This paper presents the methods for the evaluation of budget variance risk, i.e. the risk of a difference between the budgeted and actual figures. The postulated approach is based on extreme value analysis (EVA), to offer, among other things, the evaluation of maxima distribution parameters for studied phenomena. The proper recognition of these parameters yields potential for calculation of probabilities for budget variance to pass certain levels established as critical. This methodology can be used to evaluate deviation levels by time period, and to compare them against historical data. The main objective of this paper was to examine the utility of the theory of extreme values in the estimation of budget deviation risks. The study presents the results of probabilistic analyses of data obtained from a budgetary cost control unit of a production company located in eastern Poland, for the period of 2011-2012. The developed method of analysis and assessment of budget deviations is in line with the development of concepts and methods of management accounting.

Użyteczność informacji finansowych ze sprawozdań organizacji pożytku publicznego w ocenie ich dokonań przez darczyńców Joanna Dynowska: Programy finansowo-księgowe wykorzystywane w gminach Rafał Jagoda: Zarządzanie należnościami w kształtowaniu płynności finansowej przedsiębiorstw .

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The effectiveness of the university's functioning and its organizational culture can be improved thanks to the use of machine learning. At Universities, the context of student anticipation is very important from the point of view of the fundamental planning and control functions associated with this specific form of management. The purpose of this study is to present the results of an experiment involving the prediction of student structure (attributes of students and their activities) based on the use of a machine learning solution and comparing them against real data obtained from a registry system of a European public institution of higher education in economic sciences. At universities, there is a clear need to support various components of system management. The experiments revealed that -for 11 out of the 48 examined datasets -the Percentage Similarity Index was in excess of 75% but was decidedly lower for the remaining sets (with 18 sets assessed below the margin of 50%).