Illustrating the Explicative Capabilities of Bayesian Learning Neural Networks for Auto Claim Fraud Detection

2003 ◽  
pp. 365-399
Author(s):  
S. Viaene ◽  
R. A. Derrig ◽  
G. Dedene
Keyword(s):  
Neural Networks ◽  
Bayesian Learning ◽  
Fraud Detection

Auto claim fraud detection using Bayesian learning neural networks

2005 ◽  
Vol 29 (3) ◽  
pp. 653-666 ◽  
Author(s):  
S VIAENE ◽  
G DEDENE ◽  
R DERRIG
Keyword(s):  
Neural Networks ◽  
Bayesian Learning ◽  
Fraud Detection

scholarly journals Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers

2019 ◽  
Vol 11 (4) ◽  
pp. 86 ◽  
Author(s):  
César Pérez López ◽  
María Delgado Rodríguez ◽  
Sonia de Lucas Santos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Income Tax ◽  
Fraud Detection ◽  
Personal Income ◽  
Personal Income Tax ◽  
Tax Returns ◽  
The Neural Networks ◽  
Tax Fraud ◽  
Efficiency Rate

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud.

Neural networks: the panacea in fraud detection?

2010 ◽  
Vol 25 (7) ◽  
pp. 659-678 ◽  
Author(s):  
Maria Krambia‐Kapardis ◽  
Chris Christodoulou ◽  
Michalis Agathocleous
Keyword(s):  
Neural Networks ◽  
Fraud Detection

scholarly journals Decision Making Support System for Managing Advertisers By Ad Fraud Detection

2021 ◽  
Vol 11 (4) ◽  
pp. 331-339
Author(s):  
Marcin Gabryel ◽  
Magdalena M. Scherer ◽  
Łukasz Sułkowski ◽  
Robertas Damaševičius
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Financial Institutions ◽  
Support System ◽  
Categorical Data ◽  
Fraud Detection ◽  
Feedforward Neural Networks ◽  
High Accuracy ◽  
Online Channel ◽  
Lead Management

Abstract Efficient lead management allows substantially enhancing online channel marketing programs. In the paper, we classify website traffic into human- and bot-origin ones. We use feedforward neural networks with embedding layers. Moreover, we use one-hot encoding for categorical data. The data of mouse clicks come from seven large retail stores and the data of lead classification from three financial institutions. The data are collected by a JavaScript code embedded into HTML pages. The three proposed models achieved relatively high accuracy in detecting artificially generated traffic.

Sign in / Sign up

Export Citation Format

Share Document