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Please use this identifier to cite or link to this thesis: http://hdl.handle.net/10589/84804

Author: CARON, ROBERTO
CARMINATI, MICHELE
Supervisor: ZANERO, STEFANO
Scientific Disciplinary Sector: ING-INF/05 SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
Date: 3-Oct-2013
Academic year: 2012/2013
Title: Banksealer : a transaction monitoring system for Internet banking fraud detection
English abstract: The growth of Internet banking services led to a corresponding increase of online banking frauds. These are becoming more and more sophisticated, seriously threatening the security and trust of online banking business. In this thesis we propose BankSealer, an effective online banking semisupervised and unsupervised fraud and anomaly detection framework, with the goals of automatically detecting frauds and anomalies in a real online banking dataset. For the realisation of this project, we collaborated with an an IT security company and an important Italian banking group. BankSealer builds a profile for each customer on the basis of his or her past transactions, and can detect never seen before frauds in large banking datasets. It uses methods with a clear statistical meaning in order to provide the analyst a justifiable score and an easy to understand model of each customer’s spending habits. In particular, we developed three complementary types of analysis on user behaviour, based on tools to measures the anomaly of new transactions. The first is a local profile analysis, which computes the HBOS and measures the anomaly of a transactions with respect to the user’s historical behaviour profile. The second is a global profile analysis, which measures the anomaly of the user’s behaviour with respect to all other users. It uses an iterative version of DBSCAN. The last is a temporal profile analysis, which uses a threshold monitoring system to measure the anomaly of the current spending pattern of each user. In addition to these models, BankSealer addresses the rarely considered problem where the lack of past data prevents the building of a well trained profile, and is also able to follow the change of habits of the users through the updating of their models. Finally, we implemented a web application to show the potential of our method and the representation of its results. Our test on the given anonymised dataset with synthetically injected frauds, show that BankSealer is able to detect even very complex frauds with a generally high degree of accuracy and low computational complexity, despite the limitations of the context.
Italian keywords: Internet banking; rilevazione frodi; rilevazione anomalie; modello globale; modello locale; modello temporale
English keywords: Internet banking; fraud detection; anomaly detection; lcal model; global model; temporal model
Language: eng
Appears in Collections:POLITesi >Tesi Specialistiche/Magistrali

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