Open AccessProcess mining with real world financial loan applications: Improving inference on incomplete event logs
Author(s) -
Catarina Moreira,
Emmanuel Haven,
Sandro Sozzo,
Andreas Wichert
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0207806
Subject(s) - loan , event (particle physics) , inference , computer science , finance , process (computing) , sample (material) , actuarial science , econometrics , data science , artificial intelligence , business , economics , chemistry , physics , chromatography , quantum mechanics , operating system
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.