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

Reinsurance risk analysts are under intense pressure to evaluate ceded portfolios faster and with far more rigor than traditional manual review allows. On any given placement or renewal, you may receive hundreds of Loss Run Reports, monthly cedent loss bordereaux, ad hoc claim register exports, and statutory attachments like NAIC Schedule F—each in a different layout, currency, and taxonomy. The core challenge is simple to state and difficult to solve: how do you extract, normalize, and analyze loss history at scale to quantify trends, tail risk, and treaty fit—without weeks of manual data wrangling?

Doc Chat by Nomad Data was built for exactly this problem. It is a suite of purpose-built, AI-powered agents that ingest entire submissions (thousands of pages at a time), extract claim-level facts from heterogeneous documents, normalize the fields to your cedent-level and portfolio-level schema, and surface the insights that drive reinsurance pricing and capacity decisions. Unlike generic OCR or template tools, Doc Chat learns your playbooks and applies your definitions for paid, case reserves, ALAE vs. ULAE, occurrence vs. claims-made, and more—so your portfolio review moves from weeks to minutes with full auditability.

The Reinsurance-Specific Nuances a Reinsurance Risk Analyst Must Navigate

Loss analysis in reinsurance isn’t a basic data entry exercise; it’s multi-dimensional risk triage across cedents, treaties, and time. Each cedent structures loss runs differently. One includes separate ALAE and DCC fields; another nets expense into indemnity; a third mixes occurrence and claims-made on the same tab. Bordereaux formats shift monthly. Claim register exports arrive with new columns and cryptic cause codes. And when you broaden to treaty-level analysis, you must translate cedent-level activity to layers and structures—quota share vs. excess of loss, per-occurrence vs. aggregate, clash and cat. Meanwhile, Schedule F brings a separate lens: recovering insight on ceded/assumed balances, counterparties, and recoverable aging to judge credit and operational quality.

For a Reinsurance Risk Analyst, this complexity has hard consequences:

Trend detection is fragile when cedents define paid vs. incurred vs. case reserves differently across Loss Run Reports, cedent loss bordereaux, and claim register exports.
Comparability breaks down if accident year vs. underwriting year vs. report year aren’t reconciled—especially across claims-made vs. occurrence.
Cat vs. attritional separation is inconsistent; large-loss thresholds vary, and catastrophe identifiers aren’t standardized.
Multi-currency cedents require precise FX policies by as-of date and ledger, or you misstate severity and trend.
Partial duplication across a loss run and a bordereau (or multiple monthly bordereaux) can double-count paid/LAE if not deduped and time-aligned.

All of this sits inside tight underwriting calendars and broker timelines, where missing one subtle exclusion or mis-mapping one expense field can swing price adequacy, capacity, and aggregate management.

How Loss Runs Are Handled Manually Today—and Why It’s Not Sustainable

Most teams still tackle bulk loss run processing with a patchwork of email, shared drives, spreadsheets, and ad hoc macros. A typical flow looks like this:

Intake and triage: Analysts download 200–600 PDFs and Excel files, rename them, and track intake in spreadsheets. They skim for the right tabs—paid, incurred, case reserves, ALAE/ULAE—and flag missing fields or dates via email back to the cedent or broker.

Extraction: Junior staff rekey fields claim by claim from PDFs or clean inconsistent columns from Excel. A separate teammate tries to reconcile fields to the team’s canonical schema: claim number, policy number, accident date, report date, cause/peril, line of business, paid indemnity, paid ALAE, case indemnity, case ALAE, recoveries, subrogation, salvage, and financials as-of date.

Normalization: Teams maintain fragile lookup tables for cedent-specific codes (causes, perils, LOBs), try to align occurrence vs. claims-made conventions, and wrestle with mixed time bases (AY/UWY/RY). They also apply FX conversions with static rates if the as-of is unclear.

Quality control: A senior analyst spot-checks totals against the PDF Loss Run Reports or summary pages. Deduplication is handled with VLOOKUPs and subjective judgements—often missing subtle duplicates where claim identifiers changed.

Analytics: Only after all of that do analysts build large-loss listings, trend triangles, loss development snapshots, and tail analyses. The calendar is gone, and the underwriting committee wants results yesterday.

