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

Reinsurers live and die by the quality, completeness, and comparability of cedent-provided loss information. Yet any Reinsurance Risk Analyst knows that loss runs, cedent loss bordereaux, Schedule F (NAIC) tie-outs, and claim register exports arrive in wildly different formats, with different valuation dates, currencies, reserve philosophies, and line-of-business definitions. The challenge isn’t just reading the documents—it’s turning hundreds of inconsistent submissions into a clean, unified dataset you can trust for portfolio-level decisions.

Nomad Data’s Doc Chat automates this exact problem. Purpose-built for high-volume, high-variability insurance documentation, Doc Chat ingests entire submission packets—PDFs, spreadsheets, scans, and emails—and performs end-to-end extraction, normalization, and analysis so reinsurers can evaluate loss history, trends, and tail risk in minutes, not weeks. If you are evaluating AI to extract claims from loss runs for reinsurance, or you’re planning bulk loss run data digitization for portfolio review, Doc Chat provides a turnkey, auditable, and defensible solution designed around reinsurance workflows. Learn more about Doc Chat for insurance here: Doc Chat by Nomad Data.

The Reinsurance Due Diligence Challenge: Loss Run Chaos at Portfolio Scale

In reinsurance, due diligence frequently involves hundreds of cedent files arriving over a compressed timeframe. Each file may include a different mix of Loss Run Reports, cedent loss bordereaux, claim register exports, and sometimes Schedule F (NAIC) support or tie-outs. A single cedent might shift report layouts from quarter to quarter. Others provide PDFs generated from legacy policy admin systems with varying column names and inconsistent definitions (e.g., ALAE embedded vs separated, paid vs incurred treatment of LAE, or policy vs occurrence-level reporting). Without automation, the Reinsurance Risk Analyst is stuck in spreadsheet triage—mapping, deduplicating, reconciling, and constantly rechecking calculations.

Complexity compounds across portfolios. You must align valuation dates, adjust for currency differences, detect reopenings, track ultimate development, reconcile triangles, and map ceded portions given diverse treaty structures (quota share, per risk XoL, cat XoL, clash, aggregate stop loss). Portfolio analytics require consistent definitions of accident year, report year, and underwriting year; consistent handling of partial recoveries, commutations, reinstatements, and claim splits; and robust tie-outs to cedent financials (including Schedule F Part 3 recoverables and Part 5 aging where available). These nuances are not optional—they are fundamental to understanding tail behavior, attachment probability, and expected volatility at different layers.

Document Diversity Reinsurers Must Master

Submission packets rarely look the same. Typical inputs include:

• Loss Run Reports (PDF, Excel, CSV) with claim header data, paid and incurred losses, ALAE/ULAE treatment, and reserve history by valuation date.
• Cedent Loss Bordereaux (monthly or quarterly) listing claim movements, recoveries, reopening indicators, catastrophe codes, and attachment references.
• Claim Register Exports from policy admin or claims systems, often with internal codes, multiple claim keys, and inconsistent date fields.
• Schedule F (NAIC) references and tie-out sheets to reconcile ceded balances and recoverables, often needing crosswalks to claims-level files.

For a Reinsurance Risk Analyst, manual conversion of this document diversity into a consistent, portfolio-ready dataset is slow, expensive, error-prone, and hard to defend under audit—especially when timelines are tight and submission waves hit all at once.

How the Process Is Handled Manually Today

Manual review typically starts in email and shared drives, moves into Excel and Access, and ends in BI dashboards after a long series of brittle transformations. Teams build ad-hoc crosswalks to harmonize field names, join keys across claim numbers and occurrence numbers, reconcile paid/incurred figures to the latest valuation, and attempt to map cedent lines of business to internal taxonomies. They check whether ALAE is included in loss, separated, or treated as an expense; whether case reserves include LAE; and whether reopening logic is explicit or inferred. They convert currencies using valuation-date FX rates, try to distinguish supplemental payments from reopened reserves, and detect duplicates that sneak in when a cedent sends both a bordereau and a separate loss run for the same period.

