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 with no code
Securing Transactions: A Hybrid Dependable Ensemble Machine Learning Model using IHT-LR and Grid Search
Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods.
Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industry
In such a way, FL is implemented as a privacy-enhancing collaborative learning technique that addresses the challenges posed by the sensitivity and privacy of data in traditional machine learning solutions.
Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data
Anomaly detection is a critical challenge across various research domains, aiming to identify instances that deviate from normal data distributions.
On the Potential of Network-Based Features for Fraud Detection
Online transaction fraud presents substantial challenges to businesses and consumers, risking significant financial losses.
Confronting Discrimination in Classification: Smote Based on Marginalized Minorities in the Kernel Space for Imbalanced Data
Financial fraud detection poses a typical challenge characterized by class imbalance, where instances of fraud are extremely rare but can lead to unpredictable economic losses if misidentified.
Coherent Feed Forward Quantum Neural Network
Moreover, the circuit depth and qubit needs of these models scale poorly with the number of data features, resulting in an efficiency challenge for real-world machine-learning tasks.
Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection
Federated Learning (FL) is a data-minimization approach enabling collaborative model training across diverse clients with local data, avoiding direct data exchange.
Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models
Devising procedures for downstream task-oriented generative model selections is an unresolved problem of practical importance.
Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective
Exploring generative model training for synthetic tabular data, specifically in sequential contexts such as credit card transaction data, presents significant challenges.
Temporal Knowledge Distillation for Time-Sensitive Financial Services Applications
Detecting anomalies has become an increasingly critical function in the financial service industry.
