Proceedings-Artikel

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.

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