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Comparative aAnalysis of Unsupervised Anomaly Detection Methods for Credit Card Fraud Detection
Jůzová, Anna ; Červinka, Michal (advisor) ; Janásek, Lukáš (referee)
In recent years, the increasing rate of cashless payments and online pur- chases has led to a rise in credit card fraud. Detecting fraudulent transactions poses a signifcant challenge for fnancial institutions, however, machine learn- ing has emerged as a promising tool. This thesis focuses on machine learning models for anomaly detection that have not received sufcient attention in pre- vious research. Specifcally, the study examines Isolation Forest, Local Outlier Factors, and One-Class Support Vector Machine. These models identify fraud- ulent payments as transactions that do not ft the learned pattern from past transactions. To optimise performance, the data are normalised using diferent normalisation techniques. Among the tested models, the Local Outlier Factor model with data normalised using the min-max method seems to be the most efective. JEL Classifcation C49, G21, K42 Keywords credit card fraud, machine learning, anomaly de- tection, data normalisation Title Comparative Analysis of Unsupervised Anomaly Detection Methods for Credit Card Fraud De- tection Author's e-mail anna.juzova11@gmail.com Supervisor's e-mail michal.cervinka@fsv.cuni.cz
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Database conceived information assurance of E-shop operation
Debreczényiová Krammerová, Monika ; Vostrovský, Václav (advisor)
The main goal of this Bachelor thesis is to create a database design for an application that deals with the operation of a shop.
In addition to the clarification of the theoretical principles of relational database technology is an important part of the definition of the requirements for the data model, influenced by current trends in the field of the data record shop.
The core parts of the proposal itself, however, is a conceptual data model corresponding to the requirements set out in the previous section.
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Composite indicators: the construction, usage and interpretation
Hudrlíková, Lenka ; Fischer, Jakub (advisor) ; Čadil, Jan (referee) ; Hužvár, Miroslav (referee)
This thesis brings a comprehensive view on the construction, usage and interpretation of composite indicators. Methods and techniques, which can be used for constructing composite indicators, are introduced. The focus is on their contribution to the transparent solution of the problem of correlation and compensability among underlying indicators. Transparency in construction of composite indicators is a crucial requirement for obtaining reliable results and their correct interpretation. The thesis consists of two main parts. The first part is theoretically oriented. First, the problem of adequacy and subsequently a measurement of the phenomenon by means of statistical indicators are discussed. Different methods for data normalization, setting a weighting scheme and aggregation are introduced and compared. These three steps are considered to be crucial in a process of constructing a composite indicator and thus, they are the core of the thesis. The aim is to investigate an interaction of normalization methods, weight-setting and aggregation methods, since these steps are not separate. The second part of the thesis consists of two comprehensive cases. Theoretical findings are applied and empirically verified in these cases. I investigated a robustness of the composite indicator depending on a combination of selected methods of normalization, setting weights and aggregation on a set of Europe 2020 indicators. Whereas this first case dealt with the comparative analysis of methods, the second case is focused purely on one issue -- university ranking. The proposed method reacts to criticism of currently published university rankings and takes into account specifics of the particular university as well as the exogenous background characteristics. The main added value rests in a contribution to a discussion about the improvement of construction and overall quality of composite indicators including their interpretation. I pointed out the main concerns and difficulties of composite indicators that often remain unnoticed by users and even constructors. The conclusion brings several beneficial findings, which can be used for the construction of a composite indicator and an interpretation of final scores and ranking. This work can also serve as a scientific ground for further research and development of the methodology of constructing composite indicators.
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