prikaz prve stranice dokumenta Otkrivanje prijevara u korištenju kreditnih kartica primjenom metoda strojnog učenja
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master's thesis
Otkrivanje prijevara u korištenju kreditnih kartica primjenom metoda strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:283661
Dragovčić, Antoni
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Dragovčić, A. (2019). Otkrivanje prijevara u korištenju kreditnih kartica primjenom metoda strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:283661

Dragovčić, Antoni. "Otkrivanje prijevara u korištenju kreditnih kartica primjenom metoda strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:283661

Dragovčić, Antoni. "Otkrivanje prijevara u korištenju kreditnih kartica primjenom metoda strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:283661

Dragovčić, A. (2019). 'Otkrivanje prijevara u korištenju kreditnih kartica primjenom metoda strojnog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:283661

Dragovčić A. Otkrivanje prijevara u korištenju kreditnih kartica primjenom metoda strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 November 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:283661

A. Dragovčić, "Otkrivanje prijevara u korištenju kreditnih kartica primjenom metoda strojnog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:283661

Metadata
TitleOtkrivanje prijevara u korištenju kreditnih kartica primjenom metoda strojnog učenja
Title (english)Credit Card Fraud Detection Using Machine Learning Techniques
AuthorAntoni Dragovčić
MentorGoran Delač (mentor)
Committee memberGoran Delač (predsjednik povjerenstva)
Committee memberStjepan Groš (član povjerenstva)
Committee memberSiniša Srbljić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-09, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Tema ovog rada je razvoj programskog rješenja za otkrivanje prijevara u kartičnom plaćanju, kao jednom od rastućih problema u financijskom sektoru. U tu se svrhu koriste popularni algoritmi strojnog učenja -- neuronska mreža tipa autoenkoder i logistička regresija. Implementirana su tri pristupa problemu. Prvi koristi autoenkoder, drugi logističku regresiju, dok treći pristup kombinira te dvije metode. Dodatno, navedeni algoritmi su potkrijepljeni teorijskom podlogom. Rješenja se vrednuju nad neuravnoteženim skupom podataka stvarnih transakcija prikupljenih u razdoblju dva dana 2013. godine na europskom platežnom području. Korištene metrike vrednovanja su preciznost, odziv i mjera f1. Konačno, dobiveni rezultati se komentiraju i uspoređuju.
Abstract (english)
The topic of this thesis is the development of a software solution for detecting frauds in card payments as one of the growing financial sector issues. For this purpose, popular machine learning algorithms -- autoencoder as a type of neural network and logistic regression are being used. Three approaches to the problem were implemented. The first one uses an autoencoder, the second one logistic regression and finally, the third approach combines the first two methods. In addition, these algorithms are supported by theoretical background. Solutions are valued over an unbalanced set of real transaction data collected in the two-day period of 2013 on the European Payments Area. The metrics used are precision, recall and f1-score. Finally, the measured results are compared and commented on.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:283661
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2020-01-07 17:42:51