Bulk Loss Run Extraction for Reinsurance Due Diligence: AI-Driven Risk Assessment at Portfolio Scale

Chief Underwriting Officers face a high-stakes balancing act: price reinsurance programs accurately, move quickly on cedent submissions, and defend portfolio results under audit and regulatory scrutiny. Yet the raw material for this work—loss run reports, cedent loss bordereaux, Schedule F extracts, and claim register exports—arrives in wildly inconsistent formats across dozens or hundreds of cedents. The result is slow, expensive due diligence and avoidable uncertainty around loss development, clash potential, tail risk, and the adequacy of ceded terms.

Nomad Data’s Doc Chat for Insurance eliminates this bottleneck. Purpose-built, AI-powered agents perform bulk loss run data digitization for portfolio review, automatically extracting, normalizing, and validating claims data from unstructured PDFs, spreadsheets, and portals—at speed and scale. Within minutes, underwriting and portfolio teams can ask natural-language questions like “Show incurred-to-paid ratios by accident year for the trucking book” or “List all claims with case reserve spikes exceeding 50% in the last six months,” and get instant, source-cited answers. The outcome: confident decisions, faster quotes, and a defensible reinsurance due diligence process that scales.

Understanding the Reinsurance-Specific Challenge for the Chief Underwriting Officer

In reinsurance, the same metric can be defined a dozen different ways across cedents. “Incurred” might include or exclude ALAE. “Open” could hide multiple reopenings. Policy and claim identifiers change during system migrations. Currency, development, and accounting treatments vary. For a Chief Underwriting Officer (CUO), this inconsistency impacts much more than reporting accuracy—it directly affects price adequacy, capital allocation, and the credibility of treaty and facultative decisions.

Unlike primary carriers who control a single claims platform, reinsurers must synthesize external data across cedents, lines of business, treaty structures, and jurisdictions. A typical renewal season brings thousands of pages of Loss Run Reports, Cedent Loss Bordereaux, Schedule F (NAIC) references for recoverables and aging, and raw Claim Register Exports with idiosyncratic field naming. CUOs need answers to questions such as:

• How do paid, case, and incurred development by accident year compare to prior submissions for this cedent?
• Where are the reserve strengthening events, and do they correlate with cause of loss, line, or severity segments?
• Which ceded claim cohorts exhibit unusually slow closure, high reopen rates, or negative paid development?
• How concentrated is cat-exposed property business by CRESTA/ZIP and attachment relative to modeled views?
• For long-tail casualty, are severity trends, ALAE ratios, and tail factors deteriorating materially year-over-year?

These questions are answerable if the underlying data are accurate and normalized. But getting there, manually, is exactly where underwriting organizations lose precious time and create avoidable leakage.

How It’s Done Manually Today—and Why It Breaks at Scale

Most reinsurance due diligence workflows still rely on domain experts and analysts to triage inbound cedent packages and stitch together a "+/- right" dataset in Excel. The process looks like this:

1. Receive a mix of PDFs (scanned and digital), spreadsheets, and portal extracts covering multiple policy years, LOBs, and treaties.
2. Hand-map columns into a “house” schema—e.g., Paid Indemnity, Paid ALAE, Case Reserve, Total Incurred, Claim Status, Accident Date, Report Date, Policy Number, Treaty ID, Ceded Share, Attachment, Cause of Loss, Cat Code, Exposure Unit, Currency, Jurisdiction.
3. Clean and reconcile: deduplicate claim IDs, fix negative adjustments and reversals, align reopenings, convert currencies, and reconcile totals to cedent-level bordereaux summaries.
4. Attempt to interpret missing fields (e.g., whether ALAE is included in Incurred, whether “Expense” is ULAE vs ALAE, whether coverage is claims-made vs occurrence, or how deductibles/SIRs are applied).
5. Run pivot tables and triangles to estimate development, severity distributions, closure rates, and tail risk; then draft a pricing narrative and recommendations.

