magistrsko delo
Adriana Kamnik (Author), Marjetka Krajnc (Mentor)

Abstract

Rast nove tehnologije nam olajšuje vsakodnevna opravila, plačila, komuniciranje, dostop do interneta in informacij ter druge storitve. Pojavijo pa se lahko različne prevare, ki povzročijo škodo organizacijam in njihovim strankam. V magistrski nalogi se osredotočimo na odkrivanje goljufij v telekomunikacijski industriji. Telekomunikacijska podjetja se soočajo s poznanimi in novimi oblikami prevar. Te včasih niso odkrite, velikokrat pa so odkrite prepozno. Kaznovanje storilcev je zahtevno, ker so prevare pogosto vpete v mednarodno okolje. Odkrivanje prevar z matematičnega vidika predstavlja problem odkrivanja anomalij v veliki količini podatkov. Anomalije ali osamelci so redki primerki v podatkih, ki se drugače obnašajo kot ostali primerki. Telekomunikacijska podjetja hranijo vse aktivnosti njihovih strank v obliki CDR datotek, zato je količina podatkov res ogromna. V testnih podatkih običajno nimamo označene goljufive in normalne aktivnosti, zato so v magistrski nalogi predstavljene nenadzorovane metode ter ostale napredne metode za odkrivanje anomalij, kjer ne potrebujemo vrednosti ciljne spremenljivke. Cilj magistrske naloge je poiskati in pojasniti delovanje različnih naprednih metod podatkovnega rudarjenja z namenom odkrivanja anomalij v podatkih. Med drugim je cilj zgraditi model, ki zazna goljufive aktivnosti

Keywords

zaznavanje goljufij;odkrivanje anomalij v velikem podatkovju;veliko podatkovje;rudarjenje podatkov;mera različnosti;k-najbližji sosedje;gručenje;modeliranje;

Data

Language: Slovenian
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UL FMF - Faculty of Mathematics and Physics
Publisher: [A. Kamnik]
UDC: 519.8
COBISS: 18466649 Link will open in a new window
Views: 895
Downloads: 349
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Other data

Secondary language: English
Secondary title: Fraud detection in telecommunication using data mining methods
Secondary abstract: Everyday life assignments, payments, communication, access to the internet and other services are getting simplified by technology development but there are also negative effects like different fraud actions that can cause damage to a lot of organizations and their clients. In this work we are focused on the fraud detection in telecommunication industry. Telecommunication companies are confronting with well known frauds as well as with unknown frauds. Fraud actions are not always detected or they may be detected too late. Usually more different parties from all around the world are included in telecommunication sevices and thus punishment of the criminals is very difficult. From a mathematical point of view fraud detection is considered as the identification of unusual pattern or anomaly detection in a big data. Anomalies or outliers are rare cases in the data, which are significantly different from the majority of the data. Since all the activities of clients are stored in CDR files, amount of data is very large. Test data are not labeled as fraudulent or normal activities, therefore unsupervised methods and other advanced techniques for anomaly detection, which do not require target variable, are considered in this work. The aim of the thesis is to examine different advanced methods of data mining in order to detect anomalies, and to develop a model that would be capable to distinguish normal from fraudulent activities.
Secondary keywords: fraud detection;anomaly detection in large datasets;large datasets;data mining;measure of dissimilarity;k-nearest neighbors;clustering;modeling;
Type (COBISS): Master's thesis/paper
Study programme: 0
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za matematiko in fiziko, Oddelek za matematiko, Finančna matematika - 2. stopnja
Pages: XV, 81 str.
ID: 10977646