# Credit Card Fraud Detection Using Advanced Transformer Model

arXiv: https://arxiv.org/abs/2406.03733 · HTML: https://mlsanity.com/paper/2406.03733/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 63 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Pareto Self-Supervised Training for Few-Shot Learning](https://mlsanity.com/paper/2104.07841/) (2021)
- [A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction](https://mlsanity.com/paper/2208.10240/) (2022)
- [Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate](https://mlsanity.com/paper/2402.02769/) (2024)
- [Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagery](https://mlsanity.com/paper/1810.12813/) (2018)
- [MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning](https://mlsanity.com/paper/2312.08636/) (2023)
- [Attention-Enhancing Backdoor Attacks Against BERT-based Models](https://mlsanity.com/paper/2310.14480/) (2023)
- [Regional Style and Color Transfer](https://mlsanity.com/paper/2404.13880/) (2024)
- [Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) data](https://mlsanity.com/paper/2407.00028/) (2024)
- [Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates](https://mlsanity.com/paper/2310.19807/) (2023)
- [Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis](https://mlsanity.com/paper/2404.08003/) (2024)
- [Detect Any Deepfakes: Segment Anything Meets Face Forgery Detection and Localization](https://mlsanity.com/paper/2306.17075/) (2023)
- [Mask-FPAN: Semi-Supervised Face Parsing in the Wild With De-Occlusion and UV GAN](https://mlsanity.com/paper/2212.09098/) (2022)
- [Research on Driver Facial Fatigue Detection Based on Yolov8 Model](https://mlsanity.com/paper/2406.18575/) (2024)
- [Text Sentiment Analysis and Classification Based on Bidirectional Gated Recurrent Units (GRUs) Model](https://mlsanity.com/paper/2404.17123/) (2024)
- [Utilizing Large Language Models for Information Extraction from Real Estate Transactions](https://mlsanity.com/paper/2404.18043/) (2024)
- [Research on Intelligent Aided Diagnosis System of Medical Image Based on Computer Deep Learning](https://mlsanity.com/paper/2404.18419/) (2024)
- [AD-Aligning: Emulating Human-like Generalization for Cognitive Domain Adaptation in Deep Learning](https://mlsanity.com/paper/2405.09582/) (2024)
- [Mapping New Realities: Ground Truth Image Creation with Pix2Pix Image-to-Image Translation](https://mlsanity.com/paper/2404.19265/) (2024)
- [Selective Domain-Invariant Feature for Generalizable Deepfake Detection](https://mlsanity.com/paper/2403.12707/) (2024)
- [Enhancing user experience in large language models through human-centered design: Integrating theoretical insights with an experimental study to meet diverse software learning needs with a single document knowledge base](https://mlsanity.com/paper/2405.11505/) (2024)
- [INTAGS: Interactive Agent-Guided Simulation](https://mlsanity.com/paper/2309.01784/) (2023)
- [TransTARec: Time-Adaptive Translating Embedding Model for Next POI Recommendation](https://mlsanity.com/paper/2404.07096/) (2024)

## Built on by (most cited first, 26 of 26)

- [Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers](https://mlsanity.com/paper/2406.12199/) (2024)
- [CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset](https://mlsanity.com/paper/2406.13113/) (2024)
- [Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach](https://mlsanity.com/paper/2408.06634/) (2024)
- [Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks](https://mlsanity.com/paper/2407.05639/) (2024)
- [Mitigating the Risk of Health Inequity Exacerbated by Large Language Models](https://mlsanity.com/paper/2410.05180/) (2024)
- [Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU model](https://mlsanity.com/paper/2406.13987/) (2024)
- [Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methods](https://mlsanity.com/paper/2408.04268/) (2024)
- [AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing](https://mlsanity.com/paper/2407.05633/) (2024)
- [Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis](https://mlsanity.com/paper/2407.16150/) (2024)
- [NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training](https://mlsanity.com/paper/2409.09582/) (2024)
- [Style Transfer: From Stitching to Neural Networks](https://mlsanity.com/paper/2409.00606/) (2024)
- [Leveraging Contrastive Learning and Self-Training for Multimodal Emotion Recognition with Limited Labeled Samples](https://mlsanity.com/paper/2409.04447/) (2024)
- [A Review of Electromagnetic Elimination Methods for low-field portable MRI scanner](https://mlsanity.com/paper/2406.17804/) (2024)
- [Deep Learning for Lung Disease Classification Using Transfer Learning and a Customized CNN Architecture with Attention](https://mlsanity.com/paper/2408.13180/) (2024)
- [CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction](https://mlsanity.com/paper/2409.13701/) (2024)
- [Multicam-SLAM: Non-overlapping Multi-camera SLAM for Indirect Visual Localization and Navigation](https://mlsanity.com/paper/2406.06374/) (2024)
- [Transforming Movie Recommendations with Advanced Machine Learning: A Study of NMF, SVD,and K-Means Clustering](https://mlsanity.com/paper/2407.08916/) (2024)
- [Recoverable Anonymization for Pose Estimation: A Privacy-Enhancing Approach](https://mlsanity.com/paper/2409.02715/) (2024)
- [Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models](https://mlsanity.com/paper/2410.19241/) (2024)
- [Improving Credit Card Fraud Detection through Transformer-Enhanced GAN Oversampling](https://mlsanity.com/paper/2509.19032/) (2025)
- [MonoMM: A Multi-scale Mamba-Enhanced Network for Real-time Monocular 3D Object Detection](https://mlsanity.com/paper/2408.00438/) (2024)
- [RW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling](https://mlsanity.com/paper/2408.06665/) (2024)
- [Redefining Data-Centric Design: A New Approach with a Domain Model and Core Data Ontology for Computational Systems](https://mlsanity.com/paper/2409.09058/) (2024)
- [Behavioral Sequence Modeling with Ensemble Learning](https://mlsanity.com/paper/2411.02174/) (2024)
- [Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/2507.01924/) (2025)
- [CARE: A Cascaded Framework for Efficient and Reliable Time Series Anomaly Detection](https://mlsanity.com/paper/2608.01885/) (2026)

## Abstract (arXiv)

With the proliferation of various online and mobile payment systems, credit card fraud has emerged as a significant threat to financial security. This study focuses on innovative applications of the latest Transformer models for more robust and precise fraud detection. To ensure the reliability of the data, we meticulously processed the data sources, balancing the dataset to address the issue of data sparsity significantly. We also selected highly correlated vectors to strengthen the training process.To guarantee the reliability and practicality of the new Transformer model, we conducted performance comparisons with several widely adopted models, including Support Vector Machine (SVM), Random Forest, Neural Network, and Logistic Regression. We rigorously compared these models using metrics such as Precision, Recall, and F1 Score. Through these detailed analyses and comparisons, we present to the readers a highly efficient and powerful anti-fraud mechanism with promising prospects. The results demonstrate that the Transformer model not only excels in traditional applications but also shows great potential in niche areas like fraud detection, offering a substantial advancement in the field.