About

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

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Greatest papers with code

BigDL: A Distributed Deep Learning Framework for Big Data

16 Apr 2018intel-analytics/BigDL

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

19 Aug 2020safe-graph/DGFraud

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

1 May 2020safe-graph/DGFraud

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

28 Feb 2020safe-graph/DGFraud

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

SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection

11 Mar 2020yzhao062/SUOD

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 INTRUSION DETECTION OUTLIER ENSEMBLES

Continuous-variable quantum neural networks

18 Jun 2018XanaduAI/quantum-neural-networks

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

Robust Spammer Detection by Nash Reinforcement Learning

10 Jun 2020YingtongDou/Nash-Detect

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

Tabular Transformers for Modeling Multivariate Time Series

3 Nov 2020IBM/TabFormer

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 SYNTHETIC DATA GENERATION TIME SERIES

Deep Anomaly Detection with Deviation Networks

19 Nov 2019GuansongPang/deviation-network

Instead of representation learning, our method fulfills an end-to-end learning of anomaly scores by a neural deviation learning, in which we leverage a few (e. g., multiple to dozens) labeled anomalies and a prior probability to enforce statistically significant deviations of the anomaly scores of anomalies from that of normal data objects in the upper tail.

ANOMALY DETECTION CYBER ATTACK DETECTION FRAUD DETECTION NETWORK INTRUSION DETECTION REPRESENTATION LEARNING

Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection

19 Apr 2018cvjena/libmaxdiv

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.

ANOMALY DETECTION FRAUD DETECTION TIME SERIES