Project type: Bridge Project
Business processes in private companies and public organisations are today widely supported by Enterprise Resource Planning (ERP), Business Process Management (BPM) and Electronic Case Management (ECM) systems [1], put into use with the aim to improve efficiency of the business processes. Recently, also blockchain technologies are being proposed as a means to provide guarantees for security, computational integrity and pseudonymous agency.
The combined result is however often an increasingly elaborate information systems landscape, leading to ineffectiveness, limited understanding of business processes, inability to predict and find the root cause of losses, errors and fraud, and inability to adapt the business processes [1]. This lack of understanding, agility and control over business processes places a major burden on the organisations. For instance, a recent report concludes that the Danish Ministry of Taxation’s control of the state’s annual revenue of one trillion DKK is so “deficient and weak” that there is a clear “increased risk” that employees can cheat and abuse for their own gain in the same style as the recent Britta Nielsen and Armed forces cases.
Enterprise and block chain systems generate a plethora of highly granular data recording their operation. Machine learning has a great potential to aid in the analysis of this data in order to predict errors, detect fraud and improve their efficiency. Knowledge of business processes can also be used to support the needed transformation of old and heterogeneous it landscapes to new platforms. Application areas include Anti-Money-Laundering (AML) and Know-Your-Customer (KYC) supervision of business processes in the financial sector, supply chain management in agriculture and foodstuff supply, and compliance and optimisation of workflow processes in the public sector.
July 1, 2021 – December 31, 2025 – 3,5 years
University of Copenhagen
Department of Computer Science
E: slaats@di.ku.dk
University of Copenhagen
Department of Computer Science
University of Copenhagen
Department of Computer Science
Copenhagen Business School
Department of Digitalization
University of Copenhagen
Department of Computer Science
Gekkobrain
University of Copenhagen
Department of Computer Science
IT University of Copenhagen
Department of Computer Science
Gekkobrain
eToro
University of Copenhagen
Dept. of Computer Science
The aim of DIREC is to expand the capacity within research, innovation and education in digital technologies in Denmark. In addition, DIREC shall contribute to the competitiveness of Denmark through collaboration with Danish businesses and the public sector on developing new innovative products and services based on the newest digital technologies.
DIREC is partially funded by Innovation Fund Denmark.
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