10 Insurance Business Rules Examples with Rule Logic | Higson

What insurance business rules look like in production - 10 examples with concrete decision logic

Last month I sat with a Business Analyst at a $1.2B GWP regional carrier in the Midwest. Her question was specific: "I understand what a rules engine does in theory. What I need are concrete examples of insurance rules - what does a real eligibility rule look like in production, what conditions does it check, what outputs does it produce, how complex is each one in practice." Her team was evaluating BRMS vendors and the abstract "rules engines automate decisions" framing in vendor decks was not helping her scope the project. She needed examples she could compare to her actual rule library.

This article is the answer I gave her - 10 real insurance business rules examples covering P&C personal lines, commercial lines, life insurance, health insurance, and compliance. Each example includes the business problem it solves, the actual decision table structure, the input fields the rule evaluates, the outputs it produces, the regulatory context, and an honest complexity assessment (simple / medium / complex). In my experience, these are the patterns I see consistently in production BRMS deployments at mid-market insurance carriers.

The examples are written for Linda - the Business Analyst or Product Owner who will own the rule library after BRMS adoption. If you are at the evaluation stage, these examples help you scope what your year-3 rule library will look like. If you are at the implementation stage, the patterns help you avoid the most common rule-design mistakes. If you are at the operating stage, the examples are reference patterns to copy.

Skip to Section 3 if you want the examples directly. Section 13 covers implementation considerations from a BA perspective - sizing, organization, and testing patterns that determine whether the rule library stays maintainable as it grows.

Common insurance business rules examples

Insurance business rules examples fall into five operational categories with concrete patterns in each. Underwriting eligibility rules: state-by-state credit score thresholds, MVR limits, prior carrier history, vehicle restrictions (typical 40-60 rules across 4-8 decision tables per state). Premium rating rules: territory factors, tier placement, multi-policy discounts, surcharge stacking (8-15 separate tables per state, refreshed quarterly with regulatory rate filings). Claims rules: STP eligibility, severity-based assignment, fraud-flag triage (25-40 rules across 3-5 tables per line of business). Product availability rules: state-by-state product offering, line-of-business restrictions, channel-specific rules. Compliance rules: state-specific disclosure triggers (SR-22 in Texas), anti-discrimination notices, NAIC AI Bulletin requirements. The 10 examples below cover the highest-frequency patterns at mid-market $500M-$5B GWP carriers across P&C personal and commercial lines, life insurance, and health insurance - each with the actual decision-table structure, input fields, outputs, and complexity assessment.

In my experience, mid-market carriers typically run 5 000-10 000 active rules across 25-50 decision tables at production scale. The 10 examples below represent the most common patterns; a full rule library at this scale includes 200-400 instances of similar patterns across lines of business and states.

Example 1: Auto insurance state-by-state eligibility (P&C personal)

The first rule a new auto application hits at most carriers. Does this applicant qualify for our auto product in this state? In my experience, hard constraints that produce a binary approve / decline / refer-to-underwriter outcome before pricing rules run.

Business problem

Different states have different regulatory rules, market positioning preferences, and risk appetite. A Texas auto applicant with 4 prior at-fault accidents in 36 months should be declined per the carrier's risk policy; the same applicant might be referred to underwriter review in Florida. Without rule-based eligibility, the underwriting team manually evaluates every borderline application, creating quote-time delays.

Decision table structure

Input fields: state, credit score, MVR violations in 36 months, prior at-fault accidents in 36 months, prior carrier cancellation flag, drivers' license type, vehicle modifications flag. Outputs: eligibility decision (approve / decline / refer), reason code, audit trail. Typical table size: 40-60 rules across 4 conditions and 2-3 outputs per state. Multiply by 8-12 states = 320-720 rules just for auto eligibility.

Concrete rule example (Texas)

WHEN state = 'TX'
  AND credit_score < 580
  AND prior_at_fault_36mo >= 2
  AND mvr_violations_36mo >= 3
THEN eligibility = 'DECLINE'
  reason_code = 'TX_HIGH_RISK_COMBINATION'
  audit_log = current_timestamp

Complexity: Medium. Why: 4 conditions plus reason-code output plus multi-state replication. Linda the BA can author this in 30-45 minutes per state once she has the patterns down.