Every step invites risk. If a valuation date is misread, incurred development may be misinterpreted. If claim keys are inconsistent, large losses might be double-counted—or missed entirely. If treaty metadata is incomplete, ceded shares may be misapplied to the wrong layer. And because each cedent’s schema differs, analysts must constantly rebuild their mapping logic. This is where “spreadsheet archaeology” becomes a business risk—when the person who built the crosswalk leaves, the institutional memory of how those numbers were created often leaves with them.

What Reinsurers Really Need from Loss Runs

At the portfolio scale, you need more than raw extractions; you need reliable, normalized data suitable for risk modeling, pricing, and governance. In practice that means:

• Clean, standardized fields across cedents: claim ID, occurrence ID, policy number, accident date, report date, jurisdiction, cause of loss, LOB, peril/cat code, and valuation date.
• Clear, consistent financials: paid loss, paid ALAE, incurred loss, incurred ALAE, case reserves, IBNR where available, and treatment of ULAE.
• Layer-aware ceded views: ceded share, attachment point, limit, reinstatements, occurrence vs aggregate treatment, and treaty year mapping.
• Development-ready structures: ability to build triangles by accident, report, or underwriting year, and to track reopenings and subsequent development.
• Defensibility: page-level citations back to source documents, with versioning and a full audit trail of every transformation.

Without these, trend analyses, tail selections, and capital modeling inputs carry uncertainty. With them, a Reinsurance Risk Analyst can confidently quantify severity tail risk, frequency trends, and layer exhaustion probabilities.

How Doc Chat Automates Bulk Loss Run Extraction and Normalization

Doc Chat by Nomad Data is a suite of purpose-built, AI-powered agents that automate end-to-end document review and data extraction for insurance. For reinsurance due diligence, Doc Chat ingests entire submission folders—thousands of pages and dozens of spreadsheets—then extracts, normalizes, and links everything into a unified schema in minutes. This is the core of AI to extract claims from loss runs for reinsurance: accurate, repeatable, portfolio-scale automation tuned to your playbooks and data standards.

1) High-Fidelity Ingestion at Scale

Doc Chat handles PDFs (including scans), XLSX, CSV, and mixed attachments with embedded tables. It recognizes cedent-specific layouts, extracts tables that span multiple pages, and intelligently associates headers with the right columns even when formats vary. It processes entire claim files or document sets at enterprise throughput so reviews move from days to minutes.

2) Targeted Field Extraction with Reinsurance Context

Doc Chat is trained to pull the fields reinsurers care about most and to understand the context that changes meaning. For example, it distinguishes between paid loss and paid ALAE even when the cedent’s column descriptors are terse. It captures accident date, report date, close/reopen flags, valuation date, claim status, jurisdiction, cause of loss, peril codes, policy number, occurrence number, and any ceded shares or treaty references in the file. When ceded/assumed distinctions appear alongside Schedule F (NAIC) excerpts, it links those to claim-level records for tie-out.

3) Normalize Ceded Loss Data with AI

Different cedents load different meanings into similar labels. Doc Chat applies a cedent-aware normalization layer that maps source columns to your standard schema and flags ambiguous definitions for human confirmation. It standardizes ALAE treatment; harmonizes accident/report/valuation dates; aligns policy, occurrence, and treaty year; and supports currency normalization on valuation date. In short, it helps you normalize ceded loss data with AI so multi-cedent comparisons become reliable and repeatable.

4) Deduplicate, Link, and Reconcile

Doc Chat detects duplicate claims across multiple files (e.g., a bordereau and a loss run that both include the same claim) and links records via multiple keys (claim ID, occurrence ID, policy number, and date combinations). It reconciles paid/incurred movements across valuation dates and checks that ceded shares are consistent with treaty terms supplied in the packet. Where cedent-provided Schedule F tie-out sheets exist, Doc Chat validates recoverables mapping and flags discrepancies for review.

