Figure 10: LSTM visualization of states for declined loan application processes While one can perform prediction by mining a model from event logs, and mining decision rules for each process branch point (Lakshmanan et al., 2015; Unuvar et al., 2016), this has inherent drawbacks. Process mining algorithms trade off different quality criteria, such as fitness, precision, generalizability, and simplicity. The mined models are abstractions and intentionally imperfect representations of the underlying true process. This impairs predictive performance. Decision mining at process branch points or for trace prefixes (Ceci et al., 2014) makes trade-offs between recall, precision, and parsimony, and may use simplifying assumptions such as linearity. This further impairs predictive performance. Explicit process models and decision rules are useful for understandability. At the same time, however, their intentional abstraction and parsimony makes them less suitable for prediction. In contrast, deep learning networks with their larger parameter space and non-linearity ”skip” the 29