Intelligent Systems in Accounting, Finance and Management
- Publication date:
- Nbr. 25-2, April 2018
- Nbr. 25-1, January 2018
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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.
No abstract is available for this article.
- 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.
- MICRO CREDIT RISK METRICS: A COMPREHENSIVE REVIEW
SUMMARY Default modelling is a general term used for several interrelated fields of risk management. Bond defaults, credit (loan) defaults, firm defaults and country defaults are examples of this kind. The scope and reason for existence of this study is to focus mainly on firm default. The purpose...
- A pattern‐based approach to extract REA value models from business process models
Summary Business models are economic models that describe the rationale of why organizations create and deliver value. These models focus on what organizations offer and why. Business process models capture business activities and the ways in which they are accomplished (i.e. their coordination)....
- Do Sentiments Matter in Fraud Detection? Estimating Semantic Orientation of Annual Reports
Summary We present a novel approach for analysing the qualitative content of annual reports. Using natural language processing techniques we determine if sentiment expressed in the text matters in fraud detection. We focus on the Management Discussion and Analysis (MD&A) section of annual reports...
- Assessing Systemic Importance With a Fuzzy Logic Inference System
Summary Three metrics are designed to assess Colombian financial institutions' size, connectedness and non‐substitutability as the main drivers of systemic importance: (i) centrality as net borrower in the money market network; (ii) centrality as payments originator in the large‐value payment...
- Lottery Payment Cards: A Study of Mental Accounting
Summary This study analyses the difficulties of using stored‐value cards for noncash payment adoption and payment framing behaviour development. This study applies the Rasch model via mental accounting theory to identify unobservable and latent difficulties in adopting noncash payment instruments...
- Management of Knowledge Sources Supported by Domain Ontologies: Building and Construction Case Studys
Summary This paper introduces a novel conceptual framework to support the creation of knowledge representations based on enriched semantic vectors, using the classical vector space model approach extended with ontological support. This work is focused on collaborative engineering projects where...
- Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research
Summary Natural language processing (NLP) is a part of the artificial intelligence domain focused on communication between humans and computers. NLP attempts to address the inherent problem that while human communications are often ambiguous and imprecise, computers require unambiguous and precise...
- A web‐based multi‐agent decision support system for a city‐oriented management of cruise arrivals
Summary Cruise tourism represents a strategic sector for the economic growth of several countries, impacting on different direct and indirect markets. The arrival of cruises in a city represents an unmissable opportunity to increment its tourist market penetration. Nevertheless, the management of...
- An empirical investigation of analytical procedures using mixture distributions
Summary Analytical procedures are evaluations of account and transaction flow information made by a study of plausible relationships between both accounting and non‐accounting data. This study investigates the performance of Tweedie distributions (which have Gaussian distributions as members) in...
- Features selection, data mining and finacial risk classification: a comparative study
Summary The aim of this paper is to compare several predictive models that combine features selection techniques with data mining classifiers in the context of credit risk assessment in terms of accuracy, sensitivity and specificity statistics. The t‐statistic, Battacharrayia statistic, the area...