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README.md

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+---
+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+)

pricing_adequacy_distribution.csv

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+underwriter_tier,pricing_tier,count,mean_adequacy,p25,p75
+chief_underwriter,non_standard_rate,16,0.94374375,0.88415,0.939675
+chief_underwriter,preferred_rate,1,1.1883,1.1883,1.1883
+chief_underwriter,referral_priced,1,0.7795,0.7795,0.7795
+chief_underwriter,standard_rate,46,1.0343760869565217,0.994175,1.07085
+junior,bespoke_priced,128,0.68982578125,0.629525,0.7583
+junior,non_standard_rate,574,1.0782114982578397,0.8784,1.2732
+junior,preferred_rate,81,1.288837037037037,1.1966,1.3526
+junior,referral_priced,131,0.6601954198473283,0.58205,0.745
+junior,standard_rate,472,1.0474118644067796,1.0005249999999999,1.096125
+mid_level,bespoke_priced,114,0.714371052631579,0.6678,0.77275
+mid_level,minimum_premium,1,0.6869,0.6869,0.6869
+mid_level,non_standard_rate,806,1.0045392059553349,0.866025,1.183925
+mid_level,preferred_rate,96,1.2474385416666667,1.187075,1.2825
+mid_level,referral_priced,129,0.7207054263565892,0.6767,0.7751
+mid_level,standard_rate,823,1.0445381530984204,0.99355,1.0930499999999999
+principal,bespoke_priced,9,0.7658555555555555,0.7582,0.7807
+principal,non_standard_rate,121,0.9417553719008265,0.8842,0.9369
+principal,preferred_rate,5,1.2094,1.1906,1.2009
+principal,referral_priced,2,0.7764500000000001,0.775575,0.777325
+principal,standard_rate,203,1.036149261083744,0.992,1.0727000000000002
+senior,bespoke_priced,31,0.7505129032258064,0.7342,0.7839
+senior,non_standard_rate,483,0.9635697722567288,0.86395,0.94695
+senior,preferred_rate,33,1.2161454545454546,1.1796,1.2463
+senior,referral_priced,35,0.75444,0.7414499999999999,0.78175
+senior,standard_rate,659,1.037373899848255,0.992,1.0845500000000001

