Browse SoTA > Miscellaneous > Fraud Detection

# Fraud Detection Edit

14 papers with code · Miscellaneous

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

# 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.

3,562

# Continuous-variable 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.

174

# 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.

114

# One-Class Adversarial Nets for Fraud Detection

5 Mar 2018PanpanZheng/OCAN

Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users.

27

# 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.

14

# Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness

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.

13

# Spotting Collective Behaviour of Online Frauds in Customer Reviews

31 May 2019LCS2-IIITD/DeFrauder

Online reviews play a crucial role in deciding the quality before purchasing any product.

6

# Diversity-Aware Weighted Majority Vote Classifier for Imbalanced Data

16 Apr 2020goyalanil/DAMVI

In this paper, we propose a diversity-aware ensemble learning based algorithm, referred to as DAMVI, to deal with imbalanced binary classification tasks.

2

# Local Subspace-Based Outlier Detection using Global Neighbourhoods

1 Nov 2016Basvanstein/Gloss

In highly complex and high-dimensional data, however, existing methods are likely to overlook important outliers because they do not explicitly take into account that the data is often a mixture distribution of multiple components.

2

# A Survey of Predictive Modelling under Imbalanced Distributions

7 May 2015smrjan/predictive-maintainance

Many real world data mining applications involve obtaining predictive models using data sets with strongly imbalanced distributions of the target variable.

2