Einzeltreffer — DigiBib

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.

Titel:	Process mining with real world financial loan applications: Improving inference on incomplete event logs
Autor/in / Beteiligte Person:	Pinto Moreira, Catarina ; Haven, Emmanuel ; Sozzo, Sandro ; Wichert, Andreas
Link:	https://eprints.qut.edu.au/127302/1/journal.pone.0207806.pdf https://eprints.qut.edu.au/127302/ https://doi.org/10.1371/journal.pone.0207806
Zeitschrift:	PLoS One, 2018
Veröffentlichung:	Public Library of Science, 2018
Medientyp:	academicJournal
DOI:	10.1371/journal.pone.0207806
Schlagwort:	Bayes Theorem Data Interpretation Statistical Data Mining Decision Support Techniques Financial Management/statistics & numerical data Heuristics Humans Netherlands Probability
Sonstiges:	Nachgewiesen in: BASE Sprachen: unknown Collection: Queensland University of Technology: QUT ePrints Document Type: article in journal/newspaper File Description: application/pdf Language: unknown Relation: https://eprints.qut.edu.au/127302/1/journal.pone.0207806.pdf; Pinto Moreira, Catarina, Haven, Emmanuel, Sozzo, Sandro, & Wichert, Andreas (2018) Process mining with real world financial loan applications: Improving inference on incomplete event logs. PLoS One, 13(12), Article number: e0207806.; https://eprints.qut.edu.au/127302/; Science & Engineering Faculty Rights: free_to_read ; http://creativecommons.org/licenses/by/2.5/ ; Consult author(s) regarding copyright matters ; This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au

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