151 research outputs found
Identification of financial statement fraud in Greece by using computational intelligence techniques
The consequences of financial fraud are an issue with far-reaching for investors, lenders, regulators, corporate sectors and consumers. The range of development of new technologies such as cloud and mobile computing in recent years has compounded the problem. Manual detection which is a traditional method is not only inaccurate, expensive and time-consuming but also they are impractical for the management of big data. Auditors, financial institutions and regulators have tried to automated processes using statistical and computational methods. This paper presents comprehensive research in financial statement fraud detection by using machine learning techniques with a particular focus on computational intelligence (CI) techniques. We have collected a sample of 2469 observations since 2002 to 2015. Research gap was identified as none of the existing researchers address the association between financial statement fraud and CI-based detection algorithms and their performance, as reported in the literature. Also, the innovation of this research is that the selection of data sample is aimed to create models which will be capable of detecting the falsification in financial statements
Designing a Process Mining-Enabled Decision Support System for Business Process Standardization in ERP Implementation Projects
Process standardization allows to optimize ERP systems and is a nec-essary step prior to ERP implementation projects. Traditional approaches to standardizing business processes are based on manually created "de-jure" process models, which are distorted, error-prone, simplistic, and often deviating from process reality. Theoretically embedded in the organizational contingency theory as kernel theory, this paper employs a design science approach to design a process mining-enabled decision support system (DSS) which combines bottom-up process mining models with manually added top-down standardization infor-mation to recommend a suitable standard process specification from a repository. Extended process models of the as-is process are matched against a repository of best-practice standard process model using an attributebased process similarity matching algorithm. Thus, the DSS aims to reduce the overall costs of process standardization, to optimize the degree of fit between the organization and the implemented processes, and to minimize the degree of organizational change re-quired in standardization and ERP implementation projects. This paper imple-ments a working prototype instantiation in the open-source process analytics platform Apromore based on a real-life event log and standardization attributes for the Purchase-to-Pay and Order-to-Cash processes from three SAP R/3 ERP systems at the industry partner
Individual-environment interactions in swimming: The smallest unit for analysing the emergence of coordination dynamics in performance?
Displacement in competitive swimming is highly dependent on fluid characteristics,
since athletes use these properties to propel themselves. It is essential for sport
scientists and practitioners to clearly identify the interactions that emerge between
each individual swimmer and properties of an aquatic environment. Traditionally, the
two protagonists in these interactions have been studied separately. Determining the
impact of each swimmerâs movements on fluid flow, and vice versa, is a major
challenge. Classic biomechanical research approaches have focused on swimmersâ
actions, decomposing stroke characteristics for analysis, without exploring
perturbations to fluid flows. Conversely, fluid mechanics research has sought to
record fluid behaviours, isolated from the constraints of competitive swimming
environments (e.g. analyses in two-dimensions, fluid flows passively studied on
mannequins or robot effectors). With improvements in technology, however, recent
investigations have focused on the emergent circular couplings between swimmersâ
movements and fluid dynamics. Here, we provide insights into concepts and tools that
can explain these on-going dynamical interactions in competitive swimming within
the theoretical framework of ecological dynamics
A Survey of Bayesian Statistical Approaches for Big Data
The modern era is characterised as an era of information or Big Data. This
has motivated a huge literature on new methods for extracting information and
insights from these data. A natural question is how these approaches differ
from those that were available prior to the advent of Big Data. We present a
review of published studies that present Bayesian statistical approaches
specifically for Big Data and discuss the reported and perceived benefits of
these approaches. We conclude by addressing the question of whether focusing
only on improving computational algorithms and infrastructure will be enough to
face the challenges of Big Data
Big Data for the Greater Good: An Introduction
Big Data, perceived as one of the breakthrough technological developments of our times, has the potential to revolutionize essentially any area of knowledge and impact on any aspect of our life. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, analysts, researchers, and business users can analyze previously inaccessible or unusable data to gain new insights resulting in better and faster decisions, and producing both economic and social value; it can have an impact on employment growth, productivity, the development of new products and services, traffic management, spread of viral outbreaks, and so on. But great opportunities also bring great challenges, such as the loss of individual privacy. In this chapter, we aim to provide an introduction into what Big Data is and an overview of the social value that can be extracted from it; to this aim, we explore some of the key literature on the subject. We also call attention to the potential âdarkâ side of Big Data, but argue that more studies are needed to fully understand the downside of it. We conclude this chapter with some final reflections
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