Forward to: Actuarial Team

Actuarial Analytics
Workflows

Ten agent workflows for the Actuarial Team — risk modeling enrichment with digital footprint variables, catastrophe exposure analysis, portfolio segmentation by web maturity, pricing intelligence from competitor domains, loss development pattern correlation, reserve adequacy monitoring, rate filing support, experience modification factors, predictive model feature engineering, and actuarial reporting automation.

1Risk Model Feature Engineering

AI agent extracts predictive features from domain intelligence data — domain age, page count, PageRank, industry classification, and digital maturity scores — to enhance actuarial pricing models with web-derived risk variables.

1
Extract Web-Based Risk Variables
/security /compliance /careers /about OpenPageRank Domain Ages IAB Categories
FEATURE ENGINEERING — DOMAIN INTELLIGENCE VARIABLES ════════════════════════════════════════════════════════ NEW PREDICTIVE FEATURES (12,400 policy sample): F1: Digital Maturity Score (0-100) Components: Pages present/20, PageRank, domain age, content quality Gini coefficient: 0.34 (strong predictor) Loss ratio — High maturity (>70): 38% | Low (<30): 72% F2: Safety Culture Index Components: /security presence, /compliance, /sustainability Gini: 0.28 | Frequency reduction when present: 41% F3: Business Stability Score Components: Domain age, /careers trend, /press sentiment, PageRank trend Gini: 0.31 | Predicts non-renewal and adverse development F4: Revenue Proxy Index Components: /careers count, /investors presence, /about employee mentions Correlation with actual revenue: r=0.74 Useful for premium basis validation MODEL IMPROVEMENT: Combined loss ratio Gini: 0.52 (was 0.38 without web features) 37% improvement in model discrimination
2
Validate Model Performance
Domain Signal
Digital Maturity Score — The single most predictive web-derived feature. Companies with scores above 70 show loss ratios of 38% vs. 72% for those below 30. The feature captures operational discipline, safety investment, and business maturity in a single metric derived from page presence, PageRank, and domain age.
Digital maturity alone predicts 34% of loss ratio variance
Sector Signal
GLM Enhancement Results — Adding 4 domain intelligence features to the existing 23-variable GLM improved out-of-sample prediction by 18.4%. The digital maturity score alone contributed 11.2% of the improvement, making it the 3rd most predictive variable in the entire model behind industry class and years in business.
18.4% improvement in pricing accuracy — validated on out-of-sample data
3
Output: Feature Engineering Results

RISK MODEL FEATURE ENGINEERING — Q1 2026

NEW PREDICTIVE FEATURES DEVELOPED: ══════════════════════════════════════════════ Features created: 4 | Sample size: 12,400 policies Model Gini improvement: 0.38 → 0.52 (+37%) Out-of-sample validation: +18.4% prediction accuracy TOP FEATURES BY PREDICTIVE POWER: F1 Digital Maturity: Gini 0.34 (3rd in full model) F3 Business Stability: Gini 0.31 F2 Safety Culture: Gini 0.28 F4 Revenue Proxy: r=0.74 with actual revenue Enrichment: PageRank, Domain Ages, IAB, Web Filtering, /security, /compliance Recommendation: Deploy in production GLM for Q3 rate cycle

2Catastrophe Exposure Analysis

AI agent enriches catastrophe models by mapping insured business locations, operational footprints, and supply chain dependencies through domain intelligence — improving probable maximum loss estimates.