Example 2: Auto multi-policy discount stacking (P&C personal)

Discount math that customer service representatives historically got wrong via lookup tables. Multi-policy + multi-vehicle + safe driver + paperless + autopay discounts stack with specific caps per state.

Business problem

Carriers offer 6-10 customer-facing discounts (multi-policy bundle, multi-vehicle, safe driver, paperless billing, autopay, defensive driver course, good student, military, employer affinity, prior carrier loyalty). Each discount has eligibility rules, regulatory caps, and stacking limits. Without rule-based discount logic, agents quote inconsistent discounts and carriers face premium leakage.

Decision table structure

Input fields: policies in bundle, vehicles on policy, driver safety score, billing method, payment method, defensive driver course completion date, student GPA, military status, employer code, prior carrier tenure months. Outputs: applicable discounts list, discount percentage per type, total discount applied (capped), reason audit. Typical table size: 25-40 rules per state plus discount-cap rules.

Concrete rule example

WHEN policies_in_bundle >= 2
  AND vehicles_on_policy >= 2
  AND billing_method = 'PAPERLESS'
  AND payment_method = 'AUTOPAY'
THEN bundle_discount = 0.15
  multi_vehicle_discount = 0.08
  paperless_discount = 0.02
  autopay_discount = 0.03
  total_discount = LEAST(0.25, sum) // state cap 25%

Complexity: Medium-Complex. Why: discount stacking with regulatory caps creates conditional logic that varies by state. Linda authors per state with shared discount-eligibility sub-rules to avoid duplication.

Example 3: Premium tier placement (P&C personal)

After eligibility and discounts, the rate-tier placement rule determines which of the carrier's filed rate tiers the applicant receives. Standard / Preferred / Premier with different rate per coverage type.

Business problem

Carriers file multiple rate tiers with state DOIs. The placement rule determines which tier each applicant qualifies for based on risk indicators. Wrong tier placement = either lost business (preferred customer offered standard rates) or adverse selection (high-risk customer placed in preferred). Mid-market carriers typically have 3-5 tiers per state.

Decision table structure

Input fields: credit score, prior accidents 60 months, MVR violations 60 months, years with prior carrier, home ownership flag, completion of defensive driver course. Outputs: tier assignment (Premier / Preferred / Standard / Non-Standard), tier-specific rate factor. Typical table size: 30-50 rules per state with explicit tier-boundary conditions.

Concrete rule example

WHEN credit_score >= 720
  AND prior_at_fault_60mo = 0
  AND mvr_violations_60mo = 0
  AND prior_carrier_years >= 3
  AND home_ownership = TRUE
THEN tier = 'PREMIER'
  tier_rate_factor = 0.85
  audit_reason = 'top tier all 5 criteria met'

Complexity: Medium. Why: clear tier-boundary logic with documented audit reason. Linda authors quarterly with rate-filing cycle.

Example 4: Territory rating factors (P&C personal)

Territory is one of the most significant rating factors in auto and home insurance. Postal-code-level or county-level rating factors that reflect local risk patterns - theft rates, weather exposure, traffic density.

Business problem

Auto and homeowners pricing must reflect geographic risk variation. Hail-prone Oklahoma counties price differently from low-risk suburban Minnesota; high-theft urban Detroit zip codes price differently from rural Michigan. State DOI rate filings specify the territory factor structure; the BRMS rule library implements it. Mid-market carriers maintain 800-2 500 territory factors per state across coverage types.

Decision table structure

Input fields: state, postal code or county code, coverage type (collision / comprehensive / liability / property). Outputs: territory factor (multiplier 0.7-1.8 typical range), territory tier code, regulatory citation reference. Typical table size: 1 500-3 000 rules per state (each zip code mapped to a tier).

Concrete rule example

WHEN state = 'OK'
  AND postal_code IN (74005, 74006, 74008)
  AND coverage_type = 'COMPREHENSIVE'
THEN territory_factor = 1.45
  territory_tier = 'OK-HAIL-HIGH-3'
  regulatory_ref = 'OK_RATE_FILING_2026_Q1'

Complexity: Simple per rule but high volume. Why: each rule is straightforward but the count (1 500-3 000 per state) requires careful organization. Actuarial team owns the rule content; Linda owns the rule structure and update workflow.

