Fraud Detection
117 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 with no code
Generative Pretraining at Scale: Transformer-Based Encoding of Transactional Behavior for Fraud Detection
In this work, we introduce an innovative autoregressive model leveraging Generative Pretrained Transformer (GPT) architectures, tailored for fraud detection in payment systems.
Open-Set: ID Card Presentation Attack Detection using Neural Transfer Style
The accurate detection of ID card Presentation Attacks (PA) is becoming increasingly important due to the rising number of online/remote services that require the presentation of digital photographs of ID cards for digital onboarding or authentication.
Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments
This observation challenges the potential benefits of ensemble methods to combine supervised, and AD approaches to enhance performance.
Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection
This data imbalance can affect the performance or reliability of the fraud detection model.
Leveraging the Urysohn Lemma of Topology for an Enhanced Binary Classifier
In this article we offer a comprehensive analysis of the Urysohn's classifier in a binary classification context.
Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum
This detector includes a hybrid filtering module and a local environmental constraint module, the two modules are utilized to solve heterophily and label utilization problem respectively.
Similar Document Template Matching Algorithm
Fraud detection involves the SSIM computation and OCR for textual information extraction.
A Compact LSTM-SVM Fusion Model for Long-Duration Cardiovascular Diseases Detection
Globally, cardiovascular diseases (CVDs) are the leading cause of mortality, accounting for an estimated 17. 9 million deaths annually.
Bridging the Gap: Towards an Expanded Toolkit for ML-Supported Decision-Making in the Public Sector
Machine Learning (ML) systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health.
DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection
However, due to the rapid increase in data scale and dimensionality, the issues of noise and Weak Identity Mapping (WIM) during time series reconstruction have become increasingly pronounced.
