Fraud Detection

65 papers with code • 3 benchmarks • 4 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

Most implemented papers

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.

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

somepago/saint 2 Jun 2021

We devise a hybrid deep learning approach to solving tabular data problems.

Deep Anomaly Detection with Deviation Networks

GuansongPang/deviation-network 19 Nov 2019

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.

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.

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.

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

Gstaerman/ACHD 9 Oct 2019

a statistical population may play a crucial role in this regard, anomalies corresponding to observations with 'small' depth.

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

ronaldpereira/brasnam-experiments 30 May 2021

Among these patterns, financial fraud stands out for its socioeconomic relevance and for presenting particular challenges, such as the extreme imbalance between the positive (fraud) and negative (legitimate transactions) classes, and the concept drift (i. e., statistical properties of the data change over time).

A Survey of Predictive Modelling under Imbalanced Distributions

smrjan/predictive-maintainance 7 May 2015

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

Build a Deep Neural Network model using CPUs Builds a feed-forward multilayer artificial neural network on an H2OFrame

h2oai/h2o-3, Inc. 2015

With hundreds of meetups over the past three years, H2O has become a word-of-mouth phenomenon, growing amongst the data community by a hundred-fold, and is now used by 30, 000+ users and is deployed using R, Python, Hadoop, and Spark in 2000+ corporations.

Local Subspace-Based Outlier Detection using Global Neighbourhoods

Basvanstein/Gloss 1 Nov 2016

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.