Example 5: Life insurance underwriting eligibility (Life)

Life insurance underwriting combines medical history, age, occupation, and risk factors into eligibility and rate-class decisions. More complex than auto because of the medical underwriting layer.

Business problem

Life insurance carriers issue term, whole life, and universal life products with risk-class-based pricing. Underwriting evaluates medical history, occupation, hobbies, family history, smoking status, and financial information to assign one of 5-8 risk classes (Preferred Plus, Preferred, Standard Plus, Standard, Table 1-4). Rule-based eligibility handles standard cases; manual underwriter review handles non-standard.

Decision table structure

Input fields: age, gender (where allowed), height, weight (BMI calculated), tobacco use frequency, occupation code, hobby risk factors, family history coronary disease, family history cancer, prior life insurance application history. Outputs: risk class assignment, rate class factor, manual review flag, declined coverage flag. Typical table size: 80-150 rules per product (term life vs whole life require different rule sets).

Concrete rule example

WHEN age BETWEEN 30 AND 45
  AND bmi BETWEEN 19 AND 27
  AND tobacco_use_5yr = FALSE
  AND occupation_risk = 'LOW'
  AND family_coronary_before_60 = FALSE
THEN risk_class = 'PREFERRED_PLUS'
  rate_class_factor = 0.70
  manual_review = FALSE

Complexity: Complex. Why: many conditions, medical interpretation, and regulatory sensitivity. Underwriters own the medical thresholds; Linda manages the rule structure and audit trail. State DOI examinations on life products focus heavily on risk-class assignment audit trail.

Example 6: Health insurance prior authorization (Health)

Health payers' prior authorization rules determine which procedures require pre-approval before claims processing. CPT code + diagnosis combination + member benefit plan determine the answer.

Business problem

Health payers receive 30 000-100 000 prior authorization requests per month at mid-size scale. Automated rules approve 60-75% (clear medical-necessity criteria met), refer 20-35% to clinical review, and decline 5-10% (clear non-coverage). Without rule automation, every request requires manual clinical review - a multi-day turnaround that erodes provider satisfaction.

Decision table structure

Input fields: CPT procedure code, ICD-10 diagnosis code, member benefit plan ID, member age, prior authorization history for same procedure, network provider flag. Outputs: PA decision (approve / refer-clinical / decline), criteria met flag, member notification required flag. Typical table size: 800-2 000 rules per benefit plan (each plan has different coverage criteria).

Concrete rule example

WHEN procedure_cpt = '29881'  // knee arthroscopy
  AND diagnosis_icd10 LIKE 'M23.%'  // meniscal tear
  AND member_age BETWEEN 18 AND 64
  AND prior_pa_same_procedure_12mo = FALSE
  AND network_provider = TRUE
THEN pa_decision = 'APPROVE'
  criteria_met = 'MEDICAL_NECESSITY_DOCUMENTED'
  member_notification = NOT_REQUIRED
  audit_trail = current_timestamp + rule_version

Complexity: Complex. Why: medical coding requires clinical input, benefit plan variation multiplies rule count, HIPAA audit requirements add governance overhead. Clinical staff own the medical criteria; Linda manages the rule structure and HIPAA-compliant audit trail.

Example 7: Workers compensation class code rating (P&C commercial)

Workers comp pricing is class-code-based with state-specific rating modifiers. Higher-risk occupations (construction, manufacturing) price multiples higher than office-based occupations. Class code assignment is rule-based and audited frequently.

Business problem

Workers comp class codes (NCCI codes in most states, independent state codes in California, New York, others) determine the base rate for workers' compensation premium. Misclassification has regulatory consequences (state DOI fines) and competitive consequences (under-pricing high-risk classes leads to adverse selection). Rule-based class code validation catches misclassification before policy issuance.

