Fraud Detection using Machine Learning and Deep Learning

Pradheepan Raghavan, Neamat El Gayar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)
PublisherIEEE
Pages334-339
Number of pages6
ISBN (Electronic)9781728137780
DOIs
Publication statusPublished - 20 Feb 2020
Event2019 International Conference on Computational Intelligence and Knowledge Economy - Dubai, United Arab Emirates
Duration: 11 Dec 201912 Dec 2019

Conference

Conference2019 International Conference on Computational Intelligence and Knowledge Economy
Abbreviated titleICCIKE 2019
CountryUnited Arab Emirates
CityDubai
Period11/12/1912/12/19

Keywords

  • autoencoder
  • convolutional neural networks
  • credit card
  • deep belief networks
  • deep learning
  • fraud detection
  • k nearest neighbor
  • machine learning
  • random forest
  • restricted boltzmann machine
  • support vector machine

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

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  • Cite this

    Raghavan, P., & Gayar, N. E. (2020). Fraud Detection using Machine Learning and Deep Learning. In 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) (pp. 334-339). IEEE. https://doi.org/10.1109/ICCIKE47802.2019.9004231