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

Reinsurance portfolio managers face a recurring bottleneck every renewal season: hundreds of cedent submissions arrive in wildly different formats, valuation dates, and levels of granularity. Loss Run Reports, Cedent Loss Bordereaux, Schedule F (NAIC) exhibits, and Claim Register Exports must be validated, normalized, and rolled into portfolio views that drive attachment choices, pricing, and retrocession strategy. The challenge is both volume and complexity. Sifting through thousands of pages or dozens of spreadsheets per cedent to build consistent accident-year triangles, severity distributions, and tail indicators stretches teams, lengthens cycle time, and increases the risk of leakage or adverse selection.

Nomad Data’s Doc Chat solves this head-on. Doc Chat for Insurance is a suite of AI-powered agents built to ingest, extract, standardize, and analyze massive document sets at once. For reinsurance portfolio managers, that means automated extraction and normalization of loss run data from hundreds of cedent reports, instant reconciliation to Schedule F, and portfolio-level analytics in minutes. Doc Chat makes bulk loss run data digitization for portfolio review not only feasible, but routine.

Why Bulk Loss Run Extraction Matters for Reinsurance Portfolio Managers

Reinsurers price and select risk across highly heterogeneous books. In a single quarter, a portfolio manager may evaluate facultative placements, pro rata treaties, and excess of loss layers across property, casualty, workers compensation, auto liability, and specialty lines. Each cedent provides different document types and structures. A Loss Run Report might include paid, case, and incurred by accident year with reserve movement notes, while a Cedent Loss Bordereau could include claim-level rows with policy number, peril, sub-cause, state, attachment indicator, ALAE split, recoveries, and subrogation. Some provide Claim Register Exports with internal codes that map imperfectly to standard LOB taxonomies. Others share a Schedule F (NAIC) that must reconcile to ceded balances yet is insufficiently granular for claim development analysis. The operational friction of unifying these datasets is the enemy of speed-to-quote and accurate tail risk assessment.

For a portfolio manager, missed or late insights come at a cost: outdated development factors, incomplete large loss lists above attachment, mis-estimated ALAE seepage, or hidden frequency spikes in a specific state or peril group can tilt pricing and portfolio construction. When a renewal clock is ticking, teams need AI to extract claims from loss runs for reinsurance with reliability, context, and auditability.

Nuances of the Problem in Reinsurance Due Diligence

Reinsurance data complexity is not just a formatting problem; it is a semantic one. Cedents use different field names for the same concept, and sometimes the field you need does not explicitly exist. Consider these nuances a portfolio manager encounters when reviewing Loss Run Reports, Cedent Loss Bordereaux, or Claim Register Exports:

• Multiple valuation dates and currencies: Paid and incurred at 12, 24, 36 months differ across cedents, with mixed valuation cutoffs and multi-currency exposures that require precise FX handling and consistent as-of timestamps.
• Case and ALAE conventions: Some cedents include ALAE in incurred, others split ALAE or exclude it until specific triggers. ULAE is rarely present but often implicitly embedded in practices.
• Layer tagging and attachment clarity: Excess of loss submissions may inconsistently label whether a claim pierced the attachment point, requiring inference from paid plus case versus reported retentions, with re-opened claims complicating the trail.
• Event and peril coding: Property cat losses may not carry a consistent catastrophe code. Workers comp and liability often include sub-causes or jurisdictional clues that must be inferred from narrative fields.
• Development views and triangles: Accident year versus report year versus underwriting year, with gaps, late-reported claims, and aggregation logic varying by cedent.
• Recoveries and offsets: Subrogation, salvage, deductibles, and facultative reinsurance recoveries can be embedded or netted differently, impacting ultimate loss estimation.
• Reconciliation to financial statements: Loss run totals must tie to ceded balances and aging in Schedule F (NAIC), which itself is organized for statutory reporting rather than claim development analysis.

Delivering a consistent, audit-ready portfolio view requires more than OCR and field matching. It requires the ability to infer intent, apply reinsurance-specific logic, and standardize diverse cedent conventions without losing traceability to the original page or cell.

