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
Most implemented papers
BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware
ThirdAIResearch/BOLT_Benchmarks
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Efficient large-scale neural network training and inference on commodity CPU hardware is of immense practical significance in democratizing deep learning (DL) capabilities.
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.
A Survey of Predictive Modelling under Imbalanced Distributions
Many real world data mining applications involve obtaining predictive models using data sets with strongly imbalanced distributions of the target variable.
Build a Deep Neural Network model using CPUs Builds a feed-forward multilayer artificial neural network on an H2OFrame
h2oai/h2o-3
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H2O.ai, Inc. 2015
With hundreds of meetups over the past three years, H2O has become a word-of-mouth phenomenon, growing amongst the data community by a hundred-fold, and is now used by 30, 000+ users and is deployed using R, Python, Hadoop, and Spark in 2000+ corporations.
Credit Card Fraud Detection Using Convolutional Neural Networks
finint/antifraud
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International Conference on Neural Information Processing 2016
Credit card is becoming more and more popular in financial transactions, at the same time frauds are also increasing.
Local Subspace-Based Outlier Detection using Global Neighbourhoods
In highly complex and high-dimensional data, however, existing methods are likely to overlook important outliers because they do not explicitly take into account that the data is often a mixture distribution of multiple components.
Scalable Exact Parent Sets Identification in Bayesian Networks Learning with Apache Spark
In Machine Learning, the parent set identification problem is to find a set of random variables that best explain selected variable given the data and some predefined scoring function.
One-Class Adversarial Nets for Fraud Detection
PanpanZheng/OCAN
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Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users.
BigDL: A Distributed Deep Learning Framework for Big Data
This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning applications on production big data platforms.
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection
Automatic detection of anomalies in space- and time-varying measurements is an important tool in several fields, e. g., fraud detection, climate analysis, or healthcare monitoring.
