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Detecting Imbalanced Credit Card Fraud via Hybrid Graph Attention and Variational Autoencoder Ensembles

https://doi.org/10.3390/appliedmath5040131

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Unleashing the power of SDN and GNN for network anomaly detection: State‐of‐the‐art, challenges, and future directions

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A Voting Classifier for Mortality Prediction Post-Thoracic Surgery

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Machine Learning Evaluation of Imbalanced Health Data: A Comparative Analysis of Balanced Accuracy, MCC, and F1 Score

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Modeling Relational Data with Graph Convolutional Networks

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Leveraging Machine Learning Algorithms for Fraud Detection and Prevention in Digital Payments: A Cross Country Comparison

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Catch me if you can: In search of accuracy, scope, and ease of fraud prediction

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Transaction fraud detection via attentional spatial–temporal GNN

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Deep Autoencoder Neural Networks: A Comprehensive Review and New Perspectives

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Deep-efficient-guard: securing wireless ad hoc networks via graph neural network

inferredcites →https://doi.org/10.1007/s44230-022-00004-0openalex-snapshot

Review of Machine Learning Approach on Credit Card Fraud Detection

inferredcites →https://doi.org/10.1016/b978-0-44-332818-3.00020-4openalex-snapshot

Autoencoders

inferredcites →https://doi.org/10.1016/j.asoc.2024.111368openalex-snapshot

Imbalanced credit card fraud detection data: A solution based on hybrid neural network and clustering-based undersampling technique

inferredcites →https://doi.org/10.1016/j.asoc.2025.113190openalex-snapshot

An attention-based balanced variational autoencoder method for credit card fraud detection

inferredcites →https://doi.org/10.1016/j.eswa.2023.119562openalex-snapshot

A novel combined approach based on deep Autoencoder and deep classifiers for credit card fraud detection

inferredcites →https://doi.org/10.1016/j.gltp.2021.01.006openalex-snapshot

Credit card fraud detection using artificial neural network

inferredcites →https://doi.org/10.1016/j.gltp.2022.04.006openalex-snapshot

Exploratory analysis of credit card fraud detection using machine learning techniques

inferredcites →https://doi.org/10.1016/j.ins.2019.05.023openalex-snapshot

HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture

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Credit card fraud detection in the era of disruptive technologies: A systematic review

inferredcites →https://doi.org/10.1016/j.jksuci.2024.102003openalex-snapshot

Encoder–decoder graph neural network for credit card fraud detection

inferredcites →https://doi.org/10.1016/j.procs.2023.01.231openalex-snapshot

Unbalanced Credit Card Fraud Detection Data: A Machine Learning-Oriented Comparative Study of Balancing Techniques

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Review of Classification Methods on Unbalanced Data Sets

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A Neural Network Ensemble With Feature Engineering for Improved Credit Card Fraud Detection

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Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

inferredcites →https://doi.org/10.1109/access.2023.3262020openalex-snapshot

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

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Graph Neural Networks for Intrusion Detection: A Survey

inferredcites →https://doi.org/10.1109/access.2023.3302339openalex-snapshot

Credit Card Fraud Detection Based on Improved Variational Autoencoder Generative Adversarial Network

inferredcites →https://doi.org/10.1109/access.2023.3323842openalex-snapshot

A Novel Framework for Credit Card Fraud Detection

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A Suspicious Financial Transaction Detection Model Using Autoencoder and Risk-Based Approach

inferredcites →https://doi.org/10.1109/access.2024.3466288openalex-snapshot

Enhancing Fraud Detection in Banking With Deep Learning: Graph Neural Networks and Autoencoders for Real-Time Credit Card Fraud Prevention

inferredcites →https://doi.org/10.1109/access.2024.3491175openalex-snapshot

A Credit Card Fraud Detection Algorithm Based on SDT and Federated Learning

inferredcites →https://doi.org/10.1109/access.2024.3502542openalex-snapshot

A Hybrid Deep Learning Ensemble Model for Credit Card Fraud Detection

inferredcites →https://doi.org/10.1109/access.2024.3518205openalex-snapshot

Tabular Data Classification and Regression : XGBoost or Deep Learning with Retrieval-Augmented Generation

inferredcites →https://doi.org/10.1109/iccitm53167.2021.9677867openalex-snapshot

Credit Card Fraud Detection in Financial Transactions Using Data Mining Techniques

inferredcites →https://doi.org/10.1109/iccsai64074.2025.11064532openalex-snapshot

Graph Convolutional Network-Driven Adaptive Learning Framework for Fraud Detection in Complex Transactional Cryptonetworks

inferredcites →https://doi.org/10.1109/iccubea.2018.8697799openalex-snapshot

Detection of Credit Card Fraud Transactions Using Machine Learning Algorithms and Neural Networks: A Comparative Study

inferredcites →https://doi.org/10.1109/icdsis55133.2022.9915901openalex-snapshot

Credit Card Fraud Detection Web Application using Streamlit and Machine Learning

inferredcites →https://doi.org/10.1109/icics49469.2020.239524openalex-snapshot

Credit Card Fraud Detection Based on Machine and Deep Learning

inferredcites →https://doi.org/10.1109/icmlde49015.2019.00019openalex-snapshot

Analysis of Machine Learning Techniques for Credit Card Fraud Detection

inferredcites →https://doi.org/10.1109/icmnwc63764.2024.10872148openalex-snapshot

