Data-driven decision-making systems have a significant impact in the modern world, to the extent of directly contributing to saving human lives. One example of such application is the Crash Detector project developed at Generali Italia. It is a machine learning solution designed to identify vehicle collisions from telematics data collected by car blackboxes. Detecting crashes in real time enables rapid assistance from designated services to those in need. A key challenge for this project is the high imbalance in the available data, where true crash events are extremely rare among all the recorded samples. Such scarsity of positive cases makes it difficult for the model to learn meaningful patterns.