An optimal control model for adaptive auto-insurance pricing learns claim risks from telematics, captures multi-period driver responses to discounts, and applies Lagrangian relaxation to achieve asymptotically optimal portfolio-wide discount allocation.
The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation,
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Prescriptive Optimization for Adaptive Auto-insurance Pricing with Telematics Data
An optimal control model for adaptive auto-insurance pricing learns claim risks from telematics, captures multi-period driver responses to discounts, and applies Lagrangian relaxation to achieve asymptotically optimal portfolio-wide discount allocation.