How a Rules Engine Helps Insurers Adapt to Regulatory Change

Regulatory Agility Has Become a Board-Level Mandate

The regulatory rulebook for insurance now changes faster than most carriers’ systems can absorb. For the Chief Information Officer, every new regulation surfaces as an architecture question: can our systems implement this change without a multi-month development cycle? For the Chief Underwriting Officer, the same regulation surfaces as a market question: can we adjust underwriting and pricing before the change costs us margin or exposes us to a compliance finding? When the answer to either question is no, the carrier is not facing a compliance inconvenience. It is facing a structural disadvantage.

I have led Higson at Decerto for over two decades, working with carriers across the EU and US through successive waves of regulatory change. The pattern I observe consistently is that regulatory agility is no longer an IT housekeeping concern delegated down the organization. It is a board-level mandate that lands squarely on two executives: the CIO, who owns whether the systems can change fast enough, and the CUO, who owns whether the business can respond to the changed rules in time to matter. This article is written for those two roles and the decision they share.

The thesis is straightforward. A business rules engine externalizes regulatory and underwriting logic out of core application code, so that the carrier can update rules in one place, in hours rather than months, without rewriting software or waiting on an engineering release cycle. For the CIO, this removes the architecture bottleneck. For the CUO, it restores the ability to respond to regulatory shifts at market speed. Below, I will quantify the regulatory pressure, explain how the rules engine architecture addresses it, and show what the operating model looks like when it works.

Executive Summary

Regulatory velocity is rising. US insurance saw over 1,700 state rule changes in the first half of 2023 alone, up 8% year-over-year, and the NAIC Model Bulletin on AI Systems is now adopted in 24 states as the de facto national standard.

The bottleneck is architectural, not regulatory. Carriers with logic hardcoded in core systems take months to implement rule changes; carriers running a rules-engine abstraction layer take hours.

The CIO owns the architecture decision; the CUO owns the response-speed outcome. Both are accountable to the board for regulatory agility.

Business-user control is the operational unlock. When compliance and underwriting teams can author and adjust rules directly through no-code interfaces, the carrier responds to regulatory change without an IT dependency.

The 2026 examination apparatus is real. The NAIC’s AI Systems Evaluation Tool pilot (12 states, January-September 2026) operationalizes market conduct exams of AI governance - making auditable, documented decision logic a near-term necessity.

The Scale of Regulatory Pressure in 2026

Insurance regulation is not becoming simpler, and the rate of change has accelerated materially over the past five years. The numbers establish the scale of the challenge for both the CIO and the CUO.

In the first half of 2023, the United States recorded more than 1,700 state insurance rule changes - an 8% increase over the prior year. The structural reason is well known: US insurance is regulated state by state, with each of the 50 states plus the District of Columbia maintaining its own statutes, filing requirements, and supervisory practices. For brokers, the direct cost of compliance has risen 40% since 2019, now consuming 8.1% of fees and commissions. Compliance error rates remain a material exposure - some studies of underwriting files have found error rates as high as 53%, and the average annual cost of compliance for insurance businesses runs around $5.5 million.

The AI governance wave is the defining regulatory development of the current period. The NAIC adopted its Model Bulletin on the Use of Artificial Intelligence Systems by Insurers in December 2023; as of early 2026 it has been adopted in approximately 24 states, with four additional states adopting substantially similar guidance - making it the de facto national standard. The Bulletin requires a documented AI Systems (AIS) Program aligned with the FACTS principles (Fairness, Accountability, Compliance, Transparency, Security), including governance documentation, bias and discrimination testing, and third-party model oversight.

The examination apparatus is now operational. In January 2026 the NAIC launched a multistate pilot of its AI Systems Evaluation Tool, running through September 2026, with twelve states participating - Colorado, Maryland, Louisiana, Virginia, Connecticut, Pennsylvania, Wisconsin, Florida, Rhode Island, Iowa, Vermont, and California. The Tool gives examiners a structured framework for reviewing insurer AI governance during market conduct examinations. State-specific statutes add further layers: the Colorado AI Act (SB 24-205) takes effect in 2026, and Virginia’s HB 2094 closely mirrors it. For carriers writing business across a national footprint, the practical effect is that AI governance compliance is no longer optional or distant - it is being examined now.

