Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach
This addresses resource-limited optimization in business processes like loan origination, offering an incremental improvement over existing methods that assume unbounded interventions.
The paper tackles the problem of optimizing business process interventions under finite resource constraints by proposing a prescriptive process monitoring technique that combines predictive modeling and causal inference to allocate resources effectively, with a preliminary evaluation showing it yields a higher net gain than a non-causal baseline.
Prescriptive process monitoring is a family of techniques to optimize the performance of a business process by triggering interventions at runtime. Existing prescriptive process monitoring techniques assume that the number of interventions that may be triggered is unbounded. In practice, though, specific interventions consume resources with finite capacity. For example, in a loan origination process, an intervention may consist of preparing an alternative loan offer to increase the applicant's chances of taking a loan. This intervention requires a certain amount of time from a credit officer, and thus, it is not possible to trigger this intervention in all cases. This paper proposes a prescriptive process monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints. The proposed technique relies on predictive modeling to identify cases that are likely to lead to a negative outcome, in combination with causal inference to estimate the effect of an intervention on the outcome of the case. These outputs are then used to allocate resources to interventions to maximize a cost function. A preliminary empirical evaluation suggests that the proposed approach produces a higher net gain than a purely predictive (non-causal) baseline.