5) Automated Quality Checks and Auditability

Every extracted value is traceable back to the page and cell from which it came, with a clickable citation trail. Doc Chat runs completeness checks (e.g., missing accident date or valuation date), logic checks (e.g., incurred less than paid), and consistency checks across documents (e.g., share percentages inconsistent with treaty documentation). The output includes validation flags so analysts can prioritize the exceptions that actually matter.

6) Real-Time Q&A on Massive Document Sets

Doc Chat supports instant, portfolio-wide queries across all extracted and normalized data. Ask: “List all claims above 80% of attachment with paid+case > $1M by accident year,” or “Show all reopenings in the last two valuations for Workers Comp in CA.” This interactive capability mirrors how reinsurance analysts work. It’s the same innovation described in Great American Insurance Group’s experience with Nomad—fast answers with page-level citations that build trust. See the story: Reimagining Insurance Claims Management.

Automated Loss Bordereaux Analysis Reinsurance

Bordereaux are powerful but notoriously inconsistent. With automated loss bordereaux analysis reinsurance, Doc Chat ingests monthly or quarterly cedent loss bordereaux, aligns them to your schema, reconciles movements valuation-to-valuation, and constructs development views by accident, report, or underwriting year. It tracks how claim reserves evolve, identifies reopenings and large loss emergence, and separates paid indemnity from paid ALAE where possible. When a cedent flips ALAE treatment or changes a column label mid-year, Doc Chat flags the change and adjusts mappings accordingly, preventing subtle drifts from corrupting your trendlines.

Doc Chat also links bordereaux to treaty metadata—quota share percentages, per-occurrence retention, attachment points, limits, and reinstatement provisions—so you can view gross, ceded, and net at the claim and layer level. That enables layer exhaustion analysis, probability of attachment by year, and near-real-time tracking of large losses moving toward your layer.

Portfolio-Ready Outputs for Modeling, Pricing, and Governance

Once loss runs and bordereaux are standardized, Doc Chat delivers structured outputs directly to your data warehouse or modeling environment. Outputs can include:

• Claim-level fact tables: clean IDs, dates, jurisdiction, peril, LOB, status, reopen flags, paid/incurred/ALAE fields, currency, valuation date, and treaty references.
• Occurrence-level aggregations: per-occurrence totals, layer allocation, attachment utilization, reinstatement usage.
• Development structures: triangles by AY, RY, UY with consistent definitions, and data to support tail selection and Bornhuetter-Ferguson or Cape Cod methods.
• Exception reports: data quality issues, suspected duplicates, valuation inconsistencies, missing fields, mapping uncertainties.
• Schedule F tie-outs: claim-to-ledger reconciliation evidence where cedent materials are provided.

Because every data point carries a citation back to the source page and file version, your analytics become defensible to auditors, retrocessionaires, and rating agencies. This page-level transparency is a core Doc Chat capability and a major reason insurers trust its outputs. For more on why inference-driven document automation beats basic scraping, see Beyond Extraction.

Business Impact for the Reinsurance Risk Analyst

The move from manual wrangling to automated normalization changes everything. Reinsurance Risk Analysts reclaim days per deal and weeks per portfolio refresh. Underwriters see tail risk and attachment dynamics sooner. Portfolio managers gain a consistent, cross-cedent view that supports capital allocation and pricing discipline.

Time savings are dramatic. Clients have seen claim reviews cut from days to minutes when thousands of pages are involved—mirroring the transformations documented in Nomad’s medical and claims reviews where 10,000 to 15,000 pages are summarized in under two minutes. See: The End of Medical File Review Bottlenecks and Reimagining Claims Processing Through AI.

Quantified benefits typically include:

• Cycle time reductions of 70–95% for loss run consolidation and bordereau normalization.
• Cost reductions from reduced manual data entry and fewer external consultants for urgent normalization tasks.
• Accuracy and consistency gains due to standardized mappings, automated checks, and elimination of copy-paste errors.
• Scalability for surge periods—renewal seasons, M&A due diligence, or large portfolio transfers—without temporary staffing.

Beyond speed and cost, Doc Chat improves decision quality. When you can normalize ceded loss data with AI and interrogate the entire portfolio with natural-language questions, your selections for tail, LDFs, large loss load, and cat allowances are based on a broader, cleaner, and more current dataset.

