Data Science Techniques in Knowledge-Intensive Business Processes: A Collection of Use Cases for Investment Banking

Data Science Techniques in Knowledge-Intensive Business Processes: A Collection of Use Cases for Investment Banking

Matthias Lederer (ISM International School of Management, Dortmund, Germany) and Joanna Riedl (International School of Management, Dortmund, Germany)
Copyright: © 2020 |Pages: 16
DOI: 10.4018/IJDA.2020010104

Abstract

The processes of an investment bank are considered to be particularly knowledge-intensive, because analysts need to extract or generate relevant knowledge from a variety of data. With increasing digitization, modern data science and business intelligence techniques are available to support or partially automate these activities. This study presents concrete use cases for front office processes of an investment bank as how knowledge management techniques can be used. For example, the article describes how expert systems can be used in the due diligence review or how fuzzy logic systems help in deciding whether to buy or sell securities. The article is based on 1079 texts (e.g. documented cases and articles) and serves researchers as well as practitioners as an application overview of data science techniques in the example area of knowledge-intensive banking processes.
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Foundations

Well-founded methods, processes and algorithms as well as supporting systems are already being used in many companies to generate knowledge from data. Various concrete techniques such as neural networks and expert systems are discussed in science and practice (Gallant, 1993). In general, such techniques aim to support a company's strategy, structures, and processes so that lessons learned can be used to improve routine tasks (incremental innovation) or even to overcome with completely new business models (radical or disruptive innovations) (Vyas, 2016). By using data with digital techniques, knowledge can be codified and shared between employees, teams and even virtual agents. In a nutshell, with digital data science techniques, organizations can create new value and innovation through knowledge (Berman, 2018).The particular relevance of the topic of data science is reflected by the fact that it is not the pure acquisition of information that is at the center of attraction - due to globalization and the associated rapid growth of digital networks, smart business decisions are characterized by enormous volume, speed and heterogeneity of data (sometimes discussed under the term “big data”). The main task for many companies improving their processes data-driven is to use internal as well as external knowledge to make better decisions in order to increase a company's learning, adaptability and innovation capacity (Stairs & Reynolds, 2017).

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