This manual approach is slow, expensive, and risky. Fatigue leads to data entry errors. One off-by-one column shift breaks months of effort. Worse, valuable time is lost to compilation instead of interpretation—exactly what Reinsurance Risk Analysts are hired to do.

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

Doc Chat automates this pipeline with reinsurance-grade fidelity. It ingests entire submission folders—Loss Run Reports (PDF, XLS/XLSX, CSV), monthly cedent loss bordereaux, bespoke claim register exports, and Schedule F (NAIC) attachments—then orchestrates automated extraction, normalization, reconciliation, and analysis in your exact schema.

Core capabilities include:

Document understanding across formats: Doc Chat reads unpredictable tables, footnotes, and page-level roll-ups; it can parse multi-tab spreadsheets and identify which tab maps to which semantic category (e.g., Paid-to-Date vs. Incurred-to-Date).
Field-level extraction and mapping: It identifies and normalizes claim-level fields to your data dictionary—accident date, report date, line/business segment, peril/cause, paid indemnity, paid ALAE, case indemnity, case ALAE, recoveries, legal expenses, subro, salvage, and more.
Schema and code normalization: Cedent-specific cause codes are mapped to your standard peril taxonomy; occurrence vs. claims-made is inferred when not explicit; AY/UWY/RY alignment is performed per your rules.
Currency handling and FX policies: The agent recognizes currency per file, tab, or row and applies the correct FX conversion as-of the loss-run date or a user-provided valuation date.
Deduplication and time alignment: Multiple monthly bordereaux and updated Loss Run Reports are linked to the same claims; Doc Chat avoids double-counting and assembles accurate development over time.
Reconciliation: Extracted totals are reconciled to file-level summaries and cross-checked against Schedule F recoverable views where relevant.
Analytics-ready outputs: Cleaned, standardized claim-level tables feed your pricing workbooks, actuarial triangles, and portfolio dashboards without manual rework.

Crucially, Doc Chat supports real-time Q&A across large submission sets: ask, “List all claims with case+paid incurred > $500,000 in AY 2017–2019 for GL,” or “Show cat-tagged claims above the treaty attachment,” and get instant answers with page-level citations to the originating Loss Run Reports and tabs. This is a fundamentally different experience from scrolling through PDFs. As highlighted in our piece Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, the value is not just finding fields—it’s inferring meaning and applying your unwritten rules consistently at scale.

Bulk Loss Run Data Digitization for Portfolio Review—Built for Speed, Accuracy, and Scale

If your search is “bulk loss run data digitization for portfolio review,” you’re looking for more than OCR. You need a workflow engine that can process hundreds of cedent files in parallel, manage exceptions, and deliver analytics-ready outputs under tight market deadlines. Doc Chat is engineered for enterprise-grade throughput with robust queuing, retries, and validation so you can process surge volumes without adding headcount.

Doc Chat’s ingestion pipeline automatically:

Classifies each document type—cedent loss bordereaux, Loss Run Reports, claim register exports, and Schedule F—and routes it to the right extraction template.
Identifies missing or ambiguous fields (e.g., absent accident date, unclear ALAE/ULAE treatment) and flags the file for review or broker follow-up.
Builds a unified claim index across files so monthly development and re-statements align to a single claim identity—even when claim numbers evolve.
Constructs normalization logs showing how each cedent’s codes were mapped to your canonical taxonomy so your oversight and actuarial teams have a defensible audit trail.

Because Doc Chat is trained on your definitions and thresholds, it automatically computes the portfolio views reinsurers care about: large-loss listings by treaty layer, AY/UWY triangles, cat vs. attritional splits, and development snapshots at valuation dates you specify.

Normalize Ceded Loss Data with AI—Your Taxonomy, Your Rules

The phrase “normalize ceded loss data with AI” only matters if the normalization matches your institution’s rules. Doc Chat is configured during a short onboarding to capture your exact approach to:

Financial fields: How to treat DCC vs. A&O, whether to net recoveries into paid, and which figures to surface in incurred-to-date.
Time bases: AY vs. UWY vs. RY priority; which to use for triangles and which for trend; how to treat reopened claims.
Treaty mechanics: Apply deductibles, co-participations, aggregate retentions, and corridor provisions; split by layer and occurrence.
Cat flags: Recognize catastrophe identifiers even when not explicit; use date, peril, and geography to infer cat or clash.
Peril and LOB mapping: Standardize cause codes and LOBs to your enterprise taxonomy; maintain cedent-specific crosswalks with version control.

Every mapping and rule is versioned and fully traceable. You can see for any claim exactly how a cedent’s raw fields were translated into your standardized structure—critical when negotiating with brokers or presenting to underwriting committees.

Automated Loss Bordereaux Analysis Reinsurance—From Monthly Feeds to Treaty Decisions

If you’re searching for “automated loss bordereaux analysis reinsurance,” monthly bordereaux are likely a bottleneck. Doc Chat reads the rolling feeds, aligns them to prior months, consolidates paid and case development, and isolates what’s genuinely new. It builds time series by claim and by treaty, computes emergence factors, and flags volatility that warrants pricing adjustments or claim-by-claim review. You can ask, “What’s driving the unexpected deterioration in AY 2020 GL on Cedent C’s XoL 20 xs 30?” and immediately see the claim list with movement reconciled to prior valuations.

Doc Chat also reconciles bordereaux aggregates to Loss Run Reports and validates recoverable expectations against Schedule F patterns where applicable. The result is a defense-ready, transparent view of what changed, when, and why—without weeks of spreadsheet archaeology.

What This Automation Means for Business Impact

For reinsurance teams, the impact is measurable across cycle time, cost, and quality:

Time savings: Clients routinely compress a multi-week loss run compilation into hours. As we describe in The End of Medical File Review Bottlenecks, Doc Chat processes hundreds of thousands of pages per minute. Similar accelerations play out in claims/loss-run ingestion: it never tires, and it treats page 1,500 with the same attention as page 1.

Cost reduction: Manual loss run digitization is a classic data entry problem at scale. In AI’s Untapped Goldmine: Automating Data Entry, we outline how intelligent document processing typically automates the majority of repetitive extraction and yields rapid ROI by eliminating low-value manual touchpoints and overtime.

Accuracy improvements: Human accuracy degrades with volume and fatigue. Doc Chat applies the same rigor to every row, enforces your definitions consistently, and provides page-level source citations for every extracted value—reducing leakage and rework while improving underwriting confidence.

Scalability and surge handling: Market windows are unforgiving. Whether you receive 50 cedent files or 500 on the same day, Doc Chat scales horizontally to meet demand without you having to staff up.

Why Nomad Data Is the Best Solution for Reinsurance Loss Run Automation

Doc Chat is not a one-size-fits-all OCR. It’s a custom, white‑glove solution tuned to reinsurance workflows:

Built for volume and complexity: We ingest entire submission folders—loss runs, bordereaux, Schedule F, claim register exports—and handle all the edge cases: mixed currencies, shifting code sets, revised development, reopened claims, and variant cedent conventions.

The Nomad process: Our team interviews your underwriters, actuaries, and Reinsurance Risk Analysts to codify your unwritten rules—the exact way you treat ALAE, define cat, or compute incurred. Those rules are encoded and version-controlled so your process is institutionalized and scalable. See why this matters in Beyond Extraction.

Real-time Q&A with traceability: Ask portfolio questions in plain language and get answers with citations back to the source page or cell, streamlining oversight and internal review. This is the same paradigm that helped accelerate complex claims at GAIG—read the GAIG webinar recap.

White-glove implementation in 1–2 weeks: We bring prebuilt reinsurance mappings and accelerate onboarding so you see value fast. Integrations with pricing workbooks, data lakes, or BI tools are typically completed in parallel without disrupting your current workflow.

Security and compliance: Nomad Data maintains SOC 2 Type 2 controls. We provide full document-level traceability and do not train on your data unless you explicitly opt in—key requirements for reinsurers handling sensitive claim registers and bordereaux.

From Documents to Decisions: A Closer Look at the Doc Chat Pipeline

Here’s how Doc Chat converts unstructured submission chaos into precise, portfolio-ready intelligence for reinsurance:

1) Intake and classification: Drag-and-drop a submission directory or point to an S3 bucket. Doc Chat classifies files into Loss Run Reports, cedent loss bordereaux, claim register exports, and Schedule F. It tags timeframes (valuation date, as-of date) and identifies the cedent, line of business, and treaty context when available.

