Fraud Detection
116 papers with code • 4 benchmarks • 9 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
IEEE-CIS Fraud Detection
Latest papers
Graph Inference Acceleration by Learning MLPs on Graphs without Supervision
Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph learning tasks, yet their reliance on message-passing constraints their deployment in latency-sensitive applications such as financial fraud detection.
On the Detection of Reviewer-Author Collusion Rings From Paper Bidding
A major threat to the peer-review systems of computer science conferences is the existence of "collusion rings" between reviewers.
TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning
facebookresearch/taser-tgnn
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Recently, Temporal Graph Neural Networks (TGNNs) have demonstrated state-of-the-art performance in various high-impact applications, including fraud detection and content recommendation.
Comparative Study on the Performance of Categorical Variable Encoders in Classification and Regression Tasks
This study broadly classifies machine learning models into three categories: 1) ATI models that implicitly perform affine transformations on inputs, such as multi-layer perceptron neural network; 2) Tree-based models that are based on decision trees, such as random forest; and 3) the rest, such as kNN.
Distributed Monitoring for Data Distribution Shifts in Edge-ML Fraud Detection
The digital era has seen a marked increase in financial fraud.
SeqNAS: Neural Architecture Search for Event Sequence Classification
As a result of our work we demonstrate that our method surpasses state of the art NAS methods and popular architectures suitable for sequence classification and holds great potential for various industrial applications.
Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences
featurespace/foundation-model-paper
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Machine learning models underpin many modern financial systems for use cases such as fraud detection and churn prediction.
TSPP: A Unified Benchmarking Tool for Time-series Forecasting
NVIDIA/DeepLearningExamples
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While machine learning has witnessed significant advancements, the emphasis has largely been on data acquisition and model creation.
ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection
jweihe/ADA-GAD
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We pretrain graph autoencoders on these augmented graphs at multiple levels, which enables the graph autoencoders to capture normal patterns.
FiFAR: A Fraud Detection Dataset for Learning to Defer
Financial fraud detection is a high-stakes setting where algorithms and human experts often work in tandem; however, there are no publicly available datasets for L2D concerning this important application of human-AI teaming.