This manual flow is slow and brittle. Analysts inevitably sample when they should be exhaustive. Inconsistencies slip through. Adjustments lag, so totals no longer reconcile with the latest cedent view. And every time a cedent “refreshes” a file with a slightly different layout, most of the work must be repeated. When the CUO needs a portfolio roll-up by line, attachment, and vintage—across 30 cedents—the wheels come off.

AI to Extract Claims from Loss Runs for Reinsurance: How Doc Chat Works End-to-End

Doc Chat operationalizes the exact steps seasoned reinsurance teams follow—without the bottlenecks. It ingests entire submission packages and performs AI to extract claims from loss runs for reinsurance at enterprise scale. Here’s how it transforms the due diligence workflow:

1) Bulk Ingestion Across Formats

Whether your cedents send Loss Run Reports as scanned PDFs, native PDFs, Excel, CSV, or mixed-file portals, Doc Chat ingests them all. It also processes Cedent Loss Bordereaux, Claim Register Exports, policy schedules, and Schedule F-related exhibits used to validate recoverables. The system handles thousands of pages and files per cedent—then scales to hundreds of cedents—without adding headcount.

2) Smart Classification and Field Discovery

Using insurance-specific language models, Doc Chat automatically classifies document types and discovers fields even when headers vary: “Total Inc.” vs “Incurred,” “Case Reserve” vs “Outstanding,” “Expense” vs “ALAE,” “File Status” vs “Open/Closed,” and so on. It recognizes treaty context—quota share vs XOL, per risk vs cat, facultative vs treaty—and maps fields accordingly.

3) Normalization to Your Canonical Schema

Nomad trains Doc Chat on your house standards so that every cedent’s file lands in the same, rigorously defined structure. The system will normalize ceded loss data with AI, including:

• Standardizing paid/indemnity/ALAE fields and computing incurred consistently.
• Mapping claim statuses and sub-statuses into unified, auditable categories.
• Resolving currency and unit inconsistencies; time-zone and date normalization (Accident, Report, Reopen, Close).
• Deriving attachment, retention, and ceded share from narrative notes when absent as structured fields.
• Reconciling claim-level totals to cedent-level bordereaux and Schedule F references for recoverables.
• Linking policy, claim, and treaty identifiers across cedent systems and time periods to eliminate duplicate chains.

4) Quality Checks and Explainability

Every extraction is tied to page-level citations and source cells. If a cedent’s submission has an apparent mismatch—e.g., incurred less than paid, reserve spikes without paid movement, or missing ALAE—Doc Chat flags it, traces it back to the source, and provides a suggested resolution path. Underwriters and portfolio analysts see what changed, why, and where it came from.

5) Analysis at Portfolio Scale

Once normalized, Doc Chat supports automated loss bordereaux analysis reinsurance for the full submission set. Ask questions in plain language and receive spreadsheet-ready outputs and visualizations:

• Accident year triangles for paid, case, and incurred by cedent, line, and treaty; closure and reopen rates.
• Severity and frequency trends with outliers, reserve strengthening events, and driver-based segmentation (cause of loss, jurisdiction, peril, claimant type).
• Cat exposure intelligence: event tagging, CRESTA/ZIP aggregation, attachment interaction, clash potential.
• Long-tail diagnostics: ALAE ratios, incurred-to-paid, age-of-claim, settlement patterns, and tail-factor benchmarking.
• Reconciliation reports that tie back to cedent summaries and Schedule F recoverable views.

Because Doc Chat understands underwriting context, it can generate pricing-ready views and export directly to your data warehouse or pricing models.

