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

Reinsurance portfolio managers face an avalanche of inconsistent Loss Run Reports, Cedent Loss Bordereaux, Claim Register Exports, and even Schedule F (NAIC) filings across every due diligence cycle. The challenge isn’t simply digitizing the pages. It’s turning heterogeneous, cedent-specific formats into standardized, analysis-ready data that accurately reflects paid vs. incurred, case reserves vs. ALAE, event aggregations, and development patterns—all in time to influence pricing, terms, and line allocation. That is exactly what Nomad Data’s Doc Chat delivers.

Doc Chat is a suite of purpose-built, AI-powered document agents that ingest entire submission rooms—loss runs, bordereaux, cover notes, treaty wordings, claim registers—and normalize ceded loss data with AI so you can compare cedents on a true apples-to-apples basis. Whether you are assessing a new quota share, an excess of loss (XOL) tower, or a facultative placement, Doc Chat compresses weeks of manual data wrangling into hours, enabling portfolio-level risk assessment at scale. If you are searching for AI to extract claims from loss runs for reinsurance or planning bulk loss run data digitization for portfolio review, this article details how to get there—fast.

The Portfolio Manager’s Challenge: Granular Loss Intelligence From Messy Cedent Files

For a reinsurance Portfolio Manager, the core question is simple: Do the loss histories, development patterns, reserve behaviors, and event concentrations in this book align with your risk appetite and target return? The hard part is getting trustworthy signals from messy cedent submissions. Every cedent labels fields differently. Accident Year (AY) can be blended with Underwriting Year (UY) or Policy Year (PY). Indemnity and ALAE may be combined, split, or inconsistently applied. Claim statuses are ambiguous, and reopenings or re-reportings hide in free-text notes. Catastrophe IDs may be missing or masked behind local codes. Currency conversions, FX dates, and inflation treatments vary. And when cedents include Schedule F (NAIC) extracts, reconciling reinsurance recoverables to loss runs is still manual work.

In due diligence windows that last days—not months—Portfolio Managers need repeatable, accurate answers to questions like:

• What do the paid-to-incurred ratios look like by AY and maturity for the last five accident years? Are reserves strengthening or weakening?
• Which claims pierced the retention under the expiring XOL treaty and how many clustered into single events? What is the implied clash potential?
• How does ALAE intensity vary by cause of loss and line of business? Where are we seeing escalating defense costs?
• What is the distribution of late-reported claims (report lag) and settlement latency? Is there tail risk emerging in specific lines?
• How do cedent-level case reserving practices compare with industry baselines or with the cedent’s own prior periods?

Doc Chat removes the friction between raw documents and portfolio-ready answers, making it practical to run robust analyses across dozens or hundreds of cedents on a tight timeline.

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

Most reinsurers still rely on teams of analysts to download PDFs and spreadsheets, re-key or copy/paste into master templates, and then wrestle with one-off transformations. Here’s the typical manual path a Portfolio Manager supervises during reinsurance due diligence:

1. Collect files from the data room: Loss runs, bordereaux, claim registers, underwriting summaries, and any Schedule F exhibits provided.
2. Sample the loss runs for column meanings: Determine which column corresponds to paid indemnity, paid ALAE, case indemnity, case ALAE, total incurred, subrogation, salvage, and reinsurance recoveries.
3. Normalize field names via ad hoc mappings: Build VLOOKUPs and macros to align to your internal schema; reconcile AY/UY/PY; infer missing fields from free text.
4. Parse events: Try to identify catastrophe codes, event IDs, or equivalent; where missing, infer events by time and geography.
5. Validate totals: Cross-check that paid plus case equals incurred; reconcile to bordereau aggregates; spot-check against Schedule F reinsurance recoverables where possible.
6. Build triangles and KPIs: Pivot on AY and maturity; calculate LR, ULR, paid/OS ratios, ALAE ratios; produce reserve roll-forwards.
7. Iterate when new files arrive: Redo mapping when a cedent uploads a revised loss run or a new extraction layout.

Even with expert analysts, this process is slow, error-prone, and fundamentally unscalable. Teams burn precious time on repetitive data entry instead of higher-value tasks like evaluating tail risk, stress testing event scenarios, or pressure-testing treaty terms. As described in Nomad’s piece AI’s Untapped Goldmine: Automating Data Entry, the hidden cost of this work isn’t just time—it’s the opportunity lost to do real analysis.

What Makes Reinsurance Loss Data So Hard to Normalize?

