Anushka V Rodi

Anushka V Rodi

Anushka V Rodi

Personal Lines Insurance Technical Analyst, Independent Researcher, USA

Title of Talk:

The Rollout Coexistence Window: Quantifying the Multi-State Regulatory Approval Gap That Forces AI Rating Systems to Enforce Two Compliance Frameworks Simultaneously


Abstract

Not all states approve a multi-state insurance carrier's new rating program on the same day. California has an average of 246 days to approve a personal auto rate filing. A file-and-use state approves in a few weeks. The carrier's AI rating system needs to enforce two different versions of its rating logic for different subsets of states during that seven-month or longer window of time between first and last approval: one in the subset of states in which the carrier is still awaiting approval, and one in the subset of states in which the carrier already has approval. In this talk, the Rollout Coexistence Window is introduced as an original analytical construct the first structured framework that directly connects state regulatory approval timeline variance, as documented in NAIC classification data and a 2024 Insurance Research Council study, to AI rating system dual-version compliance risk. It outlines five categories of rating logic that will need to be independently validated and correctly jurisdiction-partitioned for the entire length of this window and suggests a new quantifiable pre-rollout planning metric, the Rollout Coexistence Window, which current AI governance frameworks do not offer. The main claim is that multi-state AI compliance is not a deployment issue it is a timing issue, and timing is quantifiable.


Brief Profile
Anushka V Rodi is a Personal Lines Insurance Technical Analyst and Independent Researcher based in Madison, Wisconsin, USA, specializing in the specification, governance, and empirical validation of personal lines insurance rating and underwriting rules for multi-state regulatory compliance in AI-enabled enterprise systems. Her work focuses on the intersection of state insurance regulation, actuarial rating logic, and enterprise platform design translating complex, state-specific compliance rules into deterministic system logic that governs how AI systems price, underwrite, and adjudicate risk across multi-state environments. Her independent research addresses AI governance in regulated underwriting systems, rating logic specification completeness in regulatory documents, and compliance risk quantification during multi-state platform migrations.