Why Nomad Data Is the Best Solution for Reinsurance

Doc Chat isn’t generic OCR or a black-box summarizer. It’s a configurable suite of AI agents trained on your playbooks, your documents, and your standards, backed by Nomad Data’s white glove delivery. We learn how your organization defines “right,” then encode those rules so the system produces outputs your team trusts. Implementation typically completes in 1–2 weeks, with immediate value from a drag-and-drop interface and rapid API integration to your data lake or pricing systems.

Key differentiators for reinsurers include:

• Volume and speed: Ingest entire submission folders and produce normalized outputs in minutes, not weeks.
• Complexity handling: Reliably parses inconsistent loss runs, bordereaux, and claim registers; digs out the subtle but crucial differences in ALAE treatment and reserve definitions.
• Real-time Q&A: Ask follow-up questions across massive document sets and get instant, citation-backed answers.
• Thorough and complete: Surfaces every reference to coverage, liability, or damages; eliminates blind spots that cause leakage or mispricing.
• Security and governance: SOC 2 Type II controls with document-level traceability, supporting defensible audits and peer reviews.

We’re your partner in AI, not just a software vendor. Doc Chat evolves with your needs—new cedent templates, new treaty types, new output schemas—so your analytical edge compounds over time. For a broader view on why automating document inference yields outsize ROI, see AI’s Untapped Goldmine: Automating Data Entry.

Embedding High-Intent Capabilities in Your Workflow

Doc Chat directly addresses the exact search intents driving reinsurance teams to modernize:

AI to extract claims from loss runs for reinsurance: Extract and normalize claim-level details—including accident date, report date, paid/incurred, ALAE, reopen flags, and treaty references—at enterprise scale, with page-level citations.

Bulk loss run data digitization for portfolio review: Convert mixed-format loss runs and bordereaux into a harmonized dataset suitable for portfolio analytics, LDF selection, tail modeling, and pricing studies.

Normalize ceded loss data with AI: Apply cedent-aware mappings, resolve ALAE treatment, align valuation dates, standardize LOB and peril, and reconcile to Schedule F (NAIC) where provided.

Automated loss bordereaux analysis reinsurance: Continuously ingest monthly or quarterly bordereaux, track development, detect large loss emergence, and maintain a layer-aware, ceded view of portfolio dynamics.

Example Use Cases for the Reinsurance Risk Analyst

Below are realistic scenarios that show how Doc Chat streamlines due diligence:

Scenario 1: Multi-Cedent Property Cat XoL Review

Problem: Five cedents each send different loss run structures for the same accident year, with cat codes inconsistently populated. You need to quantify near-attachment claims and estimate potential exhaustion under a new limit.

Doc Chat: Extracts all claim financials, associates claims to occurrences, normalizes cat codes and peril descriptors, and aligns currency and valuation dates. Produces a portfolio table of claims exceeding 50% of retention and a watchlist for those above 80% of attachment. Every figure links to source pages.

Scenario 2: Casualty Quota Share with ALAE Treatment Differences

Problem: Two cedents treat ALAE differently—one includes ALAE in incurred loss; the other separates it. Your pricing needs a consistent combined view.

Doc Chat: Detects the ALAE treatment per cedent, standardizes outputs according to your policy (e.g., display combined incurred and separate ALAE), and flags any ambiguous cases for review. The unified dataset enables apples-to-apples severity trend analysis.

Scenario 3: Schedule F Tie-Out for Recoverables

Problem: You need to validate ceded recoverables and ensure loss runs reconcile to financial statements.

Doc Chat: Maps claim-level ceded amounts to Schedule F (NAIC) tie-outs when provided, flags discrepancies, and produces an auditable reconciliation package with document citations.

Scenario 4: Bordereaux Drift and Reopening Detection

Problem: A cedent updates its bordereau template mid-year and reopenings increase. You need to verify whether the spike is real or a definitional artifact.

Doc Chat: Identifies mapping changes, highlights definition shifts, and builds a reopening analysis that separates genuine reopenings from new coding practices. Trendlines stay accurate—and trusted.

