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.

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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
Model Performance
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.
18.4% improvement in pricing accuracy

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.

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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

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.

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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

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.

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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

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.

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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

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.

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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

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.

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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

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.

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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

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.

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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

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.

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Generate Actuarial Board Report
/about /security OpenPageRank Domain Ages IAB Categories

Actuarial Intelligence Report — 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 $4.2M annually Reserve accuracy improved by 12 basis points 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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