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Event Entry Time Prediction in Financial Business Processes Using Machine Learning - A Use Case From Loan Applications

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dc.contributor.author Frey, Michael
dc.contributor.author Emrich, Andreas
dc.contributor.author Fettke, Peter
dc.contributor.author Loos, Peter
dc.date.accessioned 2017-12-28T00:48:08Z
dc.date.available 2017-12-28T00:48:08Z
dc.date.issued 2018-01-03
dc.identifier.isbn 978-0-9981331-1-9
dc.identifier.uri http://hdl.handle.net/10125/50058
dc.description.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.
dc.format.extent 9 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Machine Learning and Network Analytics in Finance
dc.subject process prediction, machine learning, loan applications, event entry time prediction, process mining
dc.title Event Entry Time Prediction in Financial Business Processes Using Machine Learning - A Use Case From Loan Applications
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2018.171
Appears in Collections: Machine Learning and Network Analytics in Finance


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