1
Map Geographic Exposure Concentrations
/contact /about Countries IAB Categories
CAT EXPOSURE ENRICHMENT — HURRICANE MODEL ════════════════════════════════════════════════════════ UNDISCLOSED LOCATIONS DISCOVERED: Policies analyzed: 2,847 commercial property accounts /contact page analysis found: 342 additional locations not on policy schedules Estimated uninsured TIV: $1.2B CONCENTRATION ALERT — Miami-Dade County: Known insured TIV: $4.8B Web-discovered additional locations: +$340M Actual exposure: $5.14B (7% higher than modeled) PML 250-year: $1.89B (was $1.76B) SUPPLY CHAIN DEPENDENCIES: /about + /partners analysis across portfolio: 87 insureds depend on portofmiami.com operations Combined BI exposure if port closes: $890M Not captured in property CAT models
2
Assess CAT Model Impact
Domain Signal
portofmiami.com — /about reveals the port handles $32B in cargo annually. /partners lists 87 logistics companies that are also our insureds. A single hurricane shutting down the port could trigger $890M in business interruption claims across our portfolio — a correlated loss event not captured in traditional property CAT models.
HIDDEN AGGREGATION — $890M supply chain BI exposure not in CAT model
Sector Signal
Southeast US Concentration — Web intelligence discovered 342 locations not on policy schedules, adding $1.2B in unmodeled TIV to hurricane zone. Miami-Dade actual exposure is 7% higher than modeled. Combined with supply chain dependencies, total unmodeled CAT exposure in Southeast US is $2.1B — requiring immediate reinsurance program adjustment.
PML UNDERESTIMATED — $2.1B in unmodeled CAT exposure discovered
3
Output: CAT Exposure Report

CATASTROPHE EXPOSURE ANALYSIS — Q1 2026

CAT MODEL ENRICHMENT RESULTS: ══════════════════════════════════════════════ Undisclosed locations found: 342 (from /contact analysis) Additional TIV discovered: $1.2B Supply chain BI exposure: $890M (port dependency) Total unmodeled exposure: $2.1B REVISED PML ESTIMATES: 250-year hurricane PML: $1.89B (was $1.76B, +7%) Including BI dependencies: $2.78B ACTIONS REQUIRED: 1. Update policy schedules for 342 undisclosed locations 2. Purchase additional $130M CAT reinsurance 3. Add supply chain BI to CAT model assumptions

3Portfolio Segmentation

AI agent segments the insurance portfolio using domain intelligence clusters — grouping insureds by digital maturity, industry sub-segments, and risk characteristics to enable more granular pricing and reserving.

1
Create Digital Maturity Segments
/about /careers /security OpenPageRank Domain Ages IAB Categories
PORTFOLIO SEGMENTATION — 8,400 COMMERCIAL ACCOUNTS ════════════════════════════════════════════════════════ SEGMENT A: Digital Leaders (18% of book) Profile: 15+ pages, PR >5, age >10yr, /security + /compliance Loss ratio: 34% | Avg premium: $127K Retention: 94% | Growth rate: +8%/yr SEGMENT B: Established Standard (42% of book) Profile: 8-14 pages, PR 2-5, age 5-10yr Loss ratio: 48% | Avg premium: $67K Retention: 87% | Growth rate: +3%/yr SEGMENT C: Emerging Businesses (28% of book) Profile: 4-7 pages, PR 1-2, age 2-5yr Loss ratio: 62% | Avg premium: $34K Retention: 79% | Growth rate: +12%/yr SEGMENT D: Minimal Web Presence (12% of book) Profile: 0-3 pages, PR <1, age <2yr Loss ratio: 84% | Avg premium: $18K Retention: 61% | Growth rate: -2%/yr FINDING: Segment D represents 12% of premium but 22% of losses
2
Analyze Segment Performance Drivers
Domain Signal
Segment D — Minimal Web Presence — 1,008 accounts with 0-3 pages, PageRank below 1, and domain age under 2 years. These accounts show 84% loss ratio and 61% retention. The thin digital footprint correlates with business inexperience, inadequate safety programs, and higher operational risk. They contribute 12% of premium but 22% of incurred losses.
ADVERSE SELECTION — Segment D is 12% of premium but 22% of losses
Sector Signal
Digital Maturity vs. Loss Performance — Across all 8,400 accounts, the correlation between digital maturity score and loss ratio is r=-0.68. Each 10-point increase in digital maturity corresponds to a 4.2-point improvement in loss ratio. Segment A accounts (digital leaders) outperform Segment D by 50 loss ratio points — a transformative underwriting signal.
STRONG PREDICTOR — Each 10pt digital maturity increase = 4.2pt better loss ratio
3
Output: Portfolio Segmentation Report

