Fraud Detection

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

Libraries

Datasets

Subtasks

Most implemented papers

Continuous-variable quantum neural networks

Content

Deep Anomaly Detection with Deviation Networks

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure

Promoting Fairness through Hyperparameter Optimization

How effective are Graph Neural Networks in Fraud Detection for Network Data?

TOD: GPU-accelerated Outlier Detection via Tensor Operations

Fraud Dataset Benchmark and Applications

Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation