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.
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math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
HUANet unrolls ADMM iterations into a trainable network that enforces equality constraints exactly via a differentiable correction layer and adds soft first-order optimality conditions during training.
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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.
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HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization
HUANet unrolls ADMM iterations into a trainable network that enforces equality constraints exactly via a differentiable correction layer and adds soft first-order optimality conditions during training.