The complexity gap between “reading” a PDF and understanding what the numbers mean is massive. Many loss runs do not have explicit fields for the exact metrics you need. They spread the clues across notes, separate tabs, and cedent-specific conventions. As Nomad explains in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, effective document intelligence is about inference, not just location. For example:

• Indemnity and ALAE sometimes appear as a combined number. You have to infer the split from adjacent columns, appendices, or prior-period patterns.
• Event identifiers may be present only in free text, claims notes, or a separate cover sheet. The data you need “exists” across multiple documents.
• AY versus UY versus PY is inconsistently labeled; the definitions can change between tabs in a single workbook.
• Foreign currency figures are commingled with USD without a stated FX date—requiring triangulation from the cedent narrative or schedules.
• Recoveries (subrogation, salvage, or outward reinsurance) may be netted in some places and grossed elsewhere—demanding careful reconciliation.

Building robust analytics on such inputs is not a job for regex alone. It requires an AI understanding of insurance semantics plus a workflow that enforces consistency and auditability at each step.

Introducing Doc Chat for Reinsurance: An End-to-End Engine for Bulk Loss Run Data Digitization

Doc Chat by Nomad Data combines large language models with insurance-tuned agents to automate the entire reinsurance loss data pipeline. It is built to execute exactly what Portfolio Managers need during due diligence windows:

1) Ingest Everything

Doc Chat ingests entire data rooms—PDF loss runs, Excel bordereaux, CSV claim registers, scan images—at portfolio scale. In high-volume scenarios, it processes hundreds of cedent submissions in parallel, retaining document-level audit trails. Nomad’s platform routinely handles throughput that eliminates “review bottlenecks,” as highlighted in The End of Medical File Review Bottlenecks.

2) Extract and Map to a Standard Schema

Doc Chat performs AI to extract claims from loss runs for reinsurance, then maps them into your standard schema or an ACORD-style model, including:

• Claim identifiers: claim number, policy number, policy effective/expiration dates, insured, cedent, program, treaty year.
• Dates: accident, report, reopen, closure, last update; maturity (months from accident or report).
• Amounts: paid indemnity, paid ALAE, case indemnity, case ALAE, total incurred, recoveries (subrogation, salvage, outward reinsurance), outstanding reserves, reopened amounts.
• Classifications: line of business, subline, peril/cause codes, exposure state/country, CAT number or event cluster, litigation flag, claimant type, severity band.
• Treaty context: retention, attachment point, limit, layer, share, corridor, reinstatement, reinstatement premium treatment.
• Calendars: AY, UY, PY handling; valuation date; triangulation keys by AY and maturity.
• Currency handling: original currency, FX date, rate, converted USD figures, normalization across submissions.

When cedents submit Schedule F (NAIC) excerpts, Doc Chat links recoverables and ceded balances to loss activity and flags discrepancies that may imply data gaps or timing issues.

3) Validate, Reconcile, and De-duplicate

Doc Chat auto-checks arithmetic (paid + case = incurred), surfacing rows where the relationship doesn’t hold. It reconciles claim-level totals to bordereau aggregates and highlights unmatched or duplicated claims across multiple files and versions. For bulk loss run data digitization for portfolio review, this step alone can save days of back-and-forth with cedents.

4) Normalize Cedent Idiosyncrasies

Every cedent uses its own terms. Doc Chat standardizes labels (e.g., “defense” to ALAE), structures statuses (open/closed/reopened), harmonizes AY/UY/PY logic, and infers missing fields using context. This is the core of normalize ceded loss data with AI: turning diverse inputs into consistent outputs, at scale.

5) Build Triangles and KPIs Automatically

Doc Chat produces paid and incurred triangles by AY and maturity; calculates paid-to-incurred, OS-to-incurred, ALAE ratios, LR/ULR, and report/settlement lags; and assembles reserve roll-forwards. It produces cedent- and portfolio-level rollups in minutes.

6) Automated Loss Bordereaux Analysis

For automated loss bordereaux analysis reinsurance, Doc Chat reads monthly or quarterly bordereaux, links them to claim registers and loss runs, and surfaces drifts or discontinuities across time. It flags unusual movements—sharp reserve drops, sudden reopenings, or anomalous ALAE accruals—so Portfolio Managers can press for explanations before binding.

7) Real-Time Q&A, Auditability, and Exports

Ask Doc Chat questions in plain language—“List all AY 2019 claims with incurred > retention and ALAE-to-indemnity > 40%,” “Show catastrophe clusters over $5M gross,” “Which claims have inconsistent AY/UY mapping?”—and receive instant answers with page-level citations back to the source files. Export structured data to Excel, CSV, or push directly into your data warehouse and BI tools.

From Days to Minutes: What Changes When Doc Chat Runs the Pipeline

Nomad has demonstrated repeatedly that document-heavy insurance processes move from days to minutes once AI agents take over the rote steps. Great American Insurance Group’s experience captures the speed and trust benefits—fast answers with page-level citations—described in Reimagining Insurance Claims Management. For reinsurance Portfolio Managers, the impact is equally dramatic:

• Cycle time: Consolidate hundreds of cedent loss runs and bordereaux in hours, not weeks.
• Coverage clarity: Tie claim behavior to treaty terms (retention, attachment, limits) to estimate inuring recoveries and retained risk.
• Tail risk visibility: Instantly compare late emergence across cedents and lines; quantify reserve strength.
• Event aggregation: Detect clustered claims likely belonging to the same CAT or clash event—even when event IDs are missing.
• Defensible analytics: Every metric links back to the page and cell that produced it.

How Doc Chat Works Under the Hood—Purpose-Built for Insurance Documents

Doc Chat is not a generic OCR-and-spreadsheet macro. It’s an orchestration of AI agents trained on insurance semantics and review workflows, as discussed in Reimagining Claims Processing Through AI Transformation. Components include:

Document Understanding: Advanced OCR plus layout-aware parsing for multi-tab workbooks, PDFs, and scans. Agents identify headers, footers, totals, and footnotes—critical for catching net vs. gross, currency notes, or reserving caveats.

Semantic Normalization: LLMs reconcile cedent-specific labels to your standard data model. For example, “Defense” or “LAE” maps to ALAE; “OS Ind.” / “OS Exp.” map to case indemnity/ALAE; “Valuation” becomes Val Date; AY/UY/PY inference comes from policy dates, accident dates, and cedent notes.

Quality Controls: Automated checks enforce paid + case = incurred; ensure AY/UY/PY alignment; reconcile to bordereau totals; verify currency conversions; and flag negative or impossible values (e.g., paid > incurred).

Cross-Document Reasoning: Agents link claim registers to loss runs and bordereaux, associate events, and roll up to treaty layers. When cedents provide Schedule F details, the system cross-checks reported recoverables against loss activity to surface timing or completeness issues.

Real-Time Q&A and Traceability: Every answer comes with citations to the page or cell of origin, building trust with underwriters, actuaries, and auditors.

What Gets Extracted—And Why It Matters for Portfolio Managers

Doc Chat structures the fields Portfolio Managers rely on to understand volatility, reserve strength, and contract performance. Typical outputs include:

• Exposure and Context: cedent, program, line of business, subline, geography, AY/UY/PY, valuation date, policy effective/expiration.
• Financials: paid indemnity, paid ALAE, case indemnity, case ALAE, total incurred, recoveries (subrogation, salvage, outward reinsurance), outstanding reserves, claim-level FX and USD equivalents.
• Dynamics: report lag, settlement lag, reopen flags, reserve change deltas by period, incurred movement drivers, ALAE intensity.
• Event Structure: catastrophe/event IDs, cluster groupings, clash indicators, per-occurrence and per-claim limit hits, frequency of retention piercings.
• Triangles and KPIs: paid and incurred triangles; LR/ULR; OS-to-incurred; paid-to-incurred; ALAE-to-indemnity; reserve roll-forward.

This is the foundation for defensible risk selection, pricing guidance, and treaty structuring decisions.

AI-Driven Red Flags and Anomaly Detection

Doc Chat’s anomaly models surface patterns a manual team rarely catches in time:

• Reserve compression in the final months before valuation.
• Unusual ALAE swings not matched by indemnity movement.
• Report lag spikes in specific lines or geographies.
• FX inconsistencies or mixed currencies without clear conversion notes.
• Duplicate claims across versions or files; reopening under a new ID.
• Net/gross mismatches between loss runs and bordereaux.

Portfolio Managers can turn these signals into targeted RFP questions and pre-bind conditions, safeguarding performance and reducing leakage. As Nomad observed in AI for Insurance: Real-World AI Use Cases, reinsurers that industrialize this kind of diligence gain a durable speed and intelligence advantage.

What “Bulk” Really Means: From 20 Cedents to 200

Doc Chat’s agents scale horizontally. Whether you have 20 or 200 cedent submissions, the system processes them concurrently and enforces the same rules. This removes the typical cap on how many programs a Portfolio Manager can realistically diligence per cycle. The result is broader, deeper coverage of opportunities without adding headcount—echoing the scale benefits highlighted in Nomad’s Reimagining Claims Processing.

Business Impact for Reinsurance Portfolio Managers

The gains come in four dimensions: time, cost, accuracy, and insight.

Time Savings

What previously took multi-week sprints can be executed in hours. Doc Chat cuts out the bottlenecks of manual mapping, validation, and reconciliation. Teams redirect time to scenario analysis, stress testing, and strategic allocation decisions.

Cost Reduction

By automating high-volume data entry and validation, reinsurers lower loss-adjustment-like expenses in their diligence operations. Fewer external contractors are required for re-keying, QC, and ad hoc normalization. As described in AI’s Untapped Goldmine, the ROI on document automation often materializes within months.

Accuracy and Consistency

Human fatigue and inconsistent mappings cause leakage and rework. Doc Chat applies the same rules to the 1st cedent and the 201st cedent, and it always cites its sources. That consistency reduces audit risk and strengthens pricing decisions. GAIG’s page-linked answers, featured in the GAIG