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Toward interpretable credit scoring: integrating explainable artificial intelligence with deep learning for credit card default prediction
In recent years, the increasing prevalence of credit card usage has raised concerns about accurately predicting and managing credit card defaults....
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A Comprehensive Fraud Detection for Credit Card Transactions in Federated Averaging
Credit card fraud costs card issuers billions of dollars each year. Therefore, an effective Fraud Detection System (FDS) is crucial to minimize...
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Auto-Encoder and LSTM-Based Credit Card Fraud Detection
The increased fraud risk due to the most recent methods of paying with a credit card, such as real-time payments and cards with near-field...
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A voting ensemble machine learning based credit card fraud detection using highly imbalance data
Long gone is the time when people preferred using only cash. In recent years, cashless transactions have gained much popularity, be it using UPI apps...
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CFTNet: a robust credit card fraud detection model enhanced by counterfactual data augmentation
Establishing a reliable credit card fraud detection model has become a primary focus for academia and the financial industry. The existing anti-fraud...
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Credit Card Fraud Detection: Addressing Imbalanced Datasets with a Multi-phase Approach
Credit card fraud detection plays a crucial role in safeguarding the financial security of individuals and organizations. However, imbalanced...
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Enhanced particle swarm optimization-based hyperparameter optimized stacked autoencoder for credit card fraud detection
In recent years, several fraud attempts have been made in various sectors including finance, banking and insurance. In fact, credit card fraud refers...
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Credit card fraud detection using ensemble data mining methods
Nowadays, credit card fraud has become one of the most complex and vital issues in the world, even more than the past decades. Widespread use of...
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Comparative analysis of binary and one-class classification techniques for credit card fraud data
The yearly increase in incidents of credit card fraud can be attributed to the rapid growth of e-commerce. To address this issue, effective fraud...
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Synthesizing class labels for highly imbalanced credit card fraud detection data
Acquiring labeled datasets often incurs substantial costs primarily due to the requirement of expert human intervention to produce accurate and...
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Federated learning model for credit card fraud detection with data balancing techniques
In recent years, credit card transaction fraud has resulted in massive losses for both consumers and banks. Subsequently, both cardholders and banks...
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An intelligent credit card fraudulent activity detection using hybrid deep learning algorithm
Problem StatementWith the rapid growth of internet usage, businesses, including those in the financial sector, are increasingly providing their...
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CDGAT: a graph attention network method for credit card defaulters prediction
Recognizing potential defaulters is a crucial problem for financial institutions. Therefore, many credit scoring methods have been proposed in the...
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Credit Card Fraud Detection Using Machine Learning
This research explores the application of Machine Learning (ML) algorithms in the detection of credit card fraud. Credit card fraudulence is a major...
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Threshold optimization and random undersampling for imbalanced credit card data
Output thresholding is well-suited for addressing class imbalance, since the technique does not increase dataset size, run the risk of discarding...
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The effect of feature extraction and data sampling on credit card fraud detection
Training a machine learning algorithm on a class-imbalanced dataset can be a difficult task, a process that could prove even more challenging under...
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An efficient fraud detection framework with credit card imbalanced data in financial services
Credit card fraud has adversely impacted market economic order and has broken stakeholders, financial entities, and consumers’ trust and interest....
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Weighted binary ELM optimized by the reptile search algorithm, application to credit card fraud detection
Binary classification tasks often face challenges due to imbalanced datasets. The Weighted Binary Extreme Learning Machine (WB-ELM) has emerged as a...
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Smart credit card fraud detection system based on dilated convolutional neural network with sampling technique
Numerous organization including financial industry are highly supported the online service payments due to the massive growth of Internet commerce...
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Review of Machine Learning Approach on Credit Card Fraud Detection
Massive usage of credit cards has caused an escalation of fraud. Usage of credit cards has resulted in the growth of online business advancement and...