PORTFOLIO SEGMENTATION — Q1 2026

SEGMENT PERFORMANCE: ══════════════════════════════════════════════ Seg A (Digital Leaders): LR 34% | 18% of book | Grow Seg B (Established): LR 48% | 42% of book | Maintain Seg C (Emerging): LR 62% | 28% of book | Price up Seg D (Minimal): LR 84% | 12% of book | Restrict STRATEGIC ACTIONS: 1. Restrict Segment D to 8% of book (from 12%) 2. Expand Segment A pricing credits by 5% 3. Apply +15% surcharge to Segment C new business 4. Mandate digital footprint scoring for all submissions Projected loss ratio improvement: 6 points annually Premium impact: +$4.2M from better selection

4Competitor Pricing Intelligence

AI agent monitors competitor insurance carriers' pricing signals through their web presence — tracking product launches, pricing page changes, and market positioning to inform actuarial rate-setting decisions.

1
Monitor Competitor Rate Signals
/pricing /products /press /blog Change Detection
COMPETITOR PRICING INTELLIGENCE — FEBRUARY 2026 ════════════════════════════════════════════════════════ progressivecommercial.com /pricing: Updated — new "Pay-As-You-Drive" commercial auto tier /products: Added IoT-based fleet monitoring discount /blog: "Why we're lowering commercial auto rates by 8%" SIGNAL: Aggressive pricing in commercial auto segment hartfordinsurance.com /products: Launched new "Small Business Shield" package /pricing: Bundled GL+Property+Cyber for small businesses /press: "$200M investment in small commercial platform" SIGNAL: Major push into small commercial space cikiinsurance.com /press: Announced exit from Florida homeowners market /products: Florida products page removed SIGNAL: Market capacity reduction — opportunity for rate increase
2
Assess Rate Filing Implications
Domain Signal
progressivecommercial.com — /pricing page updated with usage-based commercial auto tier and IoT fleet discounts. /blog explains 8% rate reduction rationale. /careers lists 18 telematics data scientist roles. Progressive is leveraging telematics data to selectively reduce rates for monitored fleets while maintaining or increasing rates for unmonitored fleets.
SELECTIVE PRICING — Progressive cutting rates only for telematics-monitored fleets
Sector Signal
Commercial Auto Market — 6 carriers showed pricing page changes this month. Average filed rate increase: +9.8% for standard commercial auto. However, 3 carriers now offer telematics discounts of 8-15% for monitored fleets. The market is bifurcating between telematics-enabled and traditional pricing. Florida capacity withdrawal creates localized rate hardening opportunity.
MARKET BIFURCATION — Telematics pricing creating two-tier commercial auto market
3
Output: Pricing Intelligence Report

COMPETITOR PRICING INTELLIGENCE — Q1 2026

MARKET RATE MOVEMENTS: ══════════════════════════════════════════════ Commercial auto: +9.8% avg filed (but telematics discounts 8-15%) Small commercial: Bundled pricing emerging (Hartford, Travelers) Florida homeowners: Capacity reduction — rate opportunity RATE FILING RECOMMENDATIONS: 1. File +7.5% commercial auto (below market, gain share) 2. Develop bundled small commercial product (competitive) 3. File +12% Florida homeowners (capacity withdrawn) Revenue impact: +$18M from optimized rate positioning Competitive position: Within 3% of market on all lines

5Loss Development Pattern Analysis

AI agent correlates loss development patterns with domain intelligence characteristics — identifying which risk segments develop losses faster or slower, enabling more accurate IBNR reserves.