Decision table structure

Input fields: state, primary business activity description (free text or NAICS code), secondary activities, employee count, payroll by activity, claims history by class. Outputs: validated class code or refer-to-classifier, base rate factor, experience modification eligibility flag, audit reasoning. Typical table size: 250-400 rules per state covering high-frequency class codes; long tail of rare codes handled by manual classification.

Concrete rule example

WHEN state = 'CA'
  AND naics_code IN ('236220', '236200')
  // residential & commercial building construction
  AND employee_count BETWEEN 5 AND 50
  AND payroll_in_class > 0.80 * total_payroll
THEN class_code = '5403'  // CA carpentry NOC
  base_rate_factor = 8.45  // CA WCIRB filed 2026
  exp_mod_eligible = (3yr_premium > $25,000)

Complexity: Complex. Why: state-specific code authorities (NCCI vs CA WCIRB vs NY etc.), payroll distribution logic, and regulatory audit requirements. Workers comp specialists own classification logic; Linda manages the rule structure with version control for state filing cycles.

Example 8: Producer commission calculation (cross-line)

Producer commissions are rule-heavy because every producer agreement is different. In my experience, override hierarchies, line-of-business splits, premium bands, bonus tiers - all rule-based decisions that fire on every policy and that producers will absolutely catch if miscalculated.

Business problem

Producers earn commissions on policies they sell. Commission rates vary by line of business, premium volume tier, override hierarchy (regional manager, agency principal), bonus eligibility, and special program terms. Calculation accuracy is contractual obligation - producer disputes over miscalculated commissions damage relationships and create operational overhead. Notus Finance ran this exact workload in Drools, migrated to Higson, and saw 100 000 calculations process in 8 seconds (vs 14 seconds in Drools).

Decision table structure

Input fields: producer ID, agreement type, line of business, premium amount, override producer ID(s), bonus program eligibility, year-to-date production, prior year production. Outputs: base commission percentage, override commission distribution, bonus eligibility flag, total commission per producer in chain. Typical table size: 100-300 rules across base commission schedules and override hierarchies.

Concrete rule example

WHEN producer_agreement_type = 'INDEPENDENT_AGENT'
  AND line_of_business = 'AUTO_PERSONAL'
  AND policy_year = 1  // new business
  AND premium_amount > 0
THEN base_commission_rate = 0.13  // 13% new business
  override_to_regional_mgr = 0.02
  override_to_agency_principal = 0.01
  producer_net = premium * 0.13
  regional_override = premium * 0.02
  audit_trail = current_timestamp

Complexity: Medium-Complex. Why: hierarchy logic is intricate but the math is straightforward. Compensation team owns the commission rate content; Linda manages the rule structure and producer agreement integration.

Example 9: Claims first-notice-of-loss routing (P&C claims)

Claims STP eligibility and routing decisions made within seconds of first notice of loss. Auto-approve simple claims, route by severity, fraud-flag triage - all rule-based with significant operational impact.

Business problem

Modern carriers process 40-70% of simple claims (low-severity, no injury, single-vehicle, no third-party liability) through straight-through processing with no human adjuster touch. Rule-based STP eligibility determines which claims qualify, which need adjuster review, and which need SIU (Special Investigations Unit) fraud review. STP cycle time (24-48 hours) vs adjuster cycle time (7-14 days) drives customer satisfaction differentiation.

Decision table structure

Input fields: claim type, estimated loss amount, injury flag, third-party liability flag, prior claims by insured, policy days since binding, attorney representation flag, fraud-indicator score (from ML model). Outputs: routing decision (STP-approve / adjuster-L1 / adjuster-L2 / SIU-review), priority level, estimated cycle time, customer notification required. Typical table size: 25-40 rules across 3-5 decision tables per line of business.

Concrete rule example

WHEN claim_type = 'AUTO_COLLISION'
  AND estimated_loss < $5,000
  AND injury_flag = FALSE
  AND third_party_liability = FALSE
  AND policy_days_since_binding > 30
  AND attorney_representation = FALSE
  AND fraud_score < 0.3
THEN routing = 'STP_APPROVE'
  priority = 'STANDARD'
  estimated_cycle_time_hrs = 36
  customer_notification = 'TEXT_AND_EMAIL'

Complexity: Medium. Why: clear claim-attribute logic but fraud-score input from ML model adds hybrid complexity (see our ML governance article for the BRMS-as-guardrail pattern). Claims operations team owns the STP threshold logic; Linda manages the rule structure and ML model integration.

