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
Explainable Fraud Detection with Deep Symbolic Classification
There is a growing demand for explainable, transparent, and data-driven models within the domain of fraud detection.
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
While AD is typically treated as an unsupervised learning task due to the high cost of label annotation, it is more practical to assume access to a small set of labeled anomaly samples from domain experts, as is the case for semi-supervised anomaly detection.
Text generation for dataset augmentation in security classification tasks
wenliangdai/multi-task-offensive-language-detection
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Security classifiers, designed to detect malicious content in computer systems and communications, can underperform when provided with insufficient training data.
SplitGNN: Spectral Graph Neural Network for Fraud Detection against Heterophily
split-gnn/splitgnn
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CIKM 2023
However, researches on addressing the heterophily problem in the spectral domain are still limited due to a lack of understanding of spectral energy distribution in graphs with heterophily.
Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection
bdi-lab/drag
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We address this problem using Graph Neural Networks (GNNs) by proposing a dynamic relation-attentive aggregation mechanism.
FinDiff: Diffusion Models for Financial Tabular Data Generation
sattarov/FinDiff
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The sharing of microdata, such as fund holdings and derivative instruments, by regulatory institutions presents a unique challenge due to strict data confidentiality and privacy regulations.
High Performance Computing Applied to Logistic Regression: A CPU and GPU Implementation Comparison
We present a versatile GPU-based parallel version of Logistic Regression (LR), aiming to address the increasing demand for faster algorithms in binary classification due to large data sets.
AUC-Oriented Domain Adaptation: From Theory to Algorithm
statusrank/AUC-Oriented-Domain-Adaptation
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TPAMI 2023
We propose a new result that not only addresses the interdependency issue but also brings a much sharper bound with weaker assumptions about the loss function.
Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA Graph
kdegroup/cmt
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The rise of the click farm business using Multi-purpose Messaging Mobile Apps (MMMAs) tempts cybercriminals to perpetrate crowdsourcing frauds that cause financial losses to click farm workers.
FPR Estimation for Fraud Detection in the Presence of Class-Conditional Label Noise
We consider the problem of estimating the false-/ true-positive-rate (FPR/TPR) for a binary classification model when there are incorrect labels (label noise) in the validation set.