1
Correlate Development Factors with Web Data
/about /legal Domain Ages IAB Categories OpenPageRank
LOSS DEVELOPMENT BY DIGITAL MATURITY SEGMENT ════════════════════════════════════════════════════════ 12-24 MONTH DEVELOPMENT FACTORS: Segment A (Digital Leaders): LDF 12-24: 1.08 | LDF 24-36: 1.03 Develops quickly, minimal late-reported claims /legal page presence correlates with early notification Segment B (Established Standard): LDF 12-24: 1.18 | LDF 24-36: 1.09 Standard development pattern Segment C (Emerging Businesses): LDF 12-24: 1.34 | LDF 24-36: 1.16 Slower reporting, more late-emerging claims Segment D (Minimal Web Presence): LDF 12-24: 1.52 | LDF 24-36: 1.28 Significantly longer tail — late reporting epidemic IBNR for Segment D should be 40% higher than average RESERVE IMPACT: Applying segment-specific LDFs: +$14.2M additional IBNR needed Primarily from Segment D under-reserving
2
Interpret Development Pattern Drivers
Domain Signal
Segment D Development Pattern — Accounts with minimal web presence (0-3 pages, PR below 1) show 12-24 month LDFs of 1.52 vs. 1.08 for digital leaders. The /legal page presence is the strongest individual predictor of early claim notification — companies with /legal pages report claims 3x faster, reducing IBNR uncertainty.
LONG TAIL — Minimal web presence accounts develop losses 40% slower than leaders
Sector Signal
IBNR Adequacy Impact — Applying segment-specific loss development factors reveals $14.2M in additional IBNR needed, primarily from Segment D under-reserving. Current reserving methodology uses a single set of LDFs — segmenting by digital maturity captures a 44% spread in development speed that traditional methods miss entirely.
UNDER-RESERVED — $14.2M additional IBNR needed when segmented by digital maturity
3
Output: Loss Development Report

LOSS DEVELOPMENT ANALYSIS — Q1 2026

SEGMENT-SPECIFIC DEVELOPMENT FACTORS: ══════════════════════════════════════════════ Seg A (Digital Leaders): LDF 12-24 1.08 | Fast development Seg B (Established): LDF 12-24 1.18 | Standard Seg C (Emerging): LDF 12-24 1.34 | Slow development Seg D (Minimal): LDF 12-24 1.52 | Very long tail RESERVE ADJUSTMENT: Additional IBNR needed: +$14.2M Primary driver: Segment D under-reserving Recommendation: Adopt segment-specific LDFs in Q2 reserve review Key predictor: /legal page presence → 3x faster claim notification

6Rate Filing Support

AI agent provides data-driven support for rate filing submissions — generating market analysis, risk factor justifications, and competitive comparisons using domain intelligence to strengthen regulatory filings.

1
Generate Rate Filing Data
/pricing /products /compliance IAB Categories Change Detection
RATE FILING SUPPORT — COMMERCIAL AUTO INCREASE REQUEST ════════════════════════════════════════════════════════ MARKET JUSTIFICATION DATA: Competitor rate changes (from /pricing page monitoring): Progressive: +8.2% (filed Jan 2026) Travelers: +11.4% (filed Dec 2025) Liberty Mutual: +9.7% (filed Jan 2026) Industry average filed increase: +9.8% Our requested increase: +7.5% (below market) LOSS TREND JUSTIFICATION: Fleet operators in portfolio (from /products + IAB): E-commerce delivery fleet growth: +34% YoY Gig economy fleet entries: +47% new domains in IAB:Delivery Telematics adoption: Only 23% of fleet domains show /products pages with telematics references Filing support package generated with 47 data points All competitor data sourced from public domain intelligence
2
Validate Filing Justification Strength
Domain Signal
Competitor Rate Filed Analysis — Progressive (+8.2%), Travelers (+11.4%), and Liberty Mutual (+9.7%) all filed commercial auto increases above our requested +7.5%. /pricing page monitoring confirms these changes are live in production. Our filing is below the market average, strengthening the regulatory approval argument that our request is reasonable and justified.
STRONG JUSTIFICATION — Our +7.5% request is below market average of +9.8%
Sector Signal
Commercial Auto Loss Trends — E-commerce delivery fleet domains grew 34% YoY in the IAB:Delivery category. Gig economy fleet entries up 47%. Only 23% of fleet operator domains show telematics on /products pages. The expanding, largely unmonitored fleet universe directly drives commercial auto loss frequency increases that justify rate adjustments.
LOSS DRIVERS — Fleet growth +34% YoY with only 23% telematics adoption
3
Output: Rate Filing Package