Example 10: NAIC compliance disclosure triggers (Compliance)

Regulatory disclosures triggered by specific policy events. SR-22 filings in Texas, anti-discrimination notices in California, NAIC AI Bulletin documentation requirements - all rule-driven.

Business problem

State Departments of Insurance require carriers to issue specific disclosures and notifications when triggering events occur. SR-22 financial responsibility filing in Texas after certain license events. Anti-discrimination notice in California when specific risk factors influence pricing. NAIC AI Bulletin documentation when AI/ML models contribute to adverse consumer decisions (covered in our ML governance article). Without rule-based disclosure triggering, carriers face state DOI examination findings.

Decision table structure

Input fields: state, policy event type (binding / renewal / cancellation / non-renewal / claims-driven), trigger event details, AI/ML model use flag, model version (if applicable). Outputs: required disclosures list, filing required flag (SR-22, FR-44, etc.), notification deadline, audit trail with regulatory citation. Typical table size: 60-100 rules per state covering the most common disclosure triggers.

Concrete rule example

WHEN state = 'TX'
  AND policy_event = 'BINDING'
  AND license_status = 'SUSPENDED_PRIOR_24MO'
  AND license_reinstatement_requires_sr22 = TRUE
THEN required_filing = 'SR-22'
  filing_deadline_days = 7
  filing_authority = 'TX_DPS'
  customer_notification = 'CERTIFIED_MAIL'
  audit_trail = 'TX_TRANSPORTATION_CODE_601.231'

Complexity: Complex. Why: state-specific regulatory citation requirements and audit trail expectations. Compliance team owns the regulatory content; Linda manages the rule structure with each state DOI bulletin update.

Implementation considerations from a Business Analyst perspective

The 10 examples above are the rule patterns. In my experience, building a maintainable rule library requires implementation discipline beyond the individual rule design. Patterns I recommend based on 200+ insurance implementations at Decerto.

Rule library organization

Group rules into focused decision tables (8-12 conditions, 30-150 rules each) rather than monolithic 2 000-row matrices. Namespace by business domain - underwriting in one namespace, pricing in another, claims in a third. Version each decision table independently so a change to the underwriting eligibility table does not require redeploying the unchanged claims-routing table. Linda the BA owns the underwriting and pricing rules; actuarial team owns rating factors; claims operations owns claims routing; compliance owns disclosure triggers.

Testing patterns

Every business rule has a corresponding test fixture - input data plus expected output - kept in version control. Shadow-mode validation before production cutover: run the new rule set in parallel with the existing rule set for 4-8 weeks, compare outputs, reconcile differences. In my experience, this pattern catches edge-case rule mismatches before they hit customers, and I recommend it for every non-trivial rule library migration. The fixtures become regression tests that fire on every subsequent rule change.

Audit trail and governance

Every rule execution produces an audit record - rule version that fired, input values, output decision, timestamp, optional human-readable audit reason. The NAIC Model Bulletin on AI Use in Insurance and state DOI examinations both expect this audit trail. Modern BRMS like Higson produce this audit log natively; building it from scratch on top of bare rule engines (Drools alone) requires significant engineering effort.

Rule change cycle time

Mid-market carriers using BRMS typically achieve 24-72 hour rule-change cycles vs the 4-12 week cycles of carriers with rules hardcoded in application code. The 30-50x improvement is what justifies BRMS investment. The discipline that produces this cycle time: per-rule version control, automated test execution on rule change, BA self-service authoring (Higson Studio rather than developer-required), and shadow-mode validation before production cutover.

FAQ

What are examples of business rules in insurance?

Five operational categories cover most insurance business rules. (1) Underwriting eligibility: state-by-state credit/MVR/claims history rules producing approve/decline/refer outcomes. (2) Premium rating: territory factors, tier placement, multi-policy discount stacking. (3) Claims rules: STP eligibility, severity-based routing, fraud-flag triage. (4) Product availability: state-by-state offerings, channel restrictions. (5) Compliance triggers: SR-22 filings, anti-discrimination notices, NAIC AI Bulletin documentation. Mid-market carriers typically run 5 000-10 000 active rules across 25-50 decision tables at production scale.

