Underwriting the Privacy Premium
Reframing data privacy as a measurable risk can improve decision-making, model performance, and customer trust.
Carla wasn’t new to privacy challenges. As fictional director of data science at ShieldSure Mutual, a fictional mid-sized auto insurer with an appetite for innovation, she had seen her share of balancing acts (between underwriting precision and customer trust, between legal safety and model accuracy). But something about this particular moment felt different.
ShieldSure had invested heavily in telematics-based policies—offering discounts to drivers willing to share real-time data from mobile apps or plug-in devices. In exchange, drivers got fairer pricing tailored to how safely they drove, not just their age or zip code. Customers liked the idea, in theory. But adoption lagged. Feedback from agents revealed the hesitation: people didn’t trust that their personal data would stay private, even if names and IDs were stripped out.
Internally, Carla’s team had taken what they thought were all the right steps. The models were trained using differentially privat…