RATE FILING SUPPORT — COMMERCIAL AUTO Q1 2026

FILING JUSTIFICATION SUMMARY: ══════════════════════════════════════════════ Requested increase: +7.5% (below market avg +9.8%) Competitor filings: Progressive +8.2%, Travelers +11.4% Data points supporting filing: 47 LOSS TREND EVIDENCE: Fleet growth: +34% YoY (IAB delivery domain analysis) Telematics adoption: Only 23% of fleet domains Gig economy expansion: +47% new fleet domains APPROVAL PROBABILITY: Filing is below market average — strong approval likelihood Historical approval rate for below-market filings: 94% Expected timeline: 30-60 days in most states

7Experience Modification Analysis

AI agent enriches experience modification factor calculations with web-derived business intelligence — validating payroll proxies, verifying industry classifications, and detecting experience period anomalies.

1
Validate Experience Mod Inputs
/about /careers /products IAB Categories Personas
EXPERIENCE MOD VALIDATION — WORKERS COMP PORTFOLIO ════════════════════════════════════════════════════════ CLASS CODE MISMATCH DETECTED: metrodeliveryfleet.com — Class: 8742 (Outside Sales) /products: "Last-mile delivery fleet, 200 vehicles" /careers: Hiring "delivery drivers" and "warehouse workers" CORRECT CLASS: 7219 (Trucking) — rate 3.4x higher Premium impact: +$127K annually PAYROLL VALIDATION: techstartupinnovate.io — Reported payroll: $2.1M /careers: 47 open positions, mostly senior engineers /about: "92 employees" | Industry: Software Expected payroll range: $8.4M-$12.6M Reported payroll appears significantly understated PORTFOLIO IMPACT: Class code mismatches found: 34 accounts Payroll discrepancies: 89 accounts Combined premium gap: $2.8M in under-collected premium
2
Assess Premium Leakage Impact
Domain Signal
metrodeliveryfleet.com — /products clearly describes "last-mile delivery fleet with 200 vehicles" while classified under 8742 (Outside Sales). /careers lists "delivery drivers" and "warehouse workers" — occupations consistent with Class 7219 (Trucking), which carries a rate 3.4x higher. This single misclassification represents $127K in annual premium leakage.
CLASS CODE FRAUD — Delivery fleet classified as outside sales, 3.4x rate gap
Sector Signal
Workers Comp Leakage — Across the WC portfolio, domain intelligence detected 34 class code mismatches and 89 payroll discrepancies. Combined premium gap: $2.8M annually. The most common pattern: delivery and logistics companies classified under lower-hazard office codes. Web presence analysis catches misclassification at 4x the rate of traditional audit alone.
PREMIUM LEAKAGE — $2.8M in under-collected WC premium from misclassifications
3
Output: Experience Mod Validation Report

EXPERIENCE MOD VALIDATION — Q1 2026

VALIDATION RESULTS: ══════════════════════════════════════════════ Accounts validated: 2,400 workers comp policies Class code mismatches: 34 accounts Payroll discrepancies: 89 accounts Combined premium gap: $2.8M annually TOP ISSUES: Delivery/logistics misclassified as office: 12 accounts ($890K) Tech companies understating payroll: 23 accounts ($1.2M) Construction class code shopping: 8 accounts ($340K) ACTIONS: 1. Reclassify 34 mismatched accounts at next renewal 2. Initiate premium audits for 89 payroll discrepancies 3. Deploy mandatory web-based classification verification Premium recovery: $2.8M in corrected premium

8Reserve Adequacy Monitoring

AI agent monitors reserve adequacy by tracking insured business health changes that could impact outstanding claims — detecting business closures, expansions, and financial distress that affect claim development.

