2019
DOI: 10.1051/shsconf/20196502002
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Fraud detection models and payment transactions analysis using machine learning

Abstract: The work’s aim is to research a set of selected mathematical models and algorithms that examine the data of a single payment transaction to classify it as fraud or verified. Described models are implemented in the form of a computer code and algorithms, and therefore can be executed in real-time. The main objective is to apply different methods of machine learning to find the most accurate, in other words, the one in which the cross-validation score is maximal. Thus, the main problem to resolve is the creation… Show more

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Cited by 3 publications
(2 citation statements)
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“…Thus, the listing begins with )* devices at beginning of each cycle, then steadily depletes to zero for + = ,1 because of the mixture impact of deterioration and need. A graphical representation [8], [14] of the deemed catalogue device is provided may be drawn to time interval [zero, ,-], the listing does not have any deterioration. Deterioration happens to time interval [,-, ,1] in a continuous deterioration pace ..…”
Section: Model With Zero Im and Problem Descriptionmentioning
confidence: 99%
See 1 more Smart Citation
“…Thus, the listing begins with )* devices at beginning of each cycle, then steadily depletes to zero for + = ,1 because of the mixture impact of deterioration and need. A graphical representation [8], [14] of the deemed catalogue device is provided may be drawn to time interval [zero, ,-], the listing does not have any deterioration. Deterioration happens to time interval [,-, ,1] in a continuous deterioration pace ..…”
Section: Model With Zero Im and Problem Descriptionmentioning
confidence: 99%
“…Next, hospitals will be grouped hierarchically [22]. First, second, and third clusters contain medical facilities in order (5,6,7,9,10,11). Hospitals were grouped into the four groups shown.…”
Section: Table V the Use Of A Trend-based Clustering System To A Comp...mentioning
confidence: 99%