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Abstract: Like many service industries, the financial industry is largely characterized
by administrative and back-office processes and distinguished by a broad systems
landscape with a high proportion of legacy systems. Missing interfaces between information
systems, user interfaces, or web applications often require many manual
activities. As banks are often functionally organized into traditional departments, a
process-oriented organizational structure is rarely in place. The financial industry
therefore offers enormous potential for the use of robotic process automation (RPA)
and the raising of potential benefits such as process-related cost savings, time reductions,
and quality improvements.
The aim of this chapter is to describe the tremendous opportunities that the use of
RPA technology offers to the financial industry and to explain how these opportunities
can be realized. Therefore, we start by explaining the challenges that progressive digitalization
poses to the industry and how RPA, but also more advanced technologies
(that work not only rule-based but also define own rules), such as artificial intelligence,
can help to overcome them. As well as providing an overview of the various
applications of RPA in the financial industry, we also provide a comprehensive case
study of a relevant practical application
This article examines sentiment of 12,821 reports on cyber-attacks on firms around the world from 2011 to mid-year 2019. With theworld wide web (www) and associated technologies connecting opinions worldwide, sentiment in the world wide web is an importantperspective to understanding developments in cyber-attacks and the public opinion thereof, because much of the public debate isshared online. Such sentiment can potentially explain developments in regulation and may be indirectly associated with the buildingof defense mechanisms of cyber-victims. Results show that the quantity of reports on cyber-attacks on firms has been increasing,but the sentiment of reports on cyber-attacks has been decreasing. Both trends reflect the increasing severity of the issue and arelikely contributing to regulatory responses. Results also show that the sentiment is conditional to the source that reports the cyber-attack.
One domain of application of artificial intelligence (AI) is decision support, particularly in management. Although there are already research streams examining the interaction of AI and humans (e.g. the stream on "hybrid intelligence"), there are still numerous open research gaps – for example, a comprehensive overview of which factors favor the intention to use AI is missing. By conducting a systematic literature review, we identify the factors that potentially positively influence AI usage intentions for decision-making processes in organizations. From this, we create a framework that both provides practical implications for the successful use of AI in organizational decision-making processes and delivers further research approaches, for example, on the validity/ usability of proven IS adoption models in the present context.
Chaoticity Versus Stochasticity in Financial Markets: Are Daily S&P 500 Return Dynamics Chaotic?
(2021)
In this study, we present a combinatory chaos analysis of daily wavelet-filtered (denoised) S&P 500 returns (2000–2020) compared with respective surrogate datasets, Brownian motion returns and a Lorenz system realisation. We show that the dynamics of the S&P 500 return series consist of an almost equally divided combination of stochastic and deterministic chaos. The strange attractor of the S&P 500 return system is graphically displayed via Takens’ embedding and by spectral embedding in combination with Laplacian Eigenmaps. For the field of nonlinear and financial chaos research, we present a bibliometric analysis paired with citation network analysis. We critically discuss implications and future prospects.
Sustainability accounting as a distinct discipline has evolved over the last decades and is still expanding. As a multidimensional concept, sustainability accounting, however, relates to various other disciplines and thus provides a suitable setting to examine the extent of interdisciplinarity in accounting research. This paper investigates sustainability accounting in in twofold way. First, it provides a comprehensive and systematic review of academic literature addressing sustainability accounting topics. Based on a systematic search strategy capturing articles in the fields of finance and accounting, management, economics, organization and behavioral research, and international business, we determine a sample of 5,245 articles. Descriptive findings show that equally large proportions of the sample articles are published in accounting, management, and organization journals. Using citation network analysis, we identify existing research clusters within this sample and analyse them with respect to their delineations and interactions. Our results reveal that cluster formation relates to specific subject areas with some clusters showing limited interactions to other subject areas, while other clusters are more “interdisciplinary”. Interviews with influential researchers from the most prominent clusters in the network complement the network analysis results, providing insights into the establishment of paradigms and patterns in academic research that lead to the failure of knowledge integration and impede the transfer of research insights into business and politics.
This study provides an overview of the model evolution and research trends in the field of financial and risk modelling by applying a bibliometric approach from 2008–2019 and an overall citation network analysis. We present a content analysis of contributing authors, countries, journals, main topics, agreements, disagreements and frontiers within the research community and highlight quantitative features such as implemented models, aggregated model-family combinations and algorithms. Moreover, we describe the data sets employed by researchers. Finally, we discuss insights, such as the main statement, namely the non-existence of a “single-best”-approach as well as the future prospects of our findings.
Organizations have repeatedly faced challenges due to (natural) disasters such as pandemics, economic or financial crises, and unexpected events. One reason why some firms cope more efficiently with such unexpected events than others has long been the subject of research, might be found in their resilience design. However, there is still no consensus in the literature and there is no common understanding of the definitions, the conceptualizations of resilience at the organizational level, and the interaction with management control systems (MCS). This study bridges MCS and resilience literature and provides a broader understanding of the relationship between the organization and adversity. Due to its ability to successfully control an organization and provide an effective control environment, we use Simons’ levers of control framework (LOC) as framework for integrating organizational resilience into MCS.
We perform a systematic reviewing of analytical conceptualizations and definitions of management control systems (levers of control) and organizational resilience, supplemented by current empirical findings. Based on literature, we provide a framework which integrates organizational resilience into management control systems. Our findings show that the integration of resilience aspects into MCS enables firms to manage resilience at the organizational level.
Like many service industries, the financial industry is largely characterized by administrative and back-office processes and distinguished by a broad systems landscape with a high proportion of legacy systems. Missing interfaces between Information Systems, User Interfaces or Web Applica-tions often require many manual activities. As banks are often functionally organized into traditional departments, a process-oriented organizational structure is rarely in place. The financial industry therefore offers enormous potential for the use of Robotic Process Automation (RPA) and the rais-ing of potential benefits such as process-related cost savings, time reductions and quality im-provements.
The aim of this chapter is to describe the tremendous opportunities that the use of RPA technology offers to the financial industry and to explain how these opportunities can be realized. Therefore, we start by explaining the challenges that progressive digitalization poses to the industry and how RPA, but also more advanced technologies (that work not only rule-based but also define own rules), such as Artificial Intelligence (AI), can help to overcome them. As well as providing an over-view of the various applications of RPA in the financial industry, we also provide a comprehensive case study of a relevant practical application.