Intelligent Systems in Accounting, Finance and Management

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  • Issue Information

    No abstract is available for this article.

  • Asking ‘Why’ in AI: Explainability of intelligent systems – perspectives and challenges

    Summary Recent rapid progress in machine learning (ML), particularly so‐called ‘deep learning’, has led to a resurgence in interest in explainability of artificial intelligence (AI) systems, reviving an area of research dating back to the 1970s. The aim of this article is to view current issues concerning ML‐based AI systems from the perspective of classical AI, showing that the fundamental problems are far from new, and arguing that elements of that earlier work offer routes to making progress towards explainable AI today.

  • Defining personalized concepts for XBRL using iPAD‐drawn fuzzy sets

    Summary An efficient and effective analysis of business data requires a better understanding of what the data represents, and to what degree. A human‐like way of accomplishing that without being too detailed yet learning more about data content is to summarize and map the data into concepts familiar to a person performing analysis. Processes of summarization help identify the most essential facts that are embedded in the data. All this is of significant importance for analysis of large amounts of business data required to make good and sound financial decisions. There are two aspects enabling more comprehensive yet easier processing of data: a standardized representation format of financial data; and a human‐friendly way of defining concepts and using them for building personalized models representing processing data. The first of the aspects has been addressed by the eXtensible Business Reporting Language (XBRL)—a standardized format of defining, representing and exchanging corporate and financial information. The second aspect is related to providing individuals with the ability to gain understanding of data content via determining a degree of truth of statements summarizing data based on their own perception of concepts they are looking for. In this paper, we introduce a tablet application—Tablet‐based input of Fuzzy Sets (TiFS)—and demonstrate its usefulness for entering personalized definitions of concepts and terms that enable a quick analysis of financial data. Such analysis means utilization of soft queries and operations of aggregation that extract and summarize the data and present it in a form familiar to analysts. The application allows for defining concepts and terms with ‘finger‐made’ drawings representing a person's perception of concepts. Further, these definitions are used to build summarization statements for exploring XBRL data. They are equipped with ‘drawn’ definitions of linguistic terms (e.g. LARGE, SMALL, FAST) and linguistic quantifiers (e.g. ALL, MOSTLY), and enable summarization of data content from the perspective of a user's interests. The ‘drawn’ linguistic terms and quantifiers represent membership functions of fuzzy sets. Utilization of fuzzy sets allows for performing operations of data summarization in a human‐like way. The application of TiFS illustrates ease of inputting personalized definitions of concepts and their influence on the interpretation of data. This introduces aspects of personalization and adaptation of artificial intelligence systems to perceptions and views of individuals. The proposed application is used to perform a basic analysis of an XBRL document.

  • Strategy on a Page: An ArchiMate‐based tool for visualizing and designing strategy

    Summary Nowadays, organizations need to be able to adjust more rapidly to the circumstances of their environment, at a strategic, tactical, and operational level. However, most software tools are designed to support the tactical and operational levels, while at a strategic level there are not many options available. In this paper, we propose a software tool which supports modelling of strategic information, covering several well‐known strategy techniques, and also facilitates the design of highly customizable management dashboards. To validate our proposed software tools, we perform two case studies, with two inherently different organizations, namely a public university and an investment fund.

  • How fast to run in the Red Queen race?

    Summary This paper creates a market ecosystem, via an agent‐based model, that combines the dynamic features of the Red Queen effect with well‐accepted business world performance indicators. Essentially, firms are tasked with remaining ‘alive’ by adapting to their environment through implementing a competitive response of innovating or imitating. An analysis of the firms’ behaviours delivers a deep understanding of the drivers of innovative behaviour within the economy. The key findings of the paper are (1) that concentrated markets are not entirely detrimental to innovative behaviour, with the blend of firm type being a more important consideration, and (2) that the rate at which an innovation impairs existing markets affects the activity levels of the firms within the population. The model's results are validated against a matching study based on real‐world data.

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  • Toward an ontology‐driven blockchain design for supply‐chain provenance

    Summary An interesting research problem in our age of Big Data is that of determining provenance. Granular evaluation of provenance of physical goods (e.g., tracking ingredients of a pharmaceutical or demonstrating authenticity of luxury goods) has often not been possible with today's items that are produced and transported in complex, interorganizational, often internationally spanning supply chains. Recent adoptions of the Internet of Things and blockchain technologies give promise at better supply‐chain provenance. We are particularly interested in the blockchain, as many favored use cases of blockchain are for provenance tracking. We are also interested in applying ontologies, as there has been some work done on knowledge provenance, traceability, and food provenance using ontologies. In this paper, we make a case for why ontologies can contribute to blockchain design. To support this case, we analyze a traceability ontology and translate some of its representations to smart contracts that execute a provenance trace and enforce traceability constraints on the Ethereum blockchain platform.

  • Issue Information

    No abstract is available for this article.

  • Decision analytics mobilized with digital coaching

    Summary The context to be addressed is the digitalization of industry and industrial processes. Digitalization brings enhanced customer relationships and value‐chain integration, which are effective instruments to meet increasing competition and slimmer margins for productivity and profitability. Digitalization also brings more pronounced requirements for effective planning, problem solving and decision making in an increasingly complex and fast‐changing environment. Decision analytics will meet the challenges from the growing global competition that major industrial corporations face and will help solve the problems of big data/fast data that digitalization is generating as a by‐product. A mantra is appearing in business magazines – that powerful, intelligent systems will be effective tools for the digitalization of industrial processes – but much less attention appears to be paid to the fact that users need advanced knowledge and skills to benefit from the intelligent systems. First, an effective transfer of knowledge from developers, experts and researchers to users (including management) will be needed; second, the daily use and operations of the systems need to be supported, as automated, intelligent industrial systems are complex to operate. We look at this transfer as knowledge mobilization and will work out how the mobilization can be supported with coaching; this coaching needs to be digital, as human coaches are both scarce and too expensive to employ in large numbers.

  • Evolution of multivariate copulas in continuous and discrete processes

    Summary There has been much interest in copulas, which are known to provide a flexible tool for analyzing the dependence structure among random variables. Dependence relations must be dynamic rather than static in nature. However, copulas are useful mainly for static matters. Thus we introduce evolving multivariate copulas, which transform through time autonomously governed by the multivariate heat equation. Our aims are to prove their existences and solutions to analyze their transitions. Moreover, we construct discrete type to apply empirical data analysis and investigate their properties, and prove that they converge to their original continuous type.

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