Fraud Detection
130 papers with code • 12 benchmarks • 11 datasets
Fraud Detection is a vital topic that applies to many industries including the financial sectors, banking, government agencies, insurance, and law enforcement, and more. Fraud endeavors have detected a radical rise in current years, creating this topic more critical than ever. Despite struggles on the part of the troubled organizations, hundreds of millions of dollars are wasted to fraud each year. Because nearly a few samples confirm fraud in a vast community, locating these can be complex. Data mining and statistics help to predict and immediately distinguish fraud and take immediate action to minimize costs.
Source: Applying support vector data description for fraud detection
Benchmarks
These leaderboards are used to track progress in Fraud Detection
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Libraries
Use these libraries to find Fraud Detection models and implementationsDatasets
Yelp
Yelp-Fraud
Amazon-Fraud
Vehicle Claims
CIDII Dataset
Most implemented papers
Continuous-variable quantum neural networks
XanaduAI/quantum-neural-networks
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The quantum neural network is a variational quantum circuit built in the continuous-variable (CV) architecture, which encodes quantum information in continuous degrees of freedom such as the amplitudes of the electromagnetic field.
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
somepago/saint
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We devise a hybrid deep learning approach to solving tabular data problems.
Deep Anomaly Detection with Deviation Networks
GuansongPang/deviation-network
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Instead of representation learning, our method fulfills an end-to-end learning of anomaly scores by a neural deviation learning, in which we leverage a few (e. g., multiple to dozens) labeled anomalies and a prior probability to enforce statistically significant deviations of the anomaly scores of anomalies from that of normal data objects in the upper tail.
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
Finally, the selected neighbors across different relations are aggregated together.
Credit Card Fraud Detection Using Autoencoder Neural Network
Imbalanced data classification problem has always been a popular topic in the field of machine learning research.
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure
a statistical population may play a crucial role in this regard, anomalies corresponding to observations with 'small' depth.
Graph Prototypical Networks for Few-shot Learning on Attributed Networks
kaize0409/GPN
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By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform \textit{meta-learning} on an attributed network and derive a highly generalizable model for handling the target classification task.
Promoting Fairness through Hyperparameter Optimization
Considerable research effort has been guided towards algorithmic fairness but real-world adoption of bias reduction techniques is still scarce.
How effective are Graph Neural Networks in Fraud Detection for Network Data?
ronaldpereira/brasnam-experiments
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Among these patterns, financial fraud stands out for its socioeconomic relevance and for presenting particular challenges, such as the extreme imbalance between the positive (fraud) and negative (legitimate transactions) classes, and the concept drift (i. e., statistical properties of the data change over time).
TOD: GPU-accelerated Outlier Detection via Tensor Operations
yzhao062/pytod
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Outlier detection (OD) is a key learning task for finding rare and deviant data samples, with many time-critical applications such as fraud detection and intrusion detection.