Beyond US AI governance, carriers face parallel pressures: the EU’s Digital Operational Resilience Act (DORA) took effect in January 2025, the EU AI Act carries an August 2026 compliance deadline for high-risk applications including insurance, and the UK’s FCA Consumer Duty rules continue to reshape conduct expectations post-Brexit. For a CIO managing a multi-jurisdiction carrier, this is a convergence of regulatory regimes, each demanding the ability to change decision logic quickly and prove the change was applied consistently.

The Four Regulatory Pressures on the CIO/CUO Agenda

Four categories of regulatory pressure dominate the executive agenda in 2026, each with a distinct CIO architecture implication and CUO business implication:

Consumer protection and fair-treatment rules. Requirements for clearer disclosure and demonstrably fair practices have expanded, and they vary by jurisdiction. CUO implication: underwriting and pricing logic must be jurisdiction-specific and defensibly non-discriminatory. CIO implication: the systems must apply jurisdiction-variant rules consistently and produce the audit trail to prove it.

AI/ML governance. The NAIC Model Bulletin and state AI acts require explainability, bias testing, and documented governance for AI-influenced decisions. CUO implication: any model used in underwriting or pricing must be explainable on demand. CIO implication: the architecture must store per-decision explanations (SHAP or LIME analysis) retrievable for examination.

Data privacy. GDPR in Europe, a growing patchwork of US state privacy laws, and sector-specific health-data rules all constrain how customer data flows through decision systems. CIO implication: data handling within the decision layer must be governable and auditable.

Climate risk disclosure. Insurers increasingly must demonstrate how they assess and manage climate-related risk, affecting underwriting and investment. CUO implication: climate factors must be incorporable into underwriting rules quickly as disclosure expectations evolve.

The common thread across all four is the requirement for systems that can change quickly without disrupting core operations, and that produce a defensible record of every decision. This is precisely the capability a business rules engine provides.

What a Rules Engine Is, in Executive Terms

A rules engine - also called a business rules engine (BRE) or business rules management system (BRMS) - is software that lets a carrier define, execute, and maintain its business and regulatory logic separately from its core application code. The architectural principle is decoupling: the rules that govern decisions live in a centralized, governable repository rather than embedded throughout the codebase.

For the CIO, this decoupling is the core value. When regulatory logic is hardcoded into the policy administration system, the rating engine, and the claims platform separately, every regulatory change becomes a multi-system development project. When that logic is externalized into a rules engine, the change happens once, in one place, and propagates consistently. The carrier preserves its existing core-system investments while gaining the ability to change decision logic at regulatory speed.

Mechanically, a rules engine evaluates conditional logic - structured "if condition, then action" rules - against incoming data and returns a decision. When a new claim arrives, the engine checks it against the current rule set and routes it accordingly; a claim with fraud indicators is automatically flagged for review, a straightforward claim proceeds through automated processing. The same pattern applies to underwriting eligibility, pricing, disclosure requirements, and compliance checks. Critically, every evaluation produces a logged, versioned record - the audit trail that the 2026 examination apparatus now expects.

How the Rules Engine Architecture Delivers Regulatory Agility

Centralized rule governance

A rules engine maintains a single source of truth for regulatory and business logic. When a regulation changes, the compliance team updates the rule once; the engine applies the updated logic consistently across every decision and every system that calls it. There is no need to rewrite software in multiple platforms, and no risk of the same rule being implemented inconsistently across channels. For the CIO, this collapses the change-management surface area from many systems to one.

Decision automation with a defensible audit trail

The engine automates routine decisions using predefined rules, reducing manual effort and the human error that drives compliance findings. Standard cases process automatically and consistently; only exceptions route to human review. Each decision is logged with the rule version applied, producing the comprehensive audit record that market conduct examinations - and the NAIC AI Systems Evaluation Tool - now require. For the CUO, this means the underwriting operation can demonstrate, on demand, that every decision applied the correct rule at the correct time.