Sample Natural-Language Prompts You Can Use Today

Doc Chat’s real-time Q&A lets analysts explore their portfolio immediately after ingestion and normalization:

• “Show all claims where paid loss + paid ALAE > $500,000 and incurred > $1,000,000 by accident year for General Liability.”
• “List reopenings from 2022 to 2024 with changes in case reserves > $250,000 and provide source citations.”
• “Identify claims above 80% of attachment under the 2021 occurrence XoL treaty for Cedent A; include currency normalization.”
• “Create a triangle of incurred (incl. ALAE) by accident year and valuation date for Workers Comp—note ALAE treatment by cedent.”
• “Which claims have inconsistent accident date vs report date ordering, and which files do those records come from?”

Security, Compliance, and Explainability Built In

Reinsurance due diligence demands defensibility. Doc Chat pairs enterprise-grade security (SOC 2 Type II) with document-level traceability for every extracted data point. Each number is backed by a clickable citation to the exact page and file. This transparent audit trail helps satisfy internal model validation, external audits, and counterparty queries. As highlighted in GAIG’s experience, page-level explainability builds organizational trust in AI-driven workflows.

Implementation: White Glove, 1–2 Weeks to Value

Nomad Data delivers outcomes—not just software. Our white glove team configures Doc Chat to your schemas, preferred ALAE handling, LOB taxonomy, FX policies, and treaty metadata conventions. You can start with drag-and-drop uploads on day one and move to API-driven ingestion as you scale. Typical timeline:

• Week 1: Solution design and schema alignment; pilot on your actual cedent files.
• Week 2: Validation, exception tuning, and integration to your DWH/BI or pricing tools.

Because Doc Chat learns your standards, it institutionalizes expertise and standardizes processes—so outcomes no longer depend on which analyst builds the spreadsheet. For more on standardizing unwritten rules, see Beyond Extraction.

Frequently Asked Questions

How does Doc Chat handle inconsistent cedent schemas?

We configure cedent-aware mappings that learn each cedent’s column names and definitions. The system normalizes to your internal schema and flags ambiguities for quick human review. Over time, mappings strengthen, and manual touchpoints shrink.

What about scanned PDFs and poor-quality exports?

Doc Chat combines OCR with AI-based table reconstruction to recover structured data from scans. Where confidence is low, it highlights fields for verification and provides links to the source page for easy spot checks.

Can Doc Chat reconcile to Schedule F (NAIC)?

Yes—when cedent-provided Schedule F tie-out sheets are included, Doc Chat maps claim-level ceded amounts to the financial statements, flags variances, and generates a reconciliation report with citations.

How do you prevent AI “hallucinations”?

Doc Chat extracts directly from provided documents and cites every value back to source pages. It does not invent values. Where definitions are ambiguous, it explicitly asks for human guidance rather than guessing.

Is our data secure?

Yes. Nomad Data maintains SOC 2 Type II controls, and client data is not used to train foundation models by default. Access is governed by enterprise security best practices, and detailed audit logs are maintained.

How quickly can we get live value?

Most teams see production-grade outputs in 1–2 weeks. You can begin with a proof-of-value by dragging and dropping real cedent files, then scale to API-based ingestion and downstream integrations.

A Better Operating Model for Reinsurance Analytics

With Doc Chat, reinsurers replace brittle, ad-hoc spreadsheets with an AI-powered pipeline that is fast, consistent, and explainable. You gain a unified portfolio view; you can measure severity tail risk accurately; you can watch layer approach in near-real time; and you can answer urgent underwriting questions without waiting on data wrangling. In a market where speed and defensibility matter, this operating model is a competitive advantage.

If your team is exploring bulk loss run data digitization for portfolio review or seeking AI to extract claims from loss runs for reinsurance, it’s time to see Doc Chat in action. Visit Doc Chat for Insurance to schedule a briefing, or share a sample submission pack and we’ll show you how quickly we can convert it into a portfolio-grade dataset—complete with citations, QA flags, and analyst-ready outputs.