Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes

TL;DR

This paper introduces a conformal prediction-based approach to prescriptive process monitoring, providing confidence guarantees to improve intervention decisions and resource efficiency in business process management.

Contribution

It extends existing prescriptive monitoring methods with conformal predictions, enhancing decision confidence and resource utilization.

Findings

Conformal predictions improve net gain in process monitoring.

The approach reduces unnecessary interventions.

Empirical results on real datasets validate effectiveness.

Abstract

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.