Speed: from months to hours

The headline operational benefit is response time. A regulatory change that would take weeks or months to implement in hardcoded systems can be implemented in hours in a rules engine. When the Colorado AI Act takes effect, or a state updates a disclosure requirement, the compliance team authors the rule change, validates it in a sandbox against the in-force book, and deploys it - without an engineering release cycle. This is the difference between leading a regulatory transition and scrambling to catch up to it.

Consistency that reduces compliance exposure

Rules engines apply regulatory requirements identically every time, eliminating the interpretation variance and manual error that generate compliance problems. When regulations change, the updated logic applies immediately to all new transactions, with no transition-period gap during which inconsistent application creates exposure. Carriers that move to rules-engine governance consistently report material reductions in compliance-related errors.

Where Regulatory Agility Pays Off Operationally

Claims processing and fraud detection. The engine checks claims against current regulations and policy terms automatically, routing by complexity and risk while maintaining claims-handling compliance. It flags potentially fraudulent claims against known indicators, supporting anti-fraud regulatory requirements. For the CUO, this means claims operations stay compliant as handling rules change, without manual re-training cycles.

Underwriting and risk assessment. The engine applies risk-assessment, pricing, and policy-term rules consistently, supporting fair-treatment and non-discrimination compliance - a direct CUO concern under the NAIC Bulletin. It flags applications requiring regulatory review and lets the carrier adjust pricing models as regulations change without disrupting the underwriting workflow.

Customer service and personalization. The engine ensures that offers and communications meet regulatory requirements while supporting personalization, giving service teams real-time, compliant guidance on what they can offer based on current rules and customer context.

Integration and the Business-User Operating Model

A modern rules engine connects to existing systems through APIs and web services, preserving current technology investments while adding regulatory speed. For the CIO evaluating the architecture, two integration characteristics matter most.

No-code business-user control. Modern engines let business users - compliance officers, underwriters, business analysts - author and adjust rules through visual, no-code interfaces. This is the operational unlock: when a new disclosure rule arrives, the compliance officer implements it directly rather than queuing an IT request. The person who actually does this work at most carriers is the senior business analyst - the Linda persona in our internal language - a domain expert with deep regulatory knowledge and no coding background. Empowering that role to author rules directly is what converts regulatory agility from an aspiration into an operating capability.

Real-time and historical data integration. The engine draws on both historical context and real-time data, ensuring each decision reflects the latest rules while accounting for relevant history. For the CIO, this means the decision layer operates on current data without bespoke integration work for each regulatory change.

Regulatory Agility in Practice

Industry example: Hollard Insurance

Hollard Insurance faced difficulty keeping pace with multiple regulations because rule management was fragmented across systems. The carrier implemented a business rules engine to centralize rule governance and automate decision processes, focusing on consolidating rules into a single repository, adapting quickly to new regulatory requirements, and improving operational efficiency through automation. The outcome was stronger regulatory compliance, greater efficiency, and better deployment of staff - personnel shifted from routine compliance checks to higher-value work. The Hollard example illustrates the general pattern: centralization plus automation equals regulatory agility.

Higson reference deployments

Across Higson deployments, the same architectural pattern - centralized rule governance, no-code authoring, sub-millisecond execution, state-by-state rule isolation - has delivered measurable regulatory-response improvement:

Allianz Poland - a twenty-year partnership consolidating product configuration across 12+ lines, with model deployment dropping from 6-8 weeks per rate plan iteration to under one week. For a CIO, that is the architecture bottleneck removed; for a CUO, it is regulatory response measured in days rather than quarters.

InterRisk (VIG Group) - a Digital Sales Platform Transformation that reduced regional filing preparation time by approximately 70%, with rule versioning maintained independently per region. The same per-region isolation that handles regulatory filing variation handles AI-governance variation across states.

