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In this work, we analyse and model a real life financial loan applicationbelonging to a sample bank in the Netherlands. The log is robust in terms ofdata, containing a total of 262 200 event logs, belonging to 13 087 differentcredit applications. The dataset is heterogeneous and consists of a mixture ofcomputer generated automatic processes and manual human tasks. The goal is towork out a decision model, which represents the underlying tasks that make upthe loan application service, and to assess potential areas of improvement ofthe institution's internal processes. To this end we study the impact ofincomplete event logs for the extraction and analysis of business processes. Itis quite common that event logs are incomplete with several amounts of missinginformation (for instance, workers forget to register their tasks). Absence ofdata is translated into a drastic decrease of precision and compromises thedecision models, leading to biased and unrepresentative results. We investigatehow classical probabilistic models are affected by incomplete event logs and weexplore quantum-like probabilistic inferences as an alternative mathematicalmodel to classical probability. This work represents a first step towardssystematic investigation of the impact of quantum interference in a real lifelarge scale decision scenario. The results obtained in this study indicatethat, under high levels of uncertainty, the quantum-like models generatequantum interference terms, which allow an additional non-linearparameterisation of the data. Experimental results attest the efficiency of thequantum-like Bayesian networks, since the application of interference terms isable to reduce the error percentage of inferences performed over quantum-likemodels when compared to inferences produced by classical models.

The Dutch's Real World Financial Institute: Introducing Quantum-Like Bayesian Networks as an Alternative Model to deal with Uncertainty