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
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.
VOS: a Method for Variational Oversampling of Imbalanced Data
Class imbalanced datasets are common in real-world applications that range from credit card fraud detection to rare disease diagnostics.
Deep-Net: Deep Neural Network for Cyber Security Use Cases
In this paper, we attempt to apply DNNs on three different cyber security use cases: Android malware classification, incident detection and fraud detection.
Multiple perspectives HMM-based feature engineering for credit card fraud detection
In this article, we model a sequence of credit card transactions from three different perspectives, namely (i) does the sequence contain a Fraud?
Spotting Collective Behaviour of Online Frauds in Customer Reviews
Online reviews play a crucial role in deciding the quality before purchasing any product.
A Variational Approach for Learning from Positive and Unlabeled Data
HC-Feynman/vpu
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NeurIPS 2020
Learning binary classifiers only from positive and unlabeled (PU) data is an important and challenging task in many real-world applications, including web text classification, disease gene identification and fraud detection, where negative samples are difficult to verify experimentally.
Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness
wwoods/adversarial-explanations-cifar
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For sensitive problems, such as medical imaging or fraud detection, Neural Network (NN) adoption has been slow due to concerns about their reliability, leading to a number of algorithms for explaining their decisions.
Are You for Real? Detecting Identity Fraud via Dialogue Interactions
Leechikara/Dialogue-Based-Anti-Fraud
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IJCNLP 2019
In this paper, we focus on identity fraud detection in loan applications and propose to solve this problem with a novel interactive dialogue system which consists of two modules.
Credit Card Fraud Detection Using Autoencoder Neural Network
AniketP04/Credit-Card-Fraud-Detection
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Imbalanced data classification problem has always been a popular topic in the field of machine learning research.
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs
Our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to improve the effectiveness of the classification task and allows for an increase in the detection of fraudulent transactions when combined with the state of the art expert based feature engineering strategy for credit card fraud detection.