How the Process Is Handled Manually Today

Most reinsurance portfolio teams still use spreadsheet-heavy, analyst-driven workflows. Analysts download PDFs, spreadsheets, or portal exports, then spend hours copying, pasting, and debugging formulas. When faced with scanned or image-based Loss Run Reports, they invoke generic OCR tools, then hand-edit garbled tables. Python scripts or R notebooks exist, but brittle parsing rules break when cedents change column headers, reorder tabs, or add footnotes. A single renewal set can spawn a maze of one-off macros, regexes, and manual cleanups that must be repeated annually.

What this looks like in practice:

• Pull loss runs and bordereaux by cedent and treaty. Split by LOB and valuation date in shared folders.
• Copy claim-level fields into a common spreadsheet. Try to align paid, case, incurred, ALAE, deductibles, policy numbers, exposure measures, and accident dates.
• Run manual checks to align totals to Schedule F ceded balances and to prior valuation snapshots.
• Hand-build triangles, frequency-severity tables, and large loss filters above attachment and by peril.
• Rework everything when a cedent replies with an updated extract, a late endorsement, or a retroactive reserve change.

Cycle time stretches from days to weeks. When volume spikes, teams triage by focusing on the largest cedents and skimming smaller ones, creating uneven diligence. Human fatigue introduces inconsistencies, and key inferences hide in footnotes, merged cells, or unstructured claim notes. The result is avoidable uncertainty in pricing, tail selection, and portfolio steering decisions.

How Doc Chat Automates Bulk Loss Run Data Digitization for Portfolio Review

Doc Chat brings a purpose-built, end-to-end approach that goes far beyond generic OCR or template matching. It is engineered to handle full claim files and reinsurance submission packets at scale, guided by your portfolio playbook. For reinsurers who need automated loss bordereaux analysis reinsurance-wide, Doc Chat delivers speed, accuracy, and explainability.

1. Ingest at enterprise scale

Drop entire submission folders into Doc Chat or connect to your SFTP, DMS, or e-mail inbox. The system ingests PDFs, Excel and CSV bordereaux, Claim Register Exports, scanned Loss Run Reports, and supporting documents. It processes thousands of pages per minute and entire claim registers without performance degradation. Volume spikes during renewal season are handled elastically, without adding headcount.

2. Understand the document, not just the layout

Using AI that treats document scraping as inference rather than template scraping, Doc Chat identifies cedent-specific terminology and maps it to a canonical schema. If a cedent puts ALAE in incurred or splits it out, the agent recognizes the convention and normalizes it while retaining original values for audit. This approach is aligned with Nomad’s perspective in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs; see our deep dive here: Beyond Extraction.

3. Normalize ceded loss data with AI

Doc Chat applies a reinsurance-tuned canonical model across documents and cedents. Fields such as accident date, report date, paid loss, case reserve, incurred, ALAE, recoveries, attachment indicator, peril code, state, policy number, and valuation date are extracted and standardized. It harmonizes currency, date formats, claim status labels, and LOB taxonomy, then structures the results into a consistent dataset suitable for triangles, development analysis, and treaty pricing.

4. Reconcile to Schedule F (NAIC) and cedent financials

The agent cross-checks loss run totals, by period and line, against Schedule F (NAIC) ceded balances and notes any gaps or mismatches. It flags missing valuation periods, inconsistent ALAE treatment, or recoveries that do not align with financial statements. Reinsurers can rapidly confirm completeness and accounting coherence before moving to pricing assumptions.

5. Real-time Q&A over the entire packet

With Doc Chat’s real-time Q&A, a portfolio manager can ask natural-language questions across all uploaded documents and immediately receive answers with page-level citations: list all claims above a 1 million attachment in accident year 2021 with paid ALAE over 100k; build an AY-DY triangle for workers comp with incurred net of recoveries; identify all reopened claims with reserve increases above 200k in the last valuation. Each answer links back to source pages or cells for instant verification. For a demonstration of this workflow in complex claims, see Reimagining Claims Processing Through AI Transformation.