segment_loss_ratio_table.csv

@@ -0,0 +1,63 @@
+line_of_business,underwriter_tier,total_earned,total_incurred,avg_pricing_adequacy,record_count,segment_loss_ratio_pct
+commercial_auto,chief_underwriter,48342799.93,27149424.82,0.9773666666666667,3,56.16022418914949
+commercial_auto,junior,1060823371.73,1122747178.29,0.96008,75,105.8373343018465
+commercial_auto,mid_level,1257547366.36,1014601664.84,0.9700461538461538,104,80.68098999537395
+commercial_auto,principal,140569898.48,88669941.23,0.9984999999999999,8,63.078896825564534
+commercial_auto,senior,786789681.63,657019367.91,0.9725864406779662,59,83.50635287296173
+commercial_property,chief_underwriter,6956078.12,2957267.26,1.0460666666666667,3,42.513427954429005
+commercial_property,junior,1192536507.64,755651160.15,0.9743504587155963,109,63.365033716696416
+commercial_property,mid_level,1375618401.39,905488603.64,0.9527171641791045,134,65.82411246644017
+commercial_property,principal,264818943.05,145487278.39,1.000376923076923,26,54.93839553710883
+commercial_property,senior,1146543967.5,633971080.81,0.9691047169811321,106,55.294092401214435
+cyber,chief_underwriter,25836465.55,17208623.72,1.1883,1,66.60595152497551
+cyber,junior,442742917.45,435865541.07,0.900875,44,98.44664338853559
+cyber,mid_level,711684275.25,569675195.5,0.9307950819672132,61,80.0460562796452
+cyber,principal,77058369.39,46382391.67,0.9577571428571429,7,60.1912446852517
+cyber,senior,296280390.07,214879008.19,0.949121875,32,72.52555869095221
+directors_officers,chief_underwriter,230553.29,105948.86,0.9486,1,45.954173978605986
+directors_officers,junior,276121895.26,183521950.41,1.0182607142857143,28,66.46410645457628
+directors_officers,mid_level,344979862.95,269008414.51,0.977516129032258,31,77.97800492169277
+directors_officers,principal,23901536.79,11270289.87,0.9530333333333334,3,47.15299258378774
+directors_officers,senior,264447760.83,173780320.14,0.9676181818181818,22,65.7144229902232
+excess_umbrella,chief_underwriter,20425011.64,11206824.280000001,1.10635,2,54.86814146070176
+excess_umbrella,junior,170609003.57,128055370.83,0.9120705882352942,17,75.05780360381715
+excess_umbrella,mid_level,364335188.6,288193642.53,0.936828947368421,38,79.10123741750482
+excess_umbrella,principal,12170692.24,5743012.29,1.0570333333333333,3,47.187227946863274
+excess_umbrella,senior,234347655.28,156658495.31,0.9649407407407408,27,66.84875729728275
+general_liability,chief_underwriter,54715994.62,33010749.84,1.0357333333333332,6,60.33107881755253
+general_liability,junior,754205418.94,612093667.95,0.9408322033898305,118,81.15742111880722
+general_liability,mid_level,1212768793.67,862672878.9399999,0.9574827814569538,151,71.13250962942712
+general_liability,principal,255992954.33,151407848.05,0.9911518518518518,27,59.14531845076504
+general_liability,senior,792842769.75,541430078.42,1.0014708333333333,96,68.28971633186795
+homeowners,junior,936337.97,612053.74,0.9646652173913044,23,65.36675427143044
+homeowners,mid_level,1744905.95,1460611.3599999999,0.9217181818181818,44,83.70716828606149
+homeowners,principal,406323.26,395830.98,1.0080363636363636,11,97.41775058607277
+homeowners,senior,883756.0599999999,596488.1,0.984525,24,67.4946545769655
+inland_marine,chief_underwriter,14495996.35,6030409.2,0.9701,1,41.60051544163089
+inland_marine,junior,220810463.1,111662513.13,0.9815235294117647,17,50.56939402343023
+inland_marine,mid_level,318120593.42,213587258.97,1.0098344827586208,29,67.1403434382541
+inland_marine,principal,39802972.620000005,15283053.84,0.97888,5,38.39676494996418
+inland_marine,senior,211903802.91,105180559.36,0.9517049999999999,20,49.63599421793879
+marine_cargo,junior,134677641.5,75589543.85,1.0768727272727272,11,56.126275310516185
+marine_cargo,mid_level,332698353.88,183244701.15,0.9728575757575758,33,55.078331170852145
+marine_cargo,principal,61558521.89,38804298.36,0.9615166666666667,6,63.03643617262299
+marine_cargo,senior,210004227.81,111238230.72,0.9586714285714286,21,52.969519652071995
+personal_auto,chief_underwriter,281197.03,169597.8,1.0164,1,60.312799178568845
+personal_auto,junior,4173580.9,3818461.44,0.8999481481481482,27,91.49125251172201
+personal_auto,mid_level,10206797.76,9115992.0,0.9297275862068966,58,89.31294823656818
+personal_auto,principal,1832278.4,1315998.81,0.9218777777777777,9,71.82308157974248
+personal_auto,senior,4969766.04,3459659.35,0.9641409090909092,22,69.61412915928736
+professional_liability,chief_underwriter,30867324.29,18225444.96,0.8916,3,59.04446005352154
+professional_liability,junior,463811823.43,336346136.15,0.8999211538461539,52,72.51780122003777
+professional_liability,mid_level,914112824.67,568325492.41,0.9669357142857142,84,62.17235740185231
+professional_liability,principal,91576118.89,50649983.18000001,0.9682333333333334,9,55.309161158969935
+professional_liability,senior,428204560.69,234897995.88,0.96832,45,54.85649090273355
+specialty,junior,33043962.54,19745226.6,0.9397,2,59.754415276612896
+specialty,mid_level,50773930.989999995,49130673.04,0.9456166666666667,6,96.76357942361477
+specialty,principal,65446462.410000004,78454123.34,0.89715,6,119.8752697258278
+specialty,senior,27444654.88,15187502.19,0.9278,3,55.338652485908035
+workers_comp,chief_underwriter,6092472.65,4946805.65,0.9671666666666666,3,81.19536900998645
+workers_comp,junior,619579821.54,491245759.76,0.9466701492537314,67,79.28692037435651
+workers_comp,mid_level,690946407.71,495381095.58,0.9556037500000001,80,71.69602302758022
+workers_comp,principal,174368855.36,98841447.97,0.983985,20,56.68526513288915
+workers_comp,senior,611648820.33,414103821.84,1.0015245901639345,61,67.70287264129448

underwriter_performance_summary.csv

@@ -0,0 +1,151 @@
+underwriter_id,underwriter_tier,underwriter_experience_years,total_written_premium,avg_pricing_adequacy,portfolio_loss_ratio,record_count
+UW-001,senior,15,177267002.79,0.95755,59.635,16
+UW-002,mid_level,8,128664125.25,0.9533066666666666,79.88466666666666,15
+UW-003,senior,10,80324530.2,0.9721727272727272,57.698181818181816,11
+UW-004,senior,13,110442257.27,0.94313,67.96000000000001,10
+UW-005,junior,2,63804851.16,0.9102363636363637,130.4190909090909,11
+UW-006,chief_underwriter,32,95591483.17,1.05586,53.708000000000006,10
+UW-007,senior,10,149604978.18,0.97864375,62.480624999999996,16
+UW-008,senior,15,147917140.52,1.0194352941176472,50.5835294117647,17
+UW-009,junior,2,132422344.6,0.9753249999999999,108.41833333333334,12
+UW-010,mid_level,7,139100034.92,0.9417384615384615,103.18846153846154,13
+UW-011,mid_level,5,157012257.08,0.9184916666666667,74.63583333333334,12
+UW-012,principal,24,218693063.41,0.981040909090909,59.151363636363634,22
+UW-013,mid_level,5,132727524.84,1.0269333333333333,58.1888888888889,9
+UW-014,senior,12,169284464.57,0.96707,61.402499999999996,20
+UW-015,mid_level,4,80224332.59,0.9891833333333334,90.8675,12
+UW-016,junior,1,104277532.1,0.9147,82.99545454545455,11
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