Hybrid Model for Imbalance Correction in Intrusion Detection Systems Using Advanced Optimization Techniques and Graph Neural Networks

inferredcites →https://doi.org/10.1109/infoteh.2019.8717766openalex-snapshot

Credit Card Fraud Detection - Machine Learning methods

inferredcites →https://doi.org/10.1109/iri.2018.00025openalex-snapshot

Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection: A Comparative Study

inferredcites →https://doi.org/10.1109/ojcs.2025.3543450openalex-snapshot

FraudGNN-RL: A Graph Neural Network With Reinforcement Learning for Adaptive Financial Fraud Detection

inferredcites →https://doi.org/10.1109/ssci.2015.33openalex-snapshot

Calibrating Probability with Undersampling for Unbalanced Classification

inferredcites →https://doi.org/10.1109/tbdata.2025.3562486openalex-snapshot

GE-GNN: Gated Edge-Augmented Graph Neural Network for Fraud Detection

inferredcites →https://doi.org/10.1109/ucc48980.2020.00067openalex-snapshot

Efficient Resampling for Fraud Detection During Anonymised Credit Card Transactions with Unbalanced Datasets

inferredcites →https://doi.org/10.1111/exsy.12795openalex-snapshot

Sequential credit card fraud detection: A joint deep neural network and probabilistic graphical model approach

inferredcites →https://doi.org/10.1177/15501329221106935openalex-snapshot

Research and application of XGBoost in imbalanced data

inferredcites →https://doi.org/10.26599/bdma.2023.9020035openalex-snapshot

An Evaluation of Variational Autoencoder in Credit Card Anomaly Detection

inferredcites →https://doi.org/10.3311/wins2023-005openalex-snapshot

Anomaly Detection using combination of Autoencoder and Isolation Forest

inferredcites →https://doi.org/10.3390/bdcc8010006openalex-snapshot

Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach

inferredcites →https://doi.org/10.3390/electronics11040662openalex-snapshot

Enhanced Credit Card Fraud Detection Model Using Machine Learning

inferredcites →https://doi.org/10.3390/electronics14091754openalex-snapshot

Big Data-Driven Distributed Machine Learning for Scalable Credit Card Fraud Detection Using PySpark, XGBoost, and CatBoost

inferredcites →https://doi.org/10.3390/fi16090318openalex-snapshot

Graph Attention Networks: A Comprehensive Review of Methods and Applications

inferredcites →https://doi.org/10.3390/info16080688openalex-snapshot

Ensemble Large Language Models: A Survey

inferredcites →https://doi.org/10.3390/jcp5010009openalex-snapshot

Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection

inferredcites →https://doi.org/10.3390/math9212683openalex-snapshot

Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest

inferredcites →https://doi.org/10.3390/technologies12100186openalex-snapshot

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

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-31230-3_11openalex-snapshot

AI Powered Detection of Fraud in Banking Using Behavioural Patterns

inferred← citeshttps://doi.org/10.1098/rsos.251922openalex-snapshot

Evaluating fraud detection algorithms in a decentralized scenario

inferred← citeshttps://doi.org/10.1109/icad69378.2026.11609125openalex-snapshot

Quantum-Hybrid Deep Learning Ensemble for Credit Card Fraud Detection

inferred← citeshttps://doi.org/10.1109/iccmc69250.2026.11624710openalex-snapshot

Explainable Graph Neural Network Framework for Digital Transaction Fraud Detection

inferred← citeshttps://doi.org/10.1109/ichora69329.2026.11537105openalex-snapshot

Credit Card Fraud Detection: Addressing Class Imbalance with Machine Learning

inferred← citeshttps://doi.org/10.1109/icisc69558.2026.11681160openalex-snapshot

An Intelligent Federated Learning Framework for Intrusion Detection in IoT Environments Using Metaheuristic Optimization-Enabled Graph Attention Network

inferred← citeshttps://doi.org/10.1109/ictbig68706.2025.11323750openalex-snapshot

A Hybrid FinTech Fraud Detection Model Integrating Multiscale Entropy and Transformer-GAT Techniques

inferred← citeshttps://doi.org/10.1109/isdfs69419.2026.11459094openalex-snapshot

A Hybrid Graph-Based Analysis Framework for Discovering Relational Fraud Patterns

inferred← citeshttps://doi.org/10.1109/tfuzz.2026.3717303openalex-snapshot

Solving Imbalanced Problems With Multilayer Ensemble Balanced Fuzzy Inference Broad Network

inferred← citeshttps://doi.org/10.3390/math14091496openalex-snapshot

Modeling and Optimization of Deep and Machine Learning Methods for Credit Card Fraud Risk Management

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