6. Structured export and integration

Normalized data is exportable to Excel, CSV, Snowflake, SQL warehouses, BI tools, or pricing workbooks. Standardized outputs accelerate actuarial analysis, catastrophe modeling, and treaty negotiation. Doc Chat integrates with exposure management platforms and can enrich rows with peril taxonomies or industry codes to align with internal pricing engines.

7. Auditability, governance, and security

Every extracted field retains lineage to the original page or cell. Answer citations are clickable. Workflows are SOC 2 Type 2 aligned, and data policies prevent customer data from being used for model training without explicit approval. Compliance and audit teams gain confidence through traceability, a point reinforced in our client story with GAIG: Great American Insurance Group Accelerates Complex Claims with AI.

From Extraction to Insight: Automated Loss Bordereaux Analysis for Reinsurance

Extraction is the starting line. Doc Chat then operationalizes the analytics that portfolio managers rely on to steer capital.

• Loss triangles and development analysis: Build accident-year and report-year triangles for paid, incurred, and ALAE. Compare development factors across cedents and valuation snapshots.
• Frequency and severity: Segment by state, peril, cause, jurisdiction, or account size. Identify changing claim count patterns or shifts in severity distribution.
• Attachment and layer behavior: Isolate claims breaching specific retentions or treaty layers; quantify layer burning cost with and without ALAE.
• Large loss tracking: Produce large loss inventories with movement notes, reopen flags, and latest reserve changes.
• Recoveries and net positions: Track salvage, subrogation, and facultative or retro recoveries to understand net ultimate and ALAE creep.
• Completeness and consistency: Identify missing valuation periods, unmatched policy numbers, or non-reconciling totals to Schedule F (NAIC) or cedent trial balances.

The result is immediate insight for quote readiness, capital allocation, and retro decisions. In short, Doc Chat operationalizes automated loss bordereaux analysis reinsurance-wide.

The Business Impact: Time, Cost, Accuracy, and Portfolio Performance

Doc Chat’s impact shows up in cycle time, expense ratio, decision quality, and competitive win rate.

• Cycle-time reduction: Move from days or weeks of manual collation to hours or minutes of AI-driven extraction and normalization. Portfolio managers reach pricing assumptions faster and respond to brokers with confidence.
• Lower LAE and analyst hours: Cut manual data entry and exception handling dramatically. See our perspective on the hidden opportunity in AI’s Untapped Goldmine: Automating Data Entry.
• Accuracy at scale: Machines do not fatigue. Doc Chat maintains consistent accuracy regardless of document length, decreasing leakage from missed clauses, misread totals, or overlooked reserve movements.
• Better pricing and selection: With richer, reconciled data, actuaries and underwriters can refine development patterns, tail factors, and layer burn estimates. This translates into tighter pricing and stronger portfolio construction.
• Enhanced audit and compliance: Page-level citations and lineage satisfy regulators, reinsurers, and internal auditors. Disputes and endorsements resolve faster when evidence is one click away.

Clients routinely report 70 to 90 percent reductions in manual time for loss run processing and significant improvements in due diligence completeness. These efficiency gains compound during renewal season, boosting quote throughput and hit ratios.

AI to Extract Claims from Loss Runs for Reinsurance: What Makes Doc Chat Different

Doc Chat’s effectiveness stems from three pillars: scale, sophistication, and service.

Scale: ingest entire claim files and submission packets

Doc Chat ingests entire cedent submissions, including multiple years of Loss Run Reports, Cedent Loss Bordereaux, Claim Register Exports, and Schedule F (NAIC) views. Thousands of pages or rows per cedent are processed in minutes without throttling your team.

Sophistication: inference over rigid templates

Nomad’s approach treats document analysis as inference, not web scraping for PDFs. It sees through layout changes, differing field names, and free-text footnotes to surface the true, consistent meaning. If the field you need is implied rather than explicit, Doc Chat infers, extracts, and documents the logic it used. This philosophy is described in detail here: Beyond Extraction.

