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Machine Learning Methods in Payment Card Fraud Detection
Sinčák, Jan ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
Protection of clients from fraudulent transactions is a complicated task. Banks tend to rely on rule-based systems which require manual creation of rules to identify fraud. These rules have to be set up by employees of the bank who need to look for any trends in fraudulent transactions themselves. This thesis deals with the problem of detection of fraudulent card transactions as it com- pares multiple machine learning models for fraud detection. These models can find complex relationships in the data and potentially outperform standard fraud detection systems, Logistic regression, neural network, random forest, and extreme gradient boosting (XGBoost) models are trained on a simulated dataset that closely follows properties of real card transactions. Performance of the models is measured by sensitivity, specificity, precision, AUC, and time to predict on the testing dataset. XGBoost shows the highest performance among the tested models. It is then compared to a standard fraud detection system used in a Czech bank. The bank system achieves higher specificity but XGBoost still shows promising performance. It is possible that certain machine learning models could outperform today's fraud detection systems if they are well-tuned. JEL Classification G21, K42 Keywords machine learning, card fraud, fraud...
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Detection of Accounting Errors and Frauds in Trading Activity
ŠANDOVÁ, Barbora
Fraud is one of the major ethical issues in the bookeeping. The aim of this diploma thesis is to define the question of errors or frauds in the accounting records focused on the trading activity. Appropriate fraud detection techniques are to be successively selected to illustrate common frauds in contemporary times. The work describes four methods of testing of the correctness of reporting. One of the models is applied to the case study, specifically, it is the Q-DMFCA (Quick Detection model Material Flow Cost accounting) balance model based on environmental accounting. At the conclusion of this thesis are suggested strengths and weaknesses of the model.
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The creative accounting - accounting fraud in terms of the Czech republic
KUBATOVÁ, Zuzana
This thesis deals with a very important and serious topic, which is creative accounting and its relation to accounting frauds. Most of the society finds creative accounting undesirable because it was mentioned in relation to bankruptcy of big American a European companies after the revelation of their financial manipulations. The thesis analyses accounting frauds focusing on fraud detection, prevention, typical fraudster and criteria of the fraudelent actions according to the fraud triangle.
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Towards detecting and measuring ballot stuffing
Vorobyev, Dmitriy
This paper proposes a method for detecting electoral fraud in the form of ballot stuffing. As ballot stuffing increases both turnout and the incumbent‘s vote share in precincts where it occurs, precincts with low reported turnout are more likely to be clean. Information on clean precincts is used to simulate counterfactual data for ― ¨infected¨ precincts, which are then compared to the observed data. The method is applied to the 2006 Finnish presidential elections. The test fails to reject the hypothesis of no ballot stuffing for the original data, but detects artificially imputed 1.6% fraud. The same test implies that in the 2004 presidential elections in Russia at least 4.7% of the votes were stuffed in favor of the incumbent.
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