BNP Paribas Cardif — the publicly documented case, centralizing decision logic across banking-distributed insurance products and multiple geographies with consistent governance.

Higson executes these decisions at 0.23ms per rule with sustained throughput of 9,000 requests per second per node, and handles the 51-state regulatory reality through rule isolation per state - one base ruleset plus state-specific override layers that compose at runtime. This is the architectural foundation that makes the NAIC AI Bulletin’s 24-state patchwork governable rather than overwhelming.

Implementation Considerations for the CIO/CUO

Selecting and deploying a rules engine is a strategic decision with both architecture and business dimensions. Three considerations matter most for the CIO/CUO partnership:

1. Fit to insurance operations. Assess rule complexity, the existing systems requiring integration, and - critically - how readily non-technical users can author and adjust rules. The easier business-user authoring is, the faster the carrier adapts to regulatory change. For US carriers, native handling of state-by-state variation is a key selection criterion.

2. Performance at scale. Insurance operations process high transaction volumes against large rule sets. The engine must execute at production latency - sub-millisecond per decision for real-time quoting and underwriting - without degradation under load.

3. Change governance. Establish a structured process for how rules are changed: testing, approval workflow, version control, and audit-record retention. This preserves organizational control while enabling speed, and it produces the documentation the NAIC AI Systems Evaluation Tool examines.

Implementation is an organizational change, not only a technology deployment. Both technical teams and business users require training - technical teams on integration and maintenance, business users on rule authoring and governance. The carriers that succeed treat the rules engine as a shared CIO/CUO capability rather than an IT project.

The Next Step for Regulatory Agility

Rules engines change how a carrier handles regulatory change, but the implementation partner matters as much as the technology. Higson builds rules engine solutions specifically for insurance carriers facing complex, multi-jurisdiction compliance - a centralized rule repository where regulatory requirements are updated once and applied everywhere, reducing implementation time from weeks to hours.

The Higson approach focuses on three executive priorities: compliance automation that reduces manual intervention and human error while maintaining comprehensive audit trails; business-user empowerment that lets compliance and underwriting staff modify decision logic without IT dependencies; and system integration that connects with existing core systems without disrupting them. For the CIO, this is architecture agility without core-system replacement. For the CUO, it is the ability to respond to regulatory change at market speed while maintaining a defensible compliance record.

Book a 30-minute demo (/contact?source=blog_p3_regulatory_changes) - we walk through centralized rule governance, the no-code authoring workflow your compliance team would use, 51-state rule isolation, and how a regulatory change deploys in hours instead of months. The session is built for CIO and CUO evaluation, not a generic product tour.

Or try Higson on AWS Marketplace at $0.63/hour for the PoC tier - see how the decision layer handles your own regulatory logic before any procurement conversation.

Key Sources

• NAIC Model Bulletin on the Use of AI Systems by Insurers (Dec 2023; ~24 states adopted as of early 2026)

• NAIC AI Systems Evaluation Tool multistate pilot (Jan-Sep 2026, 12 states)

• NAIC AI Issue Brief (March 2026)

• Colorado AI Act (SB 24-205, effective 2026); Virginia HB 2094

• EU Digital Operational Resilience Act (DORA, effective January 2025)

• EU AI Act (high-risk compliance deadline August 2026)

• UK FCA Consumer Duty rules

• US state insurance rule change volume (1,700+ H1 2023, +8% YoY); broker compliance cost (+40% since 2019, 8.1% of fees)

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Related Reading

• What Is a Rules Engine - The Complete Guide - the BRMS foundation for regulatory agility.

• The 51-Regulator Reality - the state-by-state structure behind every regulatory change.

• When Legacy Systems Meet Nuclear Verdicts - the litigation-driven side of jurisdiction-specific response.

• Insurance Software Solutions - where the rules engine sits in the software stack.

• Navigating IT Dependency in Product Management - the IT-dependency problem this article solves.

• Insurance Product Management

• Insurance Underwriting Automation - Complete Guide - the CUO’s underwriting-side agility.