Business Transformation and Innovation Management
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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
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.
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.