Service: the Nomad Process and white-glove delivery

We do not hand you a generic tool and wish you luck. Nomad trains Doc Chat on your playbooks, cedent mix, and analytics standards. Our team interviews your subject matter experts, codifies unwritten rules, and delivers a solution that fits like a glove. Implementation typically takes 1 to 2 weeks, with white-glove onboarding, test runs on your real cedent packets, and rapid iteration until outputs match your gold standard. For a cross-industry perspective on how this approach drives outcomes, see AI for Insurance: Real-World AI Use Cases Driving Transformation.

Detailed Workflow: From Raw Cedent Files to Portfolio-Ready Analytics

Step 1: Intake and classification

Upload or auto-ingest documents. Doc Chat detects document types, valuation periods, and cedent identity, then routes each file to the right extraction pathway.

Step 2: Extraction and field mapping

AI reads tables and free text to populate a canonical schema: accident date, report date, LOB, state, peril, policy number, claim number, paid, case, incurred, ALAE, recoveries, open or closed status, reopen flag, attachment indicator, currency, and valuation date. Exceptions are captured for follow-up.

Step 3: Normalization and harmonization

Doc Chat standardizes names, codes, units, and currencies, and aligns cedent-specific conventions to your internal taxonomy. It can recognize when ALAE is included and create both gross and normalized views.

Step 4: Reconciliation and controls

Totals by valuation period and line are reconciled to Schedule F (NAIC) and cedent attestations. Deviations are flagged with evidence so your team can resolve discrepancies quickly.

Step 5: Analytics and visualization

Automated triangle construction, large loss identification, frequency-severity summaries, and attachment-layer analytics are produced and made available to BI tools, pricing models, or spreadsheet templates. Teams can ask follow-up questions in natural language and receive instant, sourced answers.

Step 6: Export and integration

Push the normalized dataset to your warehouse, pricing engines, or actuarial workbooks. Maintain a consistent master record for renewal debates and future audits.

Common Edge Cases and How Doc Chat Handles Them

• Scanned PDFs with poor image quality: Advanced OCR with reinsurance-tuned post-processing repairs tables and validates totals against footnotes.
• Multi-currency portfolios: Extracts currency codes, applies agreed FX rules as of valuation date, and keeps original amounts for traceability.
• Event coding gaps: Infers catastrophe or cause of loss from narrative fields, state, and timing, while clearly labeling inferred versus explicit fields.
• Reopened claims: Identifies reopen flags, reserve movements, and timing to highlight adverse development risk and ALAE creep.
• Attachment inference: Where cedent tags are missing, computes whether paid plus case indicates layer breach and records the basis of inference.
• Recoveries and offsets: Separates salvage, subrogation, and facultative recoveries to show both gross and net metrics.

Operationalizing Portfolio Strategy With Faster, Better Data

With normalized cedent data available in hours, portfolio managers can test scenarios quickly: adjust attachment points, evaluate alternative per-occurrence and aggregate structures, or simulate how different tail assumptions affect expected loss and capital consumption. Faster diligence means more quotes, better negotiation positions, and fewer missed windows with brokers.

Doc Chat also brings transparency to internal governance. When committees ask for evidence behind a reserve movement claim or a large loss inclusion, a manager can click the source citation in Doc Chat and show the exact page from the Loss Run Report or the specific cell in the Cedent Loss Bordereau. That level of explainability transforms reviews from opinion to evidence.

Why Nomad Data Is the Best Solution for Reinsurance Portfolio Teams

Reinsurance is an inference-heavy, judgment-led domain. Nomad Data’s Doc Chat meets it on its own terms:

• Volume: Ingest entire submission packages and claim registers without slowing your team.
• Complexity: Understands exclusions, endorsements, and reinsurance conventions that hide behind inconsistent labels.
• The Nomad Process: We train Doc Chat on your playbooks and cedent realities, not a theoretical standard.
• Real-Time Q&A: Ask for any list, triangle, or exception and get an instant, sourced answer.
• Thorough and complete: Surfaces every reference to coverage, liability, damages, and loss development signals, minimizing blind spots.
• Your partner in AI: White-glove engagement with 1 to 2 week implementation, iterative tuning, and ongoing success management.

