Event Entry Time Prediction in Financial Business Processes Using Machine Learning - A Use Case From Loan Applications
Event Entry Time Prediction in Financial Business Processes Using Machine Learning - A Use Case From Loan Applications
Files
Date
2018-01-03
Authors
Frey, Michael
Emrich, Andreas
Fettke, Peter
Loos, Peter
Journal Title
Journal ISSN
Volume Title
Publisher
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.
Description
Keywords
Machine Learning and Network Analytics in Finance,
process prediction, machine learning, loan applications, event entry time prediction, process mining
Citation
Rights
Access Rights
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.