Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/45535
Title: Process mining with real world financial loan applications: Improving inference on incomplete event logs.
Authors: Moreira, Catarina
Haven, Emmanuel
Sozzo, Sandro
Wichert, Andreas
First Published: 31-Dec-2018
Publisher: Public Library of Science
Citation: PLoS ONE, 2018, 13(12): e0207806
Abstract: In this work, we analyse and model a real life financial loan application belonging to a sample bank in the Netherlands. The event log is robust in terms of data, containing a total of 262 200 event logs, belonging to 13 087 different credit applications. The goal is to work out a decision model, which represents the underlying tasks that make up the loan application service. To this end we study the impact of incomplete event logs (for instance workers forget to register their tasks). The absence of data is translated into a drastic decrease of precision and compromises the decision models, leading to biased and unrepresentative results. We use non-classical probability to show we can better reduce the error percentage of inferences as opposed to classical probability.
DOI Link: 10.1371/journal.pone.0207806
eISSN: 1932-6203
Links: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0207806
http://hdl.handle.net/2381/45535
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Description: All relevant data will be available in the public repository: https://github.com/catarina-moreira/bpmn.
Appears in Collections:Published Articles, School of Management

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