2) Extraction: Tables are read even when headers are inconsistent or split across pages. The system recognizes synonyms (e.g., DCC vs. ALAE, LAE vs. total expense) and aligns columns with your expected fields.

3) Normalization and mapping: Cedent-level codes (LOB, cause, cat flags) are mapped to your enterprise taxonomy. FX conversions are applied as-of the valuation date. AY/UWY/RY are computed or verified per your priority rules.

4) Deduplication and longitudinal assembly: Multiple monthly bordereaux and updated loss runs are reconciled to avoid double counting and to assemble claim development through time.

5) Reconciliation and validation: File totals reconcile to cedent summaries; variances are flagged with “explain” links that open the exact rows driving the difference. Where relevant, recoverable views reference Schedule F patterns for consistency checks.

6) Analytics and Q&A: Outputs feed your pricing models, dashboards, and actuarial triangles. You can immediately ask, “Which claims breach the 10 xs 10 layer?” or “Show attritional emergence for AY 2018–2021 by cedent and LOB,” and navigate to the documented sources with one click.

Examples of Questions Reinsurance Risk Analysts Can Ask Doc Chat

Once your submission is ingested, you can interrogate it like a seasoned file reviewer—at portfolio scale:

“List all claims with incurred-to-date > $1M in AY 2019–2020 for GL across Cedent A and Cedent B; include paid vs. case split and latest valuation date.”
“For Cedent C’s excess casualty program, show claims attaching or breaching the 20 xs 30 layer and compute contribution by AY.”
“Identify cat-tagged losses in 2021; reconcile to monthly bordereaux and highlight the delta from the last submission.”
“Normalize ceded loss data with AI for workers’ compensation cause codes and produce a severity distribution by cause group.”
“Automated loss bordereaux analysis reinsurance: show monthly emergence for AY 2020 by LOB, cedent, and layer.”

Each answer is accompanied by line-by-line citations to the originating Loss Run Reports, cedent loss bordereaux, or claim register exports, so your underwriters, actuaries, and management can verify instantly.

Tying in Schedule F (NAIC) for Cross-Checks

While Schedule F is often analyzed by cedents and regulators, reinsurers use it to validate counterparties and recoverable dynamics. Doc Chat pulls Schedule F attachments from submissions, then cross-references patterns such as recoverables and counterparties to spot inconsistencies with the claimed ceded balances and loss development. This adds an additional governance layer to your due diligence, especially when vetting new cedents or large program shifts.

Quantifying Tail Risk and Trend—Without Waiting on Manual Triangles

Doc Chat doesn’t stop at digitization. It computes trend views and tail diagnostics immediately:

Attritional vs. large loss split: Identify attritional bandwidth and isolate large-loss drivers by AY, LOB, peril, and cedent.

Development snapshots: Compare point-in-time incurred and paid across valuation dates to understand emergence and reserve adequacy patterns.

Layer behavior: Attribute losses to treaty layers, reveal potential clash/cat exposure, and highlight upward trend risk (e.g., severity creep in GL or WC).

Volatility flags: Automatically flag cedents whose development volatility deviates from historical norms, prompting deeper review before binding capacity.

Security, Auditability, and Regulator-Ready Traceability

Reinsurance due diligence demands defensible decisions. Doc Chat is built with audit in mind:

Page- and cell-level citations for every extracted value—no black-box summaries.
Versioned mappings showing how cedent codes and fields are translated to your taxonomy, with full change history.
SOC 2 Type 2 controls and a privacy model that keeps your data isolated; model training on your documents is opt-in only.
Comprehensive logs for who did what, when—supporting internal review, reinsurer committees, and regulator questions.

This transparency is why carriers and reinsurers trust Nomad to automate high-stakes workflows. As the GAIG webinar recap highlights, link-backed answers transform adoption by making validation trivial.

