Encoding Statutory Variance in Property Rating Systems: A Mandate-Strength Classification and Specification Method for Multi-State Catastrophe Mitigation Credits

Authors

  • Anushka V. Rodi Independent Researcher, Madison, Wisconsin, USA.
  • Animesh Dhole Independent Researcher, Little Rock, Arkansas, USA.
  • Harpreet Singh Siddhu Independent Researcher, Warrington, Pennsylvania, USA.
  • Sravan Reddy Kathi Independent Researcher, Bridgeport, Pennsylvania, USA.

DOI:

https://doi.org/10.70917/ijcisim-2026-4589

Keywords:

Property insurance ratemaking, regulatory compliance, statutory encoding, catastrophe mitigation credits, rating logic specification, core insurance systems, decision tables, SAP BRFplus, rules as code, compliance verification, insurance regulation technology

Abstract

Property insurance rating engines encode jurisdiction-specific catastrophe mitigation credits as local configuration, with no machine-resolvable link to the statutory provisions that require them. Practitioner discussion of this problem generally treats inter-state variation as a matter of differing credit percentages, to be handled by parameterising a single rule. We show that this framing is incorrect. Examining the operative catastrophe mitigation credit provisions of five U.S. jurisdictions from primary sources, we find that obligations differ not in magnitude but in kind: Florida imposes the credit in mandatory terms on the rate filing itself; New York's mandate is delegated, with the statute binding the regulator and the implementing regulation binding the carrier under trigger conditions absent from the statute; California's obligation binds conditionally, only on insurers whose property rating plans segment on wildfire risk, and is mandatory in content once that gate is passed; Texas authorises a construction-system discount without requiring it in the voluntary market, while its building-code mitigation credits bind only the residual-market association through a manual adopted by reference; and Ohio's property rate filing provisions contain no analogous mandate. Four obligation classes and two further structural axes -instrument layer and bound population -therefore appear across five jurisdictions on a single compliance dimension. From this finding we derive three contributions. First, a mandate-strength classification -mandatory, conditional, permissive, absent -stated as a triple over applicability, content strength, and bound population, so that obligation class becomes a first-class property of a rating rule rather than an implicit assumption. Second, a specification method encoding each obligation as a rule object that carries its enabling and operative instruments separately, together with version, mandate class, trigger, adjustment target set, and ordering constraint, and binds to core platform primitives including SAP BRFplus decision tables. Third, a compliance index whose satisfaction predicate is decidable from carrier-internal state and therefore does not presuppose the correctness oracle it exists to approximate, with its alert threshold derived from carrier loss tolerance rather than asserted. A worked analytical example over a synthetic 20,000-policy multi-state portfolio quantifies the consequence of ignoring mandate class: a uniform parameterised configuration diverges from the statute-faithful encoding on 11,875 policies (59.4%), and the divergence is driven by trigger mismatch rather than factor mismatch by a ratio of 22:1 in value. The statutory analysis is the paper's empirical core and is verifiable against the cited enacting texts; the portfolio is synthetic and the quantitative results are analytical rather than observed.

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Published

2026-08-12

How to Cite

Anushka V. Rodi, Animesh Dhole, Harpreet Singh Siddhu, & Sravan Reddy Kathi. (2026). Encoding Statutory Variance in Property Rating Systems: A Mandate-Strength Classification and Specification Method for Multi-State Catastrophe Mitigation Credits. International Journal of Computer Information Systems and Industrial Management Applications, 18(16s), 391–404. https://doi.org/10.70917/ijcisim-2026-4589

Issue

Section

Original Articles