1
Monitor Business Health for Open Claims
/press /careers /about OpenPageRank Change Detection
RESERVE ADEQUACY ALERTS — 347 OPEN CLAIMS WITH CHANGES ════════════════════════════════════════════════════════ INCREASE RESERVE — Business Deterioration grandrapidsauto.com — Claim: WC, 3 open claims /press: "Grand Rapids Auto announces plant closure" /careers: All positions removed (was 12) Impact: Claimants may pursue larger settlements if employer closes Recommendation: Increase reserves +$340K across 3 claims DECREASE RESERVE — Business Improving returntoworkrehab.com — WC claimant's new employer /careers: Claimant's job title appears in new postings Evidence of return to work — reduce BI reserve by $89K QUARTERLY RESERVE ADJUSTMENT: Increases recommended: +$4.2M (87 claims) Decreases recommended: -$1.8M (34 claims) Net adjustment: +$2.4M
2
Assess Reserve Adjustment Drivers
Domain Signal
grandrapidsauto.com — /press announced plant closure on 2026-02-01. /careers removed all 12 positions within 2 weeks. PageRank dropped from 3.2 to 2.1. Three open WC claims with this employer — claimants will have stronger settlement leverage once the employer ceases operations. Historical pattern: WC claims from closed businesses settle 40% higher.
EMPLOYER CLOSING — Open WC claims will settle 40% higher, increase reserves
Sector Signal
Business Health Monitoring — Of 347 open claims with business health changes detected this quarter, 87 require reserve increases and 34 support decreases. The net +$2.4M adjustment is modest relative to total reserves but would have been missed entirely without continuous web monitoring. Early detection reduces reserve development surprise by an estimated 28%.
PROACTIVE RESERVING — Early detection reduces development surprise by 28%
3
Output: Reserve Adequacy Report

RESERVE ADEQUACY — Q1 2026

RESERVE ADJUSTMENT SUMMARY: ══════════════════════════════════════════════ Claims monitored: 347 with business health changes Reserve increases: +$4.2M (87 claims) Reserve decreases: -$1.8M (34 claims) Net adjustment: +$2.4M KEY DRIVERS: Business closures: 8 employers | +$1.8M reserve increase Financial distress: 14 employers | +$1.2M increase Return to work signals: 34 claims | -$1.8M decrease VALUE OF MONITORING: Reserve development surprise reduction: 28% Adverse development avoided: $6.8M over 3 years

9Predictive Model Monitoring

AI agent continuously monitors actuarial model performance by tracking how domain intelligence variables drift over time — detecting model degradation and recommending recalibration before pricing errors accumulate.

1
Track Feature Drift & Model Stability
/about /careers OpenPageRank Domain Ages Change Detection
MODEL MONITORING — FEBRUARY 2026 STABILITY REPORT ════════════════════════════════════════════════════════ FEATURE STABILITY: Digital Maturity Score: Stable (PSI: 0.04) Safety Culture Index: Stable (PSI: 0.06) Business Stability Score: Drifting (PSI: 0.14) → More businesses showing career page reductions → Macro-economic slowdown affecting feature distribution Revenue Proxy Index: Stable (PSI: 0.05) MODEL DISCRIMINATION: Training Gini: 0.52 | Current Gini: 0.49 Degradation: -5.8% (within acceptable range) RECOMMENDATION: Business Stability Score feature drifting — recalibrate in Q2 Incorporate macro-economic indicators as interaction terms No immediate model overhaul needed
2
Diagnose Drift Root Causes
Domain Signal
Business Stability Score Drift — The PSI of 0.14 indicates moderate feature drift. Root cause: macro-economic slowdown has caused 18% more businesses to reduce /careers page listings (the primary component). This is a systemic shift, not a data quality issue. The feature is still predictive but its distribution has changed — recalibration will restore accuracy without changing the underlying signal.
MACRO DRIFT — Economic slowdown shifting /careers distributions across portfolio
Sector Signal
Model Stability Assessment — Overall model Gini dropped from 0.52 to 0.49 (-5.8%), within the acceptable 10% degradation threshold. Three of four features remain stable. The drifting Business Stability Score needs Q2 recalibration but does not require model replacement. Expected cost of not recalibrating: $3.2M in pricing inaccuracy over 12 months.
MODEL HEALTHY — 5.8% degradation within threshold, recalibrate one feature in Q2
3
Output: Model Monitoring Report