Implementation: White-Glove, 1–2 Week Timeline to Value

Nomad’s onboarding is deliberately fast and collaborative:

Week 1: We gather sample Loss Run Reports, cedent loss bordereaux, claim register exports, and any Schedule F attachments you routinely receive. We interview your Reinsurance Risk Analysts, underwriters, actuaries, and operations to capture the exact playbook you follow—definitions for paid vs. incurred, expense treatment, AY/UWY/RY priorities, FX rules, and large-loss thresholds.

Week 2: We configure the Doc Chat agents, test against known cases, and review results with your team. We refine edge cases and finalize your normalized output format (CSV, Parquet, database table), plus any push to BI or pricing tools. Users can begin drag-and-drop workflows immediately; APIs and system integrations are added as desired.

Because Doc Chat is purpose-built for insurance documents and has production-grade pipelines, you realize value in days—not quarters. That is consistent with the outcomes we discuss across our blog, including Reimagining Claims Processing Through AI Transformation.

A Realistic Scenario: 20 Cedents, 480 Files, 14 Days to Quote

Imagine renewal season. Your team receives:

20 cedents across GL, WC, Auto Liability, Property
480 files: a mix of PDFs and Excel Loss Run Reports, monthly bordereaux, claim register exports, and supporting Schedule F attachments
3 reporting currencies and shifting valuation dates

Using manual processes, a squad of analysts spends two weeks normalizing and deduping these files before the first large-loss listing is trustworthy. With Doc Chat, you drag-and-drop the folder, verify a few flagged exceptions, and run portfolio views the same afternoon. Underwriters focus on interpretation: Is severity creeping in AY 2018+? Which cedent drives unexpected emergence? Which layer now sees the heaviest contribution? You arrive at the underwriting committee with transparent, link-backed evidence—not a maze of spreadsheets.

How This Fits Your Operating Model—And Your Team

Doc Chat does not replace your experts; it amplifies their impact. As we argue in our AI for Insurance article, the win is not automating judgment; it’s eliminating the rote work that delays judgment. Your Reinsurance Risk Analysts spend their time asking sharper questions, exploring drivers of trend and tail, and aligning risk appetite with real-world loss behavior—across the entire portfolio, not just the handful of cedents the calendar allows.

Answers to Common Questions from Reinsurance Risk Analysts

How does Doc Chat handle ambiguous or missing fields? It flags missing or inconsistent items (e.g., accident date missing, ALAE not distinguished from total expense) and routes the file for quick review or broker follow-up. Your rules define whether to impute, exclude, or hold for resubmission.

Can we use our own peril and LOB taxonomy? Yes. We encode your crosswalks and maintain versioned mappings by cedent. When a cedent updates their codes, we update your crosswalks and document the change.

What about currency? We apply your FX policy using the valuation date (or a date you specify), supporting multi-currency submissions and mixed-currency tabs.

How do we avoid double-counting across monthly bordereaux and updated loss runs? Doc Chat builds a master claim index and time-aligns development across sources. It recognizes the same claim even when IDs evolve.

Can Doc Chat compute treaty-level views (e.g., 20 xs 30)? Yes. It applies your attachment and limit logic, co-participations, aggregates, and corridors—then attributes losses by layer and AY/UWY to feed pricing decisions.

Do we get line-by-line evidence? Every output includes links back to the originating row or page, so audits and committee reviews are faster and more defensible.

Why Now: The Competitive Edge of Automated Loss Run Review

The reinsurance market moves quickly. Teams that rely on manual compilation are forced to either cut scope (review fewer cedents in depth) or cut corners (accept unverified mappings and totals). With Doc Chat, you hold a higher analytical bar without sacrificing speed. You can analyze more cedents, more deeply, and still meet broker timelines. You can maintain consistent underwriting discipline even at peak volumes.

Get Started—Turn Submissions into Portfolio-Ready Insight

If you’re searching for “AI to extract claims from loss runs for reinsurance,” “bulk loss run data digitization for portfolio review,” “normalize ceded loss data with AI,” or “automated loss bordereaux analysis reinsurance,” you’re exactly the audience Doc Chat was built to serve. Upload a recent submission and see your portfolio views materialize with citations in minutes. Then decide how you’ll spend the time you’ve won back: investigating trend drivers, calibrating price adequacy, and refining capacity and attachment points.

Learn more about Doc Chat’s insurance capabilities and request a demonstration here: Doc Chat for Insurance.