ElevateNow Agentic Recipe

Prior Carrier Loss Runs: From PDF Chaos to Underwriting Intelligence

Every carrier sends loss runs in different formats. Every claim is coded differently. Underwriters spend hours mapping claims to coverage lines. or worse, they don't, and miss the signals that matter most.
Specialty Commercial Municipal & Public Entity 10 min read

The Loss Run Data Gap

Underwriters need to understand an account's loss history before pricing coverage. But prior carrier loss runs arrive as unstructured PDFs with carrier-specific codes, inconsistent formats, and no mapping to your coverage lines.
58
Canonical coverage products in specialty commercial
1,400+
LOB code aliases across carriers
15 min
Average time to manually categorize one loss run
23%
Claims miscategorized under manual review
FORMAT CHAOS

Every Carrier, Different Format

Liberty Mutual uses "AL" for Auto Liability. Travelers uses "CAL". Hartford uses "CommAuto-Liab". The same coverage, three different codes. Multiply by 50 carriers and 58 products.

HIDDEN SIGNALS

Misclassified Claims Hide Risk

A discrimination claim filed under "General Liability" should be EPL. A police excessive force claim coded as "GL" should be Law Enforcement Liability. Carriers often misfile. and underwriters inherit those errors.

COVERAGE GAPS

Uninsured Exposures Go Unnoticed

A $1.1M property claim on a policy with no property coverage isn't a mapping failure. it's a critical finding. But when everything lands in a spreadsheet, these signals get lost.

How Loss Runs Are Processed Today

1

PDF Arrives via Email

Loss run PDF from prior carrier lands in underwriter's inbox. Format unknown until opened.

Pain: No standardization. Each carrier's format requires mental translation.
2

Manual Data Entry

Underwriter or assistant manually keys claims into spreadsheet. Date of loss, description, amounts, carrier codes.

Pain: Time-consuming, error-prone. Typos introduce downstream errors.
3

Code Translation (Mental)

Underwriter mentally maps carrier codes to coverage lines. "CommPkge" → probably Property? Or GL? Context-dependent.

Pain: Tribal knowledge required. New underwriters guess. Veterans inconsistent.
4

Coverage Matching (If Time Permits)

Compare claims against policy coverages to identify relevance. Often skipped under time pressure.

Pain: Coverage gaps invisible. Claims for inactive coverages mixed with active.
5

Pricing Decision

Underwriter prices based on incomplete picture. Some claims miscategorized, some exposures undetected.

Pain: Pricing decisions made on flawed data. Adverse selection risk.

Loss Run Intelligence That Thinks Like an Underwriter

We don't just extract data from PDFs. We map claims to your coverage lines, detect misclassifications carriers missed, flag uninsured exposures, and deliver audit-ready output with full provenance. every mapping traced to evidence.
Stage 1
Intelligent Extraction

Extract claims from any carrier's PDF format. Normalize dates, amounts, descriptions. Capture LOB codes exactly as provided. Confidence scores on every field. when extraction is uncertain, we flag it rather than guess.

Multi-carrier format support
Field-level confidence scores
Automatic insured name matching
85-90%+ extraction accuracy threshold
Stage 2
Canonical Product Mapping

Map carrier codes to your canonical coverage products using a 5-tier algorithm: direct LOB lookup, alias matching, package hint detection, AI-powered inference, and description analysis. Every mapping includes method and confidence.

58 canonical products
1,400+ alias mappings
AI fallback for unknown codes
Method attribution on every claim
Stage 3
Override Intelligence

Detect misclassified claims using 19 EPL indicators, law enforcement keywords, sexual abuse patterns, and more. A claim coded "GL" with "EEOC" or "discrimination" in the description gets reclassified to EPL. because that's what an experienced underwriter would do.

EPL override detection (19 indicators)
Law Enforcement Liability flags
Sexual Abuse & Molestation detection
Priority cascade (SAM > LEL > EPL)
Stage 4
Coverage Gap Detection

Match claims against active policy coverages. When a claim maps to a coverage the account doesn't have, flag it as "Uninsured Exposure". not as a mapping failure. A $1.1M property claim with no property coverage is a critical underwriting finding.

Active product detection (3-tier)
Uninsured exposure flagging
Coverage gap quantification
Premium vs. claims alignment
Stage 5
Reconciliation & Audit Trail

Generate Excel reconciliation with full audit trail: every claim, its original code, mapped product, mapping method, confidence score, and override reason if applicable. Underwriters see exactly why each claim landed where it did.

Claim-level provenance
Excel reconciliation report
Category summaries by product
Year-over-year trending

Trust Through Transparency

Every mapping decision is explainable. Every override is justified. Underwriters don't get black-box outputs. they get recommendations with full reasoning.

Mapping Provenance

Every claim shows how it was mapped: direct lookup, alias match, AI inference, or override. No hidden logic.

Claim X35408: "GL" → EPL
Method: override_epl
Reason: Description contains "DISCRIMINATION"
Confidence: 95%

Override Justification

When the system reclassifies a claim, it shows the trigger keyword and the override rule that applied.

Override Applied: EPL_KEYWORDS
Trigger: "EEOC CLAIM" in description
Original: General Liability
Reclassified: Employment Practices Liability

Confidence Thresholds

Low-confidence mappings are flagged for review. The system recommends. it doesn't decide autonomously on uncertain cases.

Claim X35409: Confidence 62%
Status: FLAGGED FOR REVIEW
Reason: Ambiguous LOB code "MISC"
Suggested: Review description context

Audit-Ready Output

Excel reconciliation includes methodology reference, override priority hierarchy, and execution checklist for compliance review.

Reconciliation Report
├── Claims Detail (with provenance)
├── Category Summary
├── Override Reference
└── Methodology Notes

Beyond Basic Extraction

Capability Traditional Insurtech ElevateNow
PDF Extraction Generic OCR, format-specific Multi-carrier with confidence scores
Code Mapping Simple lookup table 5-tier algorithm with 1,500+ aliases
Misclassification Accepts carrier coding Override intelligence (EPL, LEL, SAM)
Coverage Gaps Not detected Uninsured exposure flagging
Explainability Black box output Full provenance on every claim
Multi-Policy Batch Manual file matching Automatic loss run to policy matching

What This Means for Underwriting

SPEED

15 Minutes → 30 Seconds

Automated extraction, mapping, and reconciliation. Underwriters receive structured output ready for review. not raw PDFs requiring manual processing.

97% reduction in processing time
ACCURACY

Underwriter-Level Mapping

Override intelligence catches what carriers miss. A discrimination claim coded as GL gets reclassified to EPL. because that's where it belongs.

23% fewer misclassified claims
INSIGHT

Coverage Gaps Surfaced

Uninsured exposures flagged before they become surprises. A $1.1M property claim on a no-property policy is a finding, not a failure.

100% coverage gap detection
TRUST

Explainable Decisions

Every mapping includes method, confidence, and reasoning. Underwriters verify rather than wonder. Auditors trace rather than question.

Full provenance on every claim
SCALE

Batch Processing

Process multiple loss runs against multiple policies in a single batch. Automatic matching by insured name. no manual file renaming required.

Unlimited batch volume
GOVERNANCE

Audit-Ready Output

Excel reconciliation with claim-level detail, category summaries, and methodology reference. Ready for compliance review without rework.

Zero rework for audit preparation

Ready to Transform Loss Run Processing?

Join specialty carriers turning PDF chaos into underwriting intelligence. with governance encoded, provenance included, and coverage gaps surfaced.