Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/50058

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

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Title: Event Entry Time Prediction in Financial Business Processes Using Machine Learning - A Use Case From Loan Applications
Authors: Frey, Michael
Emrich, Andreas
Fettke, Peter
Loos, Peter
Keywords: Machine Learning and Network Analytics in Finance
process prediction, machine learning, loan applications, event entry time prediction, process mining
Issue Date: 03 Jan 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.
Pages/Duration: 9 pages
URI/DOI: http://hdl.handle.net/10125/50058
ISBN: 978-0-9981331-1-9
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Machine Learning and Network Analytics in Finance


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