PREDICTIVE MODEL MONITORING — Q1 2026

MODEL HEALTH STATUS: ══════════════════════════════════════════════ Overall Gini: 0.49 (training: 0.52, -5.8% degradation) Status: Within acceptable range (threshold: -10%) FEATURE STABILITY: Digital Maturity: Stable (PSI 0.04) Safety Culture: Stable (PSI 0.06) Business Stability: Drifting (PSI 0.14) — recalibrate Q2 Revenue Proxy: Stable (PSI 0.05) ACTIONS: Recalibrate Business Stability Score in Q2 rate cycle Add macro-economic interaction terms to next model version Cost of inaction: $3.2M pricing inaccuracy over 12 months

10Actuarial Reporting Automation

AI agent automates actuarial reporting by generating segment-level loss analyses, reserve development exhibits, and portfolio health metrics enriched with domain intelligence signals for board and regulatory presentations.

1
Aggregate Actuarial Data Points
/about /security OpenPageRank Domain Ages IAB Categories
ACTUARIAL DATA AGGREGATION — Q1 2026 ════════════════════════════════════════════════════════ PORTFOLIO PERFORMANCE BY DIGITAL SEGMENT: Seg A (Digital Leaders): LR 34% | CR 72% | Growing +8% Seg B (Established): LR 48% | CR 84% | Growing +3% Seg C (Emerging): LR 62% | CR 98% | Growing +12% Seg D (Minimal): LR 84% | CR 124% | Shrinking -2% PRICING MODEL IMPACT: Domain intelligence features improve Gini by 37% Premium leakage detected: $2.8M | Reserve adjustment: +$2.4M Rate filing support: 47 competitive data points generated DATA ENRICHMENT METRICS: Policies enriched: 12,400 | Features extracted: 4 per policy /security pages analyzed: 3,472 | /careers tracked: 8,900 PageRank scores: 12,400 | Domain ages: 12,400
2
Synthesize Board-Level Insights
Domain Signal
Portfolio Intelligence Summary — Domain intelligence has been applied to all 12,400 policies with measurable impact: 37% improvement in pricing model accuracy, $2.8M in detected premium leakage, $14.2M in reserve adjustments identified, and 47 competitive data points supporting rate filings. The system generates 4 predictive features per policy from web-derived data.
FULL ENRICHMENT — 12,400 policies scored with 4 web-derived features each
Sector Signal
Actuarial Value Summary — Across all actuarial workflows, domain intelligence contributed: pricing accuracy improvement worth $18M in better risk selection, $2.8M in recovered premium leakage, $14.2M in reserve adequacy improvements, and $2.4M in reserve adjustment early detection. Total actuarial value: $37.4M annually from a $25K database investment.
1,496x ROI — $37.4M actuarial value from $25K annual database investment
3
Output: Actuarial Board Report

ACTUARIAL INTELLIGENCE — Q1 2026 BOARD PRESENTATION

PORTFOLIO PERFORMANCE BY DIGITAL SEGMENT: ══════════════════════════════════════════════ Segment A (Digital Leaders): LR 34% | CR 72% | Growing +8% Segment B (Established): LR 48% | CR 84% | Growing +3% Segment C (Emerging): LR 62% | CR 98% | Growing +12% Segment D (Minimal): LR 84% | CR 124% | Shrinking -2% PRICING MODEL IMPACT: Domain intelligence features improve Gini by 37% Premium leakage reduced by $2.8M annually Reserve accuracy improved by $14.2M in IBNR corrections RECOMMENDATION: Restrict Segment D to 8% of book (currently 12%) Expand Segment A pricing credits by 5% Implement mandatory digital footprint scoring for all new business
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