In short, Doc Chat does not just extract; it institutionalizes expertise and standardizes diligence. That drives consistent, defensible decisions across your reinsurance portfolio.

Implementation Timeline: 1 to 2 Weeks to Production Value

Nomad’s white-glove approach keeps lift light and time-to-value short.

• Week 1: Discovery and scoping. We review sample Loss Run Reports, Cedent Loss Bordereaux, Schedule F (NAIC) examples, and Claim Register Exports. We agree on your canonical schema, analytics outputs, and validation checks.
• Week 2: Configuration and tuning. Doc Chat is trained on your playbook. We run through your real cedent packets, validate reconciliation to Schedule F, and align on exception handling. Users begin operating via drag-and-drop while integrations are finalized.

Most teams are operating at scale within two weeks, with integrations to data warehouses or BI tools added as needed, without slowing initial value realization. For more on how fast teams adopt purpose-built AI when it is accurate and explainable, see our GAIG webinar recap: Reimagining Insurance Claims Management.

Use This Article As Your Checklist for Renewal Season

If you are a portfolio manager preparing for renewal, use the following checks to ensure your workflow is ready:

• Document intake: Can your team ingest and classify all cedent documents rapidly, including scanned PDFs and mixed-format spreadsheets?
• Normalization: Do you have a canonical schema across cedents and lines for paid, case, incurred, ALAE, recoveries, peril, state, and attachment indicators?
• Reconciliation: Are your totals reconciled to Schedule F (NAIC) and prior valuation snapshots, with auditable variance explanations?
• Analytics: Can you produce and refresh triangles, large loss lists, and attachment analyses in minutes, not days?
• Explainability: Can you click back to the exact page or cell to support committee reviews and broker discussions?
• Throughput: Can your team scale quotes when broker submissions surge, without sacrificing diligence or accuracy?

If any answer is no, Doc Chat can close the gap quickly. For a broader context on AI’s role in insurance transformation, read AI for Insurance and our perspective on eliminating file review bottlenecks: The End of Medical File Review Bottlenecks. While the latter focuses on medical files, the lessons on volume, consistency, and explainability apply directly to reinsurance loss runs.

Search-Driven Answers for Portfolio Managers

Doc Chat is designed to answer the questions portfolio managers ask every day, in the language they use:

• AI to extract claims from loss runs for reinsurance: Which claims pierced our proposed 2 million attachment for the last three valuation periods, consolidated by peril and jurisdiction?
• Bulk loss run data digitization for portfolio review: How many submission packets remain without a reconciled Schedule F tie-out and what are the material gaps?
• Normalize ceded loss data with AI: For cedents X, Y, and Z, normalize ALAE treatment and produce comparable incurred-to-paid ratios by accident year.
• Automated loss bordereaux analysis reinsurance: Generate a ranked list of cedents by emerging severity above 500k and highlight reopen contributions to adverse development.

Every answer includes citations back to the Loss Run Report page, the Cedent Loss Bordereau cell, or the Claim Register Export row that informed the result.

Getting Started

The fastest path to proving value is to upload a cross-section of your toughest cedent submissions and ask Doc Chat the questions you needed answers to last renewal. Seeing your own submissions return reconciled, normalized, and analyzed in minutes has a way of turning skepticism into momentum. Start here: Doc Chat for Insurance.

Conclusion

Portfolio-scale reinsurance diligence no longer has to trade speed for accuracy. With Doc Chat, reinsurers automate the messy middle of loss run extraction, bordereaux normalization, and Schedule F reconciliation, and move straight to decisions. The payoff is faster quotes, better tail risk visibility, stronger negotiating leverage, and a consistent, auditable process that scales when submissions spike. In a market where timing and clarity set winners apart, Doc Chat gives portfolio managers the operational leverage to evaluate more, decide faster, and price with confidence.