  Fraud detection is a difficult problem that can benefit from predictive
modeling. However, the verification of a prediction is challenging; for a
single insurance policy, the model only provides a prediction score. We present
a case study where we reflect on different instance-level model explanation
techniques to aid a fraud detection team in their work. To this end, we
designed two novel dashboards combining various state-of-the-art explanation
techniques. These enable the domain expert to analyze and understand
predictions, dramatically speeding up the process of filtering potential fraud
cases. Finally, we discuss the lessons learned and outline open research
issues.
