Publication
Event entry time prediction in financial business processes using machine learning: A use case from loan applications
Michael Frey; Andreas Emrich; Peter Fettke; Peter Loos
In: Proceedings of the 51st Hawaii International Conference on System Sciences 2018 (Hrsg.). Proceedings of the 51st Hawaii International Conference on System Sciences 2018. Hawaii International Conference on System Sciences (HICSS), January 3-6, Hilton Waikoloa Village, Big Island, Hawaii, USA, Pages 1386-1394, ISBN 978-0-9981331-1-9, IEEE Computer Society, 2018.
Abstract
The recent financial crisis has forced politics
to overthink regulatory structures and compliance
mechanisms for the financial industry. Faced with these
new challenges the financial industry in turn has to
reevaluate their risk assessment mechanisms. While
approaches to assess financial risks, have been widely
addressed, the compliance of the underlying business
processes is also crucial to ensure an end-to-end
traceability of the given business events. This paper
presents a novel approach to predict entry times and
other key performance indicators of such events in a
business process. A loan application process is used as a
data example to evaluate the chosen feature modellings
and algorithms.