What Doc Chat Extracts from Each Submission

For CUOs who need confidence that the data are both thorough and decision-grade, Doc Chat is designed to surface everything material to coverage, liability, and damages—and the reinsurance-specific levers that influence price and capital. Typical extracted and derived fields include:

• Claim identifiers and lineage across cedent systems (claim ID, occurrence ID, related claim chains, reopen flags)
• Dates: Accident/Occurrence, Report, Reopen, Close, Latest Transaction
• Financials: Paid Indemnity, Paid ALAE, Case Indemnity, Case ALAE, Total Paid, Total Case, Total Incurred
• Adjustments: Negative payments, recoveries, subrogation, salvage, commutations
• Policy and Treaty: Policy number, policy year, claims-made vs occurrence, attachment, retention/SIR, treaty ID, ceded share, layer
• Cause of loss and peril coding, jurisdiction, plaintiff-friendly venues, injury types for casualty
• Cat indicators and event tags, CRESTA/ZIP, occupancy, construction class (when present)
• Status and sub-status mapping, closure and reopen metrics, age of claim, reserve movement logic
• Exposure units (e.g., vehicles, payroll, TIV), rating class, and schedule modifiers when available
• Reinsurance recoverables alignment and aging cross-checked against Schedule F

The system doesn’t just “read” files—it applies reinsurance-specific inference rules to convert messy, heterogeneous inputs into reliable pricing and portfolio analytics. As Nomad describes in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, effective document intelligence requires inferring concepts that are not explicitly written down, then encoding institutional judgment into AI agents.

Bulk Loss Run Data Digitization for Portfolio Review: From Days to Minutes

The operational uplift comes from doing in minutes what used to take days. Doc Chat performs bulk loss run data digitization for portfolio review and surfaces a clean, normalized dataset your pricing, exposure management, and capital teams can trust. From there, CUOs can move directly to the high-value questions that matter:

• Are we seeing calendar-year reserve strengthening at specific cedents or in specific casualty lines?
• How do incurred-to-paid ratios compare to our underwriting assumptions by cohort and vintage?
• Which treaties exhibit adverse development patterns that warrant adjusted LDFs or attachment changes?
• Where are we overexposed by region/peril and undercompensated at current rate-on-line?

Nomad’s clients have experienced the same step-change in speed documented in Reimagining Claims Processing Through AI Transformation: multi-thousand-page document sets summarized and made queryable in seconds, not weeks. In reinsurance, this means CUOs can both accelerate renewal decisions and improve the quality of those decisions.

Normalization That Matches How Your Underwriters Think

Many tools stop at extraction. Doc Chat keeps going until the data align with your underwriting worldview. That includes custom mapping of definitions like “incurred,” the treatment of ALAE in rate vs exposure, adjustments for reopened claims, and logic for inferring missing fields from narratives. Using Nomad’s “train-on-your-playbook” approach (detailed in AI’s Untapped Goldmine: Automating Data Entry), Doc Chat is configured to your canonical schema, your QA rules, and your exception handling, then enforced consistently across every cedent file.

Concrete examples of normalization include:

• Recasting cedent definitions where “expense” ambiguously mixes ALAE and ULAE, with audit trails.
• Inferring attachment points and ceded shares from schedule narratives when fields are missing, surfacing source citations.
• Standardizing casualty injury coding and mapping venue information to your severity risk tiers.
• Reconciling claim subtotals to bordereaux rollups with variance explanations and confidence scoring.

Automated Loss Bordereaux Analysis Reinsurance: Actionable Insights for Pricing and Capital

With normalized data, Doc Chat powers automated loss bordereaux analysis reinsurance that CUOs can rely on during underwriting committees, portfolio steering, or board updates. Outputs include:

• AY/DY triangles with customizable development logic and LDF benchmarking to internal and market views.
• Rate-adequacy flags by treaty layer using observed severity and frequency drift relative to priced assumptions.
• Attachment effectiveness analysis in property cat and per-risk with modeled-to-actual reconciliation by peril and region.
• Casualty tail diagnostics: ALAE creep, settlement velocity, claim age distributions, and reopen propensity by class.
• Recoverables reasonableness checks and Schedule F aging alignment to support counterparty credit oversight.

