Data Science Techniques in Knowledge-Intensive Business Processes

Autor:	Matthias Lederer, Joanna Riedl
Rok vydání:	2020
Předmět:	Investment banking business.industry Business process 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Accounting Use case 02 engineering and technology business
Zdroj:	International Journal of Data Analytics. 1:52-67
ISSN:	2644-1713 2644-1705
DOI:	10.4018/ijda.2020010104
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::799062a96cc8fe6edef739b77a18b3a0 https://doi.org/10.4018/ijda.2020010104 Zobrazit plný text záznamu