  Prescriptive process monitoring methods seek to improve the performance of a
process by selectively triggering interventions at runtime (e.g., offering a
discount to a customer) to increase the probability of a desired case outcome
(e.g., a customer making a purchase). The backbone of a prescriptive process
monitoring method is an intervention policy, which determines for which cases
and when an intervention should be executed. Existing methods in this field
rely on predictive models to define intervention policies; specifically, they
consider policies that trigger an intervention when the estimated probability
of a negative outcome exceeds a threshold. However, the probabilities computed
by a predictive model may come with a high level of uncertainty (low
confidence), leading to unnecessary interventions and, thus, wasted effort.
This waste is particularly problematic when the resources available to execute
interventions are limited. To tackle this shortcoming, this paper proposes an
approach to extend existing prescriptive process monitoring methods with
so-called conformal predictions, i.e., predictions with confidence guarantees.
An empirical evaluation using real-life public datasets shows that conformal
predictions enhance the net gain of prescriptive process monitoring methods
under limited resources.