What does an auto insurance eligibility rule look like?

Auto eligibility rules check state, credit score, MVR violations 36 months, prior at-fault accidents 36 months, prior carrier cancellation flag against state-specific thresholds. Outputs: approve / decline / refer-to-underwriter plus reason code plus audit timestamp. Typical Texas pattern: state='TX' AND credit_score < 580 AND prior_at_fault_36mo >= 2 AND mvr_violations_36mo >= 3 → DECLINE with reason code TX_HIGH_RISK_COMBINATION. Each state has 40-60 such rules; multi-state carriers replicate across 8-12 states (320-720 eligibility rules total).

How many business rules does a mid-market insurance carrier have?

Mid-market $500M-$5B GWP insurance carriers typically run 5 000-10 000 active business rules across 25-50 decision tables at production scale. The largest mid-market deployment I have seen sat at 11 800 rules across 47 tables. Breakdown: underwriting eligibility 300-700 rules across 8-12 states, premium rating 1 500-3 500 territory factors plus rating-tier rules, claims routing 100-150 rules, product availability 200-400 rules, compliance disclosure triggers 500-1 000 rules, producer commissions 100-300 rules. The rest sits in line-of-business-specific operational rules.

What are workers compensation business rules examples?

Workers comp rules center on class code validation and rating. State-specific code authorities (NCCI codes in most states, CA WCIRB independent codes, NY independent codes). Example: state='CA' AND naics_code IN ('236220','236200') AND employee_count BETWEEN 5 AND 50 AND payroll_in_class > 0.80*total_payroll → class_code='5403' (CA carpentry NOC) with base_rate_factor=8.45. Plus experience modification eligibility (3yr_premium > $25,000). Misclassification triggers state DOI fines and produces adverse selection - rule-based validation catches errors before issuance.

What are life insurance underwriting rule examples?

Life insurance underwriting rules assign risk classes (Preferred Plus / Preferred / Standard Plus / Standard / Table 1-4) based on medical history, BMI, tobacco use, occupation, family history, and prior application history. Example: age BETWEEN 30 AND 45 AND BMI BETWEEN 19 AND 27 AND tobacco_use_5yr=FALSE AND occupation_risk='LOW' AND family_coronary_before_60=FALSE → risk_class='PREFERRED_PLUS' with rate_class_factor=0.70 and manual_review=FALSE. Each life product typically has 80-150 rules; term life and whole life require different rule sets due to underwriting depth differences.

What are health insurance prior authorization rule examples?

Health prior authorization rules combine CPT procedure codes, ICD-10 diagnosis codes, member benefit plan, member age, and prior authorization history to produce approve / refer-clinical / decline decisions. Example: procedure_cpt='29881' (knee arthroscopy) AND diagnosis_icd10 LIKE 'M23.%' (meniscal tear) AND member_age BETWEEN 18 AND 64 AND prior_pa_same_procedure_12mo=FALSE AND network_provider=TRUE → pa_decision='APPROVE' with criteria_met='MEDICAL_NECESSITY_DOCUMENTED'. Each benefit plan typically has 800-2 000 rules; mid-size health payers run 30 000-100 000 PA requests/month.

What are claims processing business rules examples?

Claims rules center on STP routing, severity-based assignment, and fraud-flag triage. Example STP-approve pattern: claim_type='AUTO_COLLISION' AND estimated_loss < $5,000 AND injury_flag=FALSE AND third_party_liability=FALSE AND policy_days_since_binding > 30 AND attorney_representation=FALSE AND fraud_score < 0.3 → routing='STP_APPROVE' with cycle_time=36 hours. Modern carriers process 40-70% of simple claims through STP; the rest route to adjuster L1/L2 or SIU based on severity and fraud indicators. Typical 25-40 rules across 3-5 decision tables per line of business.

How long does it take a Business Analyst to author insurance rules?

