README.md

---
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

```python
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

```bibtex
@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+)