  In this work, we analyse and model a real life financial loan application
belonging to a sample bank in the Netherlands. The log is robust in terms of
data, containing a total of 262 200 event logs, belonging to 13 087 different
credit applications. The dataset is heterogeneous and consists of a mixture of
computer generated automatic processes and manual human tasks. The goal is to
work out a decision model, which represents the underlying tasks that make up
the loan application service, and to assess potential areas of improvement of
the institution's internal processes. To this end we study the impact of
incomplete event logs for the extraction and analysis of business processes. It
is quite common that event logs are incomplete with several amounts of missing
information (for instance, workers forget to register their tasks). Absence of
data is translated into a drastic decrease of precision and compromises the
decision models, leading to biased and unrepresentative results. We investigate
how classical probabilistic models are affected by incomplete event logs and we
explore quantum-like probabilistic inferences as an alternative mathematical
model to classical probability. This work represents a first step towards
systematic investigation of the impact of quantum interference in a real life
large scale decision scenario. The results obtained in this study indicate
that, under high levels of uncertainty, the quantum-like models generate
quantum interference terms, which allow an additional non-linear
parameterisation of the data. Experimental results attest the efficiency of the
quantum-like Bayesian networks, since the application of interference terms is
able to reduce the error percentage of inferences performed over quantum-like
models when compared to inferences produced by classical models.
