license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
tags:
- insurance
- underwriting
- pricing
- actuarial
- submission-triage
- bind-rate
- market-cycle
- synthetic-data
- p-and-c
- commercial-lines
pretty_name: INS-009 — Synthetic Underwriting Intelligence Dataset (Sample)
size_categories:
- 1K<n<10K
INS-009 — Synthetic Underwriting Intelligence Dataset (Sample)
XpertSystems.ai Synthetic Data Platform · SKU: INS009-SAMPLE · Version 1.0.0
This is a free preview of the full INS-009 — Synthetic Underwriting Intelligence Dataset product. It contains roughly ~10% of the full dataset at identical schema, market cycle calibration, and UW workflow modeling, so you can evaluate fit before licensing the full product.
| File | Rows (sample) | Rows (full) | Description |
|---|---|---|---|
underwriting_records.csv |
~5,000 | ~50,000 | Per-submission records (161 columns) |
underwriter_performance_summary.csv |
~150 | ~150 | Bound-only UW performance KPIs |
segment_loss_ratio_table.csv |
~62 | ~65 | Loss ratio by LOB × UW tier |
pricing_adequacy_distribution.csv |
~25 | ~30 | Pricing tier × UW tier adequacy distribution |
Dataset Summary
INS-009 simulates the full commercial underwriting submission lifecycle — from broker submission through risk assessment, pricing, binding, and in-force performance — with realistic UW tier hierarchies and market cycle modeling.
Calibration sources (named, authoritative):
- NAIC Industry Aggregate Reports — combined ratio, loss ratio by LOB
- A.M. Best Combined Ratio annual reports
- Conning Strategic Study on UW Performance
- McKinsey U.S. P&C insurance analytics
- PwC Commercial Insurance UW Survey — bind / decline / NTU rates
- ISO loss costs — base loss ratio calibration
13 lines of business:
- Commercial property
- General liability
- Commercial auto
- Workers compensation
- Professional liability (E&O)
- Directors & Officers
- Cyber (first-party / third-party / combined)
- Marine cargo
- Inland marine
- Excess / umbrella
- Personal auto
- Homeowners
- Specialty (accident & health, aviation, agriculture, event cancellation, surety)
Market cycle modeling (configurable in full product):
- Hard market: rate increases, restricted capacity, higher decline rates, tighter terms
- Soft market: rate decreases, abundant capacity, lower decline rates, loose terms
- Transitional: mixed signals, varying by LOB
7 submission outcomes:
- Bound (~43%)
- Declined (~23%)
- Quoted not taken (~20%)
- Withdrawn (~6%)
- Incomplete/abandoned (~5%)
- Referred out (~2%)
- Remarket (~1%)
5 underwriter tier hierarchy:
- Junior UW (entry-level, lower binding authority)
- Mid-level UW (standard book)
- Senior UW (specialty / large accounts)
- Principal UW (major accounts, complex risks)
- Chief Underwriter (executive, portfolio steward)
UW skill gradient is empirically modeled: senior tiers produce better loss ratios and tighter pricing adequacy than junior tiers (realistic experience curve effect).
Submission/insured features (40+ columns):
- Submission ID, carrier ID, broker tier, distribution channel
- Insured: legal entity type, ownership structure, years in business, revenue/payroll/headcount, NAICS code, geographic spread
- Publicly traded flag, regulatory jurisdiction
- Submission completeness score
- Submitted ACORD flag, application type
Risk assessment features (30+ columns):
- Credit score (commercial), prior claims history
- Loss ratio history (prior, 5yr avg, segment benchmark)
- Experience modification factor (mod)
- Technical risk score
- Underwriter judgment score
- Risk quality score
- Final composite score
- CAT zone exposure, peril concentration
- Risk-improvement recommendations issued
Pricing & coverage (40+ columns):
- Quoted premium, written premium
- Pricing adequacy ratio (target = 1.0)
- Pricing tier (preferred / standard / non-standard / referral / bespoke / minimum)
- Rate adequacy filing flag
- Coverage limits (primary, retention, excess)
- TIV (total insured value)
- Commission rate, broker tier
- Expected loss cost, reinsurance cost
- ROEL (return on expected loss)
- Rate change vs expiring
- Cat load, expense load, profit & contingency
Binding & policy (20+ columns):
- Submission outcome (7 classes)
- Effective date, term length
- UW authority level used
- Manual referral count
- Decline reason taxonomy
In-force performance (20+ columns):
- Earned premium, current period incurred
- Loss ratio current
- Loss ratio segment benchmark
- Loss ratio vs benchmark
- IBNR estimate
- Adverse / favorable development flags
Regulatory & financial (10+ columns):
- IFRS 17 LRC / LIC / Risk Adjustment
- IFRS 17 loss component flag
- Solvency II SCR allocation
- Rate filing required, jurisdiction approval status
Calibrated Validation Results
Sample validation results across 10 underwriting-intelligence KPIs:
| Metric | Observed | Target | Source | Verdict |
|---|---|---|---|---|
| n_lines_of_business | 13 | 13 | 13 LOBs in product taxonomy | ✓ PASS |
| n_underwriter_tiers | 5 | 5 | 5 UW tier hierarchy | ✓ PASS |
| bind_rate_pct | 42.90 | 42.00 | Commercial UW bind rate | ✓ PASS |
| decline_rate_pct | 22.64 | 22.00 | Commercial UW decline rate | ✓ PASS |
| quoted_not_taken_rate_pct | 20.16 | 20.00 | Commercial UW NTU rate | ✓ PASS |
| pricing_adequacy_ratio_mean | 0.961 | 1.000 | Target pricing adequacy | ✓ PASS |
| bound_loss_ratio_mean_pct | 73.55 | 70.00 | P&C industry loss ratio | ✓ PASS |
| uw_tier_lr_gradient_pct | 17.01 | 15.00 | Junior-Senior LR gap (skill) | ✓ PASS |
| composite_risk_score_mean | 55.83 | 55.00 | Composite score mid-range | ✓ PASS |
| submission_completeness_mean | 72.83 | 70.00 | Completeness score (data quality) | ✓ PASS |
Note: The uw_tier_lr_gradient_pct metric measures the loss-ratio gap
between junior and senior underwriters. A positive gap is correct: senior
UWs produce better books due to selection and pricing skill. This is a
key training signal for ML models predicting UW performance trajectory.
Schema Highlights
The 161-column schema is extensive. Key groupings:
Submission identification: submission_id, carrier_id, line_of_business, underwriter_id, underwriter_tier, broker_tier, distribution_channel, submission_date, effective_date.
Insured profile: legal_entity_type, ownership_structure, years_in_business, naics_code, naics_description, annual_revenue_usd, annual_payroll_usd, employee_headcount, publicly_traded_flag, multistate_operations_flag.
Risk scoring: credit_score_commercial, prior_loss_ratio_pct, loss_ratio_5yr_avg_pct, experience_mod_factor, technical_risk_score, underwriter_judgement_score, risk_quality_score, final_composite_score, submission_completeness_score, cat_zone, cat_concentration_pct.
Coverage: primary_limit_usd, retention_usd, total_insured_value_usd, deductible_usd, sublimit_count, optional_endorsement_count.
Pricing: quoted_premium_usd, written_premium_usd, pricing_adequacy_ratio, pricing_tier, base_rate_per_unit, schedule_rating_credit_pct, experience_credit_pct, commission_rate_pct, rate_change_vs_expiring_pct.
Outcome: submission_outcome (7 classes), decline_reason, referral_count, days_to_quote, days_to_bind.
Performance: earned_premium_usd, current_period_incurred_usd, loss_ratio_current_pct, loss_ratio_segment_benchmark_pct, adverse_development_flag, favorable_development_flag.
Regulatory: ifrs17_lrc_usd, ifrs17_lic_usd, ifrs17_risk_adjustment_usd, ifrs17_loss_component_flag, solvency_ii_scr_allocation_usd, rate_filing_required_flag.
Suggested Use Cases
- Submission triage — predict probability of binding from submission features
- UW workflow automation — predict which submissions need manual referral
- Quote-to-bind conversion prediction (NTU vs bound classification)
- Decline reason classification — multi-class decline taxonomy
- Pricing adequacy modeling — regression on
pricing_adequacy_ratio - UW tier performance ranking — predict junior vs senior UW outputs
- Risk score calibration — train composite_score predictors from features
- Loss ratio forecasting at bind time — predict future LR from submission
- Adverse development early warning for in-force policies
- Pricing tier classification (6-class: preferred → bespoke → minimum)
- Market cycle detection — train on hard/soft/transitional data
- NAICS-based risk scoring
- Cyber UW automation — first-party vs third-party vs combined modeling
- Workers comp class code rating
- Commission rate optimization by broker tier
- Reinsurance cost forecasting by LOB and TIV
- IFRS 17 LRC/LIC modeling at policy issuance
- Insurtech UW model training without proprietary submission data
Loading the Data
import pandas as pd
submissions = pd.read_csv("underwriting_records.csv")
uw_perf = pd.read_csv("underwriter_performance_summary.csv")
seg_lr = pd.read_csv("segment_loss_ratio_table.csv")
pricing_dist= pd.read_csv("pricing_adequacy_distribution.csv")
# Binary bind prediction
y_bind = (submissions["submission_outcome"] == "bound").astype(int)
# Multi-class submission outcome (7 classes)
y_outcome = submissions["submission_outcome"]
# Regression: pricing adequacy ratio (bound only)
bound = submissions[submissions["submission_outcome"] == "bound"]
y_adequacy = bound["pricing_adequacy_ratio"]
# Regression: bound loss ratio
y_lr = bound["loss_ratio_current_pct"]
# Multi-class UW tier prediction (5 tiers)
y_tier = submissions["underwriter_tier"]
# Multi-class pricing tier prediction (6 tiers)
y_pricing_tier = submissions["pricing_tier"]
# Multi-class LOB classification (13 LOBs)
y_lob = submissions["line_of_business"]
# Composite risk score regression
y_score = submissions["final_composite_score"]
License
This sample is released under CC-BY-NC-4.0 (free for non-commercial research and evaluation). The full production dataset is licensed commercially — contact XpertSystems.ai for licensing terms.
Full Product
The full INS-009 dataset includes ~50,000 underwriting submission records across 161 columns, with configurable market cycle (hard / soft / transitional), underwriter count, carrier count, and LOB filtering. Calibrated to NAIC Industry Aggregates, A.M. Best Combined Ratio, Conning UW Performance, McKinsey U.S. P&C analytics, and PwC Commercial UW Survey.
📧 pradeep@xpertsystems.ai 🌐 https://xpertsystems.ai
Citation
@dataset{xpertsystems_ins009_sample_2026,
title = {INS-009: Synthetic Underwriting Intelligence Dataset (Sample)},
author = {XpertSystems.ai},
year = {2026},
url = {https://huggingface.co/datasets/xpertsystems/ins009-sample}
}
Generation Details
- Generator version : 1.0.0
- Random seed : 42
- Generated : 2026-05-16 20:59:33 UTC
- Market cycle : transitional
- Records : 5,000
- Underwriters : 150 / Carriers: 20
- Calibration basis : NAIC + A.M. Best + Conning + McKinsey + PwC
- Overall validation: 100.0 / 100 (grade A+)