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CREDIT CARD FRAUD DETECTION (Machine learning algorithms)
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
2017 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesisAlternative title
Kreditkortsbedrägeri med användning av maskininlärningsalgoritmer (Swedish)
Abstract [en]

Credit card fraud is a field with perpetrators performing illegal actions that may affect other individuals or companies negatively. For instance, a criminalcan steal credit card information from an account holder and then conduct fraudulent transactions. The activities are a potential contributory factor to how illegal organizations such as terrorists and drug traffickers support themselves financially. Within the machine learning area, there are several methods that possess the ability to detect credit card fraud transactions; supervised learning and unsupervised learning algorithms. This essay investigates the supervised approach, where two algorithms (Hellinger Distance Decision Tree (HDDT) and Random Forest) are evaluated on a real life dataset of 284,807 transactions. Under those circumstances, the main purpose is to develop a “well-functioning” model with a reasonable capacity to categorize transactions as fraudulent or legit. As the data is heavily unbalanced, reducing the false-positive rate is also an important part when conducting research in the chosen area. In conclusion, evaluated algorithms present a fairly similar outcome, where both models have the capability to distinguish the classes from each other. However, the Random Forest approach has a better performance than HDDT in all measures of interest.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Credit card fraud detection, Machine learning, Supervised learning algorithms, Classification, Unbalanced data
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-136031OAI: oai:DiVA.org:umu-136031DiVA: diva2:1108821
Available from: 2017-06-13 Created: 2017-06-13 Last updated: 2017-06-13Bibliographically approved

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File name FULLTEXT01.pdfFile size 637 kBChecksum SHA-512
896ce83ea35defe1d9d806f9998879047ec73e81733b2638b27a8018c9aa95d8820f11fd19985ac86fe1f05cb9cbe6214023019fea87cf188efa0291c956d196
Type fulltextMimetype application/pdf

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf