Conformalized prescriptive machine learning for uncertainty-aware automated decision making: the case of goodwill requests

Abstract Due to the inherent presence of uncertainty in machine learning (ML) systems, the usage of ML is until now out of scope for many critical (financial) business processes. One such process is goodwill assessment at car manufacturers, where a large part of goodwill cases is still assessed manually by human experts. To increase the degree of automation while still providing an overall reliable assessment service, we propose a selective uncertainty-aware automated decision making approach based on uncertainty quantification through conformal prediction. In our approach, goodwill requests are still shifted to human experts in case the risk of a wrong assessment is too high. Nevertheless, ML can be introduced into the process with reduced and controllable risk. We hereby determine the risk of wrong ML assessments through two hierarchical conformal predictors that make use of the prediction set and interval size as the main criteria for quantifying uncertainty. We also utilize conformal prediction’s property to output empty prediction sets if no prediction is significant enough and abstain from an automatic decision in that case. Instead of providing mathematical guarantees for limited risk, we focus on the risk vs. degree of automation trade-off and how a business decision maker can select in an a posteriori fashion a trade-off that best suits the business problem at hand from a set of pareto optimal solutions. We also show empirically on a goodwill data set of a BMW National Sales Company that by only selecting certain requests for automated decision making we can significantly increase the accuracy of automatically processed requests. For instance, from 92 to 98% for labor and from 90 to 98% for parts contributions respectively, while still maintaining a degree of automation of approximately 70%.