arXiv:2605.28327v1 Announce Type: cross Abstract: Traditional insurance pricing relies on risk-based principles that ensure actuarial fairness and solvency but do not explicitly account for policyholders' price sensitivity. We formulate insurance pricing as a decision-making problem and study it using tools from off-policy evaluation and stochastic control. We propose a kernelized inverse propensity score estimator that exploits local structure in the action space and yields variance reduction compared to the classical inverse propensity score estimator. Building on these value estimates, we i
The increasing sophistication of AI models and the availability of large datasets make it possible to apply advanced machine learning techniques to complex financial problems like insurance pricing, moving beyond traditional actuarial methods.
This development signals a move towards more dynamic and personalized insurance pricing, potentially disrupting established business models and improving profitability for insurers, while also impacting policyholder fairness and market risk.
Insurance pricing models will shift from broad risk-based principles to highly granular, data-driven optimization that considers individual policyholder behavior and price sensitivity, enabled by off-policy evaluation.
- · Insurers adopting advanced AI pricing
- · Data scientists and machine learning engineers
- · Policyholders receiving more personalized rates
- · Traditional actuarial firms
- · Insurers slow to adopt AI
- · Policyholders whose price sensitivity was previously subsidized
Insurance companies will achieve higher profitability through optimized pricing and reduced adverse selection.
Increased competition among insurers will lead to more complex pricing structures and a need for greater regulatory oversight on AI ethics and fairness.
The application of off-policy evaluation could expand to other financial services, leading to more personalized and potentially volatile pricing across banking and lending.
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