Authoring time depends on rule complexity. Simple rules (single-condition territory factors, basic eligibility checks): 10-20 minutes each. Medium rules (multi-condition tier placement, discount stacking): 30-45 minutes each. Complex rules (life underwriting, workers comp classification, claims STP with ML model integration): 1-3 hours each including test fixtures. Mid-market BA productivity at modern BRMS like Higson Studio: 8-15 rules per day for routine work, 3-5 rules per day for complex regulatory or actuarial work. Initial authoring of a new product line typically takes 4-8 weeks of BA time.

Related reading

• Common Business Rules Examples: 5-Type Framework Across Industries - broader framework covering insurance, banking, healthcare, retail.

• Decision Tables in Higson Studio - the no-code authoring layer Linda uses to build the examples above.

• Operational Decisions: The Backbone of Every Business - the decision-category framing for these example patterns.

• Rules Engine Scalability: 10 000+ Rules at Sub-Millisecond Latency - operating rule libraries at this scale.

• How to Choose a Rules Engine: 9-Criteria Buying Checklist - evaluation methodology for selecting BRMS.

• Machine Learning Problems: ML Governance & BRMS Guardrail - hybrid pattern for Example 9 fraud-score integration.

• Simplifying Insurance Claims Management with Rules Engines - Example 9 in claims workflow context.

Talk to Higson

The 10 examples above are the patterns I show carriers during BRMS evaluation conversations. They make abstract "rules engines automate decisions" framing concrete - here is what your year-3 rule library actually looks like in production. If you are at the evaluation stage, these examples help you scope what your team will own; if you are implementing, the patterns help you avoid common rule-design mistakes; if you are operating, the examples are reference patterns to copy.

Higson is built for mid-market insurance carriers $500M-$5B GWP with 5 000-15 000 active rules across underwriting, rating, claims, compliance, and producer compensation. Higson Studio is the no-code authoring tool Linda uses to build and maintain these rule patterns; Higson Runtime executes them at 0.23 ms P50 latency / 9 000 req/s sustained. We have 200+ insurance implementations via Decerto since 2005 including Allianz, BNP Paribas Cardif, InterRisk (VIG Group), Warta (HDI/Talanx), and Nationale Nederlanden. We are not the right answer for sub-100-rule deployments (BRMS overhead does not pay back), 50 000+ req/s enterprise scale (InRule or FICO Blaze enterprise tiers fit better), or .NET-end-to-end shops (InRule wins). Where we do fit, Higson moves from PoC to production cutover in 3-6 months.

If you would like to see Higson Studio with sample rule library covering the examples above, I would be happy to walk through it with your BA and architect team.

Three ways to start:

• Try Higson on AWS Marketplace at $0.63 / hour - 15 minutes from subscription to first decision execution. Sample insurance rule set with auto eligibility + tier placement + claims STP patterns included.

• Schedule a 30-minute Higson Studio demo - we will walk through the rule patterns above in our authoring tool with your BA team.

• Download the BRE Comparison Guide - 12 vendors compared on authoring capability for these rule patterns.

Citations

1. NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers (December 2023, updated 2024-2025) - rule audit trail requirements for AI/ML integration. https://content.naic.org/sites/default/files/inline-files/2023-12-4 Model Bulletin_Adopted_0.pdf

2. OMG Decision Model and Notation (DMN) Specification - decision table format that most rule patterns above implement. https://www.omg.org/dmn/

3. ACORD Standards - insurance data model alignment for rule input field definitions. https://www.acord.org/

4. NCCI (National Council on Compensation Insurance) - workers comp class code authority for most states. https://www.ncci.com/

5. California Workers' Compensation Insurance Rating Bureau (WCIRB) - CA-specific class code authority. https://www.wcirb.com/

6. NAIC Insurance Holding Company System Regulatory Act - regulatory framework for state DOI compliance disclosures. https://content.naic.org/

7. Notus Finance / Higson case study (commission calculation migration from Drools, 100K calculations 14 sec to 8 sec) - https://www.higson.io/case-study/

8. Forrester Wave: Digital Decisioning Platforms (Q1 2026) - mid-market BRMS landscape including authoring capability assessment.