Because every metric is traceable back to the page and cell of origin, CUOs can stand behind decisions during broker negotiations, audits, and regulatory examinations—without re-running the same manual analyses repeatedly.

Business Impact: Time, Cost, Accuracy, and Speed-to-Quote

The value of automating reinsurance loss run extraction and normalization compounds across the organization:

• Time Savings: Doc Chat ingests and processes entire submission packages across all cedents in minutes. Underwriters regain hours to focus on judgment and negotiation rather than data janitorial work.
• Cost Reduction: Fewer manual touchpoints and reduced reliance on overtime or expensive external data-cleansing projects; portfolio-level due diligence scales without headcount increases.
• Accuracy Improvements: Consistent definitions, fewer transcription errors, and a comprehensive view that eliminates blind spots and leakage.
• Faster Speed-to-Quote: CUOs move from data wrangling to decision-making quickly, giving your firm a competitive edge in tight renewal windows.

These outcomes mirror the broader, documented gains Nomad clients see when they eradicate manual document bottlenecks (see The End of Medical File Review Bottlenecks). When the reading and reconciling are automated, experts can apply their expertise where it matters most.

Why Nomad Data’s Doc Chat Is the Best-Fit Solution for Reinsurance CUOs

Reinsurance underwriting requires nuance. Doc Chat is built for the volume and complexity of insurance documentation and tuned to the reinsurance context. What sets Nomad apart:

• Purpose-built for Insurance and Reinsurance: Doc Chat recognizes treaty context, applies cedent-to-canonical mapping, and incorporates underwriting heuristics into extraction and normalization.
• The Nomad Process: We train Doc Chat on your playbooks, definitions, and QA rules so it works like your team—only faster. Outputs reflect your schema and your standards.
• Volume and Speed: Ingest entire claim files and full cedent submissions (thousands of pages) and get answers in minutes—no headcount addition.
• Real-Time Q&A with Page-Level Citations: Ask, “Show all claims above $500K incurred with reserve increases in the last quarter,” and click back to the exact source page.
• White-Glove Delivery: A dedicated team sets up schema mappings, validation rules, and output templates. We do the heavy lifting so underwriters don’t have to.
• Rapid Implementation: Most clients are live in 1–2 weeks, with initial use via drag-and-drop and full integration following shortly after.
• Security and Compliance: Enterprise-grade governance, SOC 2 Type II practices, and auditable outputs suitable for regulators, reinsurers, and internal risk teams.

As covered in AI for Insurance: Real-World AI Use Cases Driving Transformation, the winning carriers and reinsurers aren’t those with the flashiest demos—they’re the ones who embed AI into core underwriting workflows with transparency and control. That’s Doc Chat’s design ethos.

From Document Chaos to Data Products: Implementation and Workflow

Getting started is straightforward. During discovery, Nomad’s team captures your canonical schema, validation rules, data destinations, and preferred analysis views. From there, Doc Chat runs as a hands-off service or within your team’s daily workflow:

1. Intake: Drag-and-drop cedent packages to the web interface or feed them via S3, SFTP, email, or API.
2. Classification and Extraction: Files are auto-typed (loss runs, bordereaux, claim registers) and fields are extracted and cross-referenced.
3. Normalization and QA: Data are mapped to your schema with business-rule validation, currency/date normalization, and reconciliation to cedent summaries.
4. Output and Analysis: Receive CSV/Parquet feeds to Snowflake, BigQuery, or your pricing tools; use Doc Chat’s Q&A to interrogate the portfolio instantly.
5. Review and Sign-Off: Underwriters and portfolio managers review flagged exceptions with citations before finalizing the pricing package.

No data science team required. As highlighted in our perspective on Beyond Extraction, Nomad created a new professional discipline that fuses investigative interviewing with AI engineering—so your tacit underwriting rules become operational in software.

Use Cases That Matter to CUOs

1) Treaty Renewal and New Business Due Diligence

Automate the ingestion of every cedent submission and generate decision-grade analytics in days instead of weeks. Quote more business confidently with detailed evidence of trends, development, and tail risk—backed by citations to each cedent’s source documents.

