Fraud Detection using Machine Learning and Deep Learning

Frauds are known to be dynamic and have no patterns, hence they are not easy to identify. Fraudsters use recent technological advancements to their advantage. They somehow bypass security checks, leading to the loss of millions of dollars. Analyzing and detecting unusual activities using data mining...

Full description

Saved in:
Bibliographic Details
Published in2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) pp. 334 - 339
Main Authors Raghavan, Pradheepan, Gayar, Neamat El
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
Artificial neural networks
autoencoder
convolutional neural networks
credit card
Credit cards
Data models
deep belief networks
deep learning
Europe
Forestry
fraud detection
k nearest neighbor
Machine learning
random forest
restricted boltzmann machine
support vector machine
Support vector machines
Online AccessGet full text

Cover

More Information
Summary:Frauds are known to be dynamic and have no patterns, hence they are not easy to identify. Fraudsters use recent technological advancements to their advantage. They somehow bypass security checks, leading to the loss of millions of dollars. Analyzing and detecting unusual activities using data mining techniques is one way of tracing fraudulent transactions. transactions. This paper aims to benchmark multiple machine learning methods such as k-nearest neighbor (KNN), random forest and support vector machines (SVM), while the deep learning methods such as autoencoders, convolutional neural networks (CNN), restricted boltzmann machine (RBM) and deep belief networks (DBN). The datasets which will be used are the European (EU) Australian and German dataset. The Area Under the ROC Curve (AUC), Matthews Correlation Coefficient (MCC) and Cost of failure are the 3-evaluation metrics that would be used.
DOI:10.1109/ICCIKE47802.2019.9004231