Fraud Detection

42 papers with code • 1 benchmarks • 1 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


Greatest papers with code

BigDL: A Distributed Deep Learning Framework for Big Data

intel-analytics/BigDL 16 Apr 2018

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.

Fraud Detection Object Detection

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

safe-graph/DGFraud 19 Aug 2020

Finally, the selected neighbors across different relations are aggregated together.

Fraud Detection Node Classification

Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection

safe-graph/DGFraud 1 May 2020

In this paper, we introduce these inconsistencies and design a new GNN framework, $\mathsf{GraphConsis}$, to tackle the inconsistency problem: (1) for the context inconsistency, we propose to combine the context embeddings with node features, (2) for the feature inconsistency, we design a consistency score to filter the inconsistent neighbors and generate corresponding sampling probability, and (3) for the relation inconsistency, we learn a relation attention weights associated with the sampled nodes.

Fraud Detection

A Semi-supervised Graph Attentive Network for Financial Fraud Detection

safe-graph/DGFraud 28 Feb 2020

Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.

Fraud Detection Platform

SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection

yzhao062/SUOD 11 Mar 2020

Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.

Dimensionality Reduction Fraud Detection +2

Continuous-variable quantum neural networks

XanaduAI/quantum-neural-networks 18 Jun 2018

The quantum neural network is a variational quantum circuit built in the continuous-variable (CV) architecture, which encodes quantum information in continuous degrees of freedom such as the amplitudes of the electromagnetic field.

Fraud Detection Quantum Machine Learning

XBNet : An Extremely Boosted Neural Network

tusharsarkar3/XBNet 9 Jun 2021

Neural networks have proved to be very robust at processing unstructured data like images, text, videos, and audio.

Anomaly Detection Breast Cancer Detection +5

Tabular Transformers for Modeling Multivariate Time Series

IBM/TabFormer 3 Nov 2020

This results in two architectures for tabular time series: one for learning representations that is analogous to BERT and can be pre-trained end-to-end and used in downstream tasks, and one that is akin to GPT and can be used for generation of realistic synthetic tabular sequences.

Fraud Detection Hierarchical structure +2

Robust Spammer Detection by Nash Reinforcement Learning

YingtongDou/Nash-Detect 10 Jun 2020

We experiment on three large review datasets using various state-of-the-art spamming and detection strategies and show that the optimization algorithm can reliably find an equilibrial detector that can robustly and effectively prevent spammers with any mixed spamming strategies from attaining their practical goal.

Fraud Detection