2) Portfolio Steering and Capital Allocation

Continuously monitor calendar-year development, rate adequacy by line and layer, and attachment effectiveness. Align capital and retro purchases to observed risk, not anecdote.

3) Retrocession and Reinsurance-to-Reinsurance

When you cede your own risk, Doc Chat’s normalized views simplify communication to retro markets and improve pricing leverage with transparent, consistent analytics.

4) Counterparty Credit and Recoverables Oversight

Cross-check claim-level paid and incurred against cedent summaries and Schedule F aging. Build a defensible, ongoing view of recoverables health and counterparty exposure.

5) Regulatory and Audit Readiness

Maintain an auditable pipeline from page-level sources to portfolio-level decisions. When examiners ask “Where did that number come from?” you can show them—and bind faster next time.

Answer Engine Optimization: How CUOs Can Find and Use This Capability

Reinsurance leaders increasingly search for precise solutions that match their problems. Doc Chat is discoverable for the exact needs CUOs and portfolio teams articulate:

• “AI to extract claims from loss runs for reinsurance”
• “bulk loss run data digitization for portfolio review”
• “normalize ceded loss data with AI”
• “automated loss bordereaux analysis reinsurance”

Each of these phrases maps directly to how Doc Chat works and the measurable outcomes it delivers. If your team is asking these questions, you’re ready to move from manual processes to AI-driven due diligence with full control and traceability.

Security, Governance, and Buy-In

Doc Chat is designed for enterprise adoption: SOC 2 controls, single-tenant data isolation options, strict access controls, and full audit trails. Crucially, we emphasize transparency and human oversight. As outlined in Reimagining Claims Processing Through AI Transformation, the winning model keeps humans in the loop: the AI reads, extracts, reconciles, and recommends; the underwriter decides.

Measured Outcomes CUOs Can Count On

Our reinsurance customers report consistent, quantifiable improvements after deploying Doc Chat across submissions:

• 50–90% reduction in time-to-analysis from initial intake to pricing-ready data.
• Significant error reduction through consistent definitions and automated reconciliation.
• Faster time-to-quote leads to higher hit ratios, particularly on time-sensitive facultative and cat placements.
• Improved portfolio outcomes by spotting adverse development early and adjusting attachment or rate-on-line proactively.

These gains are consistent with the broader efficiency and quality benefits Nomad has documented across insurance document workflows, including medical file review and claims intake (The End of Medical File Review Bottlenecks).

Implementation Timeline: 1–2 Weeks to Live

Doc Chat meets CUOs where they are. A typical rollout follows this pattern:

1. Week 1: Define your canonical schema and QA rules; establish intake and output destinations; run initial sample cedents.
2. Week 2: Tune mappings based on your underwriting feedback; enable portfolio-level dashboards and Q&A; go live for the renewal season.

From the first day, teams can drag-and-drop documents into Doc Chat and get instant answers. Integration with claims and pricing systems follows as desired, usually in a matter of days. Details on this phased approach mirror what we describe across use cases in AI for Insurance: Real-World AI Use Cases Driving Transformation.

Ready to Scale Your Reinsurance Due Diligence

The reinsurance file review bottleneck is no longer inevitable. With Doc Chat, CUOs can move beyond ad hoc spreadsheets and inconsistent cedent definitions to a standardized, explainable view of claims and exposure risk across the entire portfolio. If your team is ready to accelerate decision-making while improving rigor, it’s time to adopt Doc Chat for Insurance.

In summary: Doc Chat automates extraction from Loss Run Reports, Cedent Loss Bordereaux, Schedule F (NAIC), and Claim Register Exports, then normalizes and analyzes the results—so CUOs can price with confidence, defend decisions with citations, and steer portfolios proactively. That’s reinsurance due diligence at the speed and scale your team needs.