An Investigation on Experimental Issues in Financial Fraud Mining

Research output: Book chapter/Published conference paperConference paperpeer-review

8 Citations (Scopus)

Abstract

Financial fraud has shown itself to be a fundamental issue throughout history, and due to its substantial impact on society consideration into the best method of solving it is highly important. There are a several key experimental issues that are relevant to computational intelligence-based financial fraud detection, and in this paper we will investigate three of them: choice of detection algorithm, performance metrics, and feature selection. The characteristics of these three issues has been described as they are understood in existing literature, but we have identified that there are large gaps in the research that need further attention. We will reduce this deficit by conducting an in-depth investigation of detection algorithms, performance metrics, and feature selection using a series of controlled simulations for a credit card fraud problem and analysing the results.
Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA)
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1796-1801
Number of pages6
Publication statusPublished - 2016
Event2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA) - Hefei, China, Hefei, China
Duration: 05 Jun 201607 Jun 2016

Conference

Conference2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA)
CountryChina
CityHefei
Period05/06/1607/06/16

Grant Number

  • Financial fraud detection, Computational Intelligence, Information Security, Artificial Intelligence

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