on Blockchain

In recent years, rapid technological advancements have propelled blockchain and artificial intelligence (AI) into prominent roles within the digital industry, each having unique applications. Blockchain, recognized for its secure and transparent data storage, and AI, a powerful tool for data analysis and decision making, exhibit common features that render them complementary. At the same time, machine learning has become a robust and influential technology, adopted by many companies to address non-trivial technical problems. This adoption is fueled by the vast amounts of data generated and utilized in daily operations. An intriguing intersection of blockchain and AI occurs in the realm of federated learning, a distributed approach allowing multiple parties to collaboratively train a shared model without centralizing data. This paper presents a decentralized platform FBLearn for the implementation of federated learning in blockchain, which enables us to harness the benefits of federated learning without the necessity of exchanging sensitive customer or product data, thereby fostering trustless collaboration. As the decentralized blockchain network is introduced in the distributed model training to replace the centralized server, global model aggregation approaches have to be utilized. This paper investigates several techniques for model aggregation based on the local model average and ensemble using either local or globally distributed validation data for model evaluation. The suggested aggregation approaches are experimentally evaluated based on two use cases of the FBLearn platform: credit risk scoring using a random forest classifier and credit card fraud detection using a logistic regression. The experimental results confirm that the suggested adaptive weight calculation and ensemble techniques based on the quality of local training data enhance the robustness of the global model. The performance evaluation metrics and ROC curves prove that the aggregation strategies successfully isolate the influence of the low-quality models on the final model. The proposed system’s ability to outperform models created with separate datasets underscores its potential to enhance collaborative efforts and to improve the accuracy of the final global model compared to each of the local models. Integrating blockchain and federated learning presents a forward-looking approach to data collaboration while addressing privacy concerns.

Credit Shield Solutions: Credit Card Fraud Detection System Using Machine Learning Approach

Credit Shield Solutions: Credit Card Fraud Detection System Using Machine Learning Approach

An in-depth PRISMA based review of cybercrime in a developing economy: Examining sector-wide impacts, legal frameworks, and emerging trends in the digital era

In today's digital age, the swift advancement of information and communication technology (ICT) has brought immense benefits to humanity, but it has also opened the door to various technological crimes, particularly cybercrime, which is becoming an increasingly significant issue in Bangladesh. This paper aspires to critically examine the nature and scope of cybercrime in Bangladesh, exploring its impact on individuals, businesses, and the economy as a whole. This study investigates the state of cybercrime in Bangladesh, its sector-wide impacts, and the effectiveness of existing legal government law in combating these crimes. a survey alongside semi-structured interviews and a variety of sources, including Scopus, Web of Science, DOAJ, Scimago, and Google Scholar databases. The study also utilized PRISMA based methodology to find out the research gaps in the context to increase deeper insights and understanding into technology-related crimes. Cybercrime (hacking, phishing and credit Card Fraud, financial fraud etc) greatly affected economic growth, emerging trends, different sectors, banking, finance and legal frameworks which are emerging economies that have the mediation process of expanding factors in Bangladesh. Additionally, financially motivated attackers, politically motivated attackers and espionage motivated attackers are the main reasons for cybercrime. As Bangladesh rapidly adopts digital technologies, the study identifies the emerging factors, patterns, underlying logic, the role of law enforcement agencies and cybersecurity measures related to cybercrime. The implication will be presented in the increasingly alarming consequences of cybercrime and suggests strengthening existing global cyber laws to more effectively fight these crimes.

CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING

The rise of e-commerce and digital payment systems has been accompanied by an increase in financial fraud, especially involving credit cards. Ensuring the detection of fraudulent activities is crucial to protecting users' financial assets and preserving trust in online transactions. This study introduces a novel method for detecting credit card fraud by integrating machine learning (ML) techniques with a genetic algorithm (GA) for feature selection. Feature selection plays a vital role in improving fraud detection models by pinpointing the most relevant features linked to fraudulent transactions. The effectiveness of the proposed method is assessed using a dataset from European cardholders, a widely used benchmark in this research area. In terms of performance, the Isolation Forest Algorithm exhibits slightly higher accuracy compared to the Local Outlier Factor (LOF) Algorithm. Consequently, based on accuracy alone, the Isolation Forest Algorithm is deemed more effective for this specific dataset. Keywords: Credit card fraud detection, Machine learning (ML), E-commerce.

Identifying Credit Card Fraud with Machine Learning: Evaluation of Algorithms and Oversampling Techniques

The physical loss of a credit card or the theft of sensitive credit card data is referred to as credit card fraud. For detection, a variety of machine learning methods can be applied. This study presents several algorithms for identifying transactions as authentic or fraudulent. The study used the credit card fraud detection dataset. The SMOTE approach was utilized for oversampling due to the extremely unbalanced nature of the dataset. Additionally, a feature selection process was carried out, and the dataset was divided into training and test sets. The experiment employed eight machine learning models, including Random Forest, AdaBoost, Support Vector Classifier, Extreme Gradient Boost, and Logistic Regression algorithms. The findings indicate that few algorithms have a high degree of accuracy when detecting credit card fraud. The Random Forest model can be applied to find further anomalies. This approach has proven to be effective in accurately detecting fraudulent activity and reducing the number of false positives and negatives. The results of the experiment also highlight the importance of feature selection in improving the performance of the models by the highest accuracy score with 96%, precision of 100, Recall of 91, and F1Scroe of 95. By using a combination of different machine learning algorithms, financial institutions can enhance their fraud detection systems and better protect their customers from potential financial losses. The findings from this study demonstrate the potential for significant advancements in the field of fraud detection using machine learning techniques.