Abstract. Financial auditing cannot be imagined without the involvement of IT specialists since business processes are designed to be served by IT components such as ERP systems, online customer-facing applications, databases etc. Financial auditors therefore exposed to IT system and control reliance want to gain reasonable assurance that data and transactions stored in IT systems cannot be modified, access is controlled, and there is no suspicion of any fraud at business organizations. The paper tries to understand the current situation of IT audit involvement in financial auditing, interpret risks that parties face and provide some solutions by use of intelligent applications.
Abstract. Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a wellknown and established method, ARMA with exogenous variables with a relatively new technique Gradient Boosting Regression. The method was tested on data from Global Energy Forecasting Competition 2014 with a year long rolling window forecast. The results from the experiment reveal that a multi-model approach is significantly better performing in terms of error metrics. Gradient Boosting can deal with seasonality and autocorrelation out-of-the box and achieve lower rate of normalized mean absolute error on real-world data.
In this paper we propose a framework for automated forecasting of energy-related time series using open access data from European Network of Transmission System Operators for Electricity (ENTSO-E). The framework provides forecasts for various European countries using publicly available historical data only. Our solution was benchmarked using the actual load data and the country provided estimates (where available). We conclude that the proposed system can produce timely forecasts with comparable prediction accuracy in a number of cases. We also investigate the probabilistic case of forecasting -that is, providing a probability distribution rather than a simple point forecast -and incorporate it into a web based API that provides quick and easy access to reliable forecasts.
A vállalatok működése szempontjából a döntéstámogató funkció folyamatos fejlesztése, monitorozása kiemelt jelentőségű, hiszen az vezetést támogató eszközként segíti a menedzsmentfeladatok ellátását. Az üzleti intelligencia (business intelligence, BI) olyan infokommunikációs megoldás, mely a vállalati rendszerekből különböző adatforrásokat felhasználva képes az adatok összekapcsolására és elemzésére. A napi üzletmenet gördülékeny biztosítása céljából alkalmazott tranzakciós rendszerektől eltérően a BI-eszközök beszámolás orientáltak, a fókusz a döntéstámogatásra helyeződik. A kutatás a fogalmak tisztázását követően képet ad a legfrissebb üzleti intelligencia trendekről. A tanulmány szakmai mélyinterjúk elemzésén keresztül betekintést nyújt az üzleti intelligencia megoldások világába. A kutatás eredményeként az olvasó képet kaphat a BI bevezetésétől várt eredményekről, az implementáció és a hosszú távú működtetés sikerkritériumait illetően.
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Gergely GORCSI - Gergo BARTA - Zsuzsanna SZELES
Success criteria for the application of business
intelligence solutions
In the running of any given company, continuous improvement and monitoring of decision support functions is crucial for such activities to serve as tools to support management tasks. Business Intelligence (BI) is an infocommunication tool that connects and analyses data from corporate systems using varied data sources. Unlike transactional systems that are used to ensure the sound operation of day-to-day business, BI tools are report-oriented, and focus on decision support. Reviewing related concepts, this research gives an overview of the latest business intelligence trends.
Our study sets out to provide an insight into the world of business intelligence solutions by analysing professional, in-depth interviews. Through our research, one will become familiar with the results expected from the introduction of BI, in relation to the success criteria of its implementation and long-term operation.
The number of projects and the amount of investment into artificial intelligence (AI) based business process automation is increasing that is also due to research advancements in corresponding fields. To utilise its true power, business organisations shall identify and treat risks arising from AI, that must be reduced to an acceptable level to maintain fraud-free business operation in alignment with external legislative requirements. If risks are not assessed, then AI might cause greater headache resulting in expensive implementation without business benefit. The objective of the paper is to analyse the nature of risk elements that AI can bring to the life of corporations and the countermeasures that shall be implemented by analysing general IT risk assessment processes and the stages of intelligent system development. The article also examines frameworks for AI risk management approaching risks associated with intelligent decision making by providing guidelines of required business processes to be implemented.
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