Process mining with real world financial loan applications: Improving inference on incomplete event logs.

Moreira, C; Haven, E; Sozzo, S; Wichert, A

Process mining with real world financial loan applications: Improving inference on incomplete event logs.

Moreira, C Haven, E Sozzo, S Wichert, A

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Publisher:: PUBLIC LIBRARY SCIENCE
Publication Type:: Journal Article
Citation:: PLoS One, 2018, 13, (12), pp. e0207806
Issue Date:: 2018

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Field	Value	Language
dc.contributor.author	Moreira, C
dc.contributor.author	Haven, E
dc.contributor.author	Sozzo, S
dc.contributor.author	Wichert, A
dc.contributor.editor	Wen, F
dc.date.accessioned	2024-12-18T01:23:56Z
dc.date.available	2018-10-06
dc.date.available	2024-12-18T01:23:56Z
dc.date.issued	2018
dc.identifier.citation	PLoS One, 2018, 13, (12), pp. e0207806
dc.identifier.issn	1932-6203
dc.identifier.issn	1932-6203
dc.identifier.uri	http://hdl.handle.net/10453/182684
dc.description.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.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	PUBLIC LIBRARY SCIENCE
dc.relation.ispartof	PLoS One
dc.relation.isbasedon	10.1371/journal.pone.0207806
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	General Science & Technology
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Data Interpretation, Statistical
dc.subject.mesh	Data Mining
dc.subject.mesh	Decision Support Techniques
dc.subject.mesh	Financial Management
dc.subject.mesh	Heuristics
dc.subject.mesh	Humans
dc.subject.mesh	Netherlands
dc.subject.mesh	Probability
dc.subject.mesh	Humans
dc.subject.mesh	Data Interpretation, Statistical
dc.subject.mesh	Probability
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Decision Support Techniques
dc.subject.mesh	Financial Management
dc.subject.mesh	Netherlands
dc.subject.mesh	Data Mining
dc.subject.mesh	Heuristics
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Data Interpretation, Statistical
dc.subject.mesh	Data Mining
dc.subject.mesh	Decision Support Techniques
dc.subject.mesh	Financial Management
dc.subject.mesh	Heuristics
dc.subject.mesh	Humans
dc.subject.mesh	Netherlands
dc.subject.mesh	Probability
dc.title	Process mining with real world financial loan applications: Improving inference on incomplete event logs.
dc.type	Journal Article
utslib.citation.volume	13
utslib.location.activity	United States
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-12-18T01:23:52Z
pubs.issue	12
pubs.publication-status	Published online
pubs.volume	13
utslib.citation.issue	12

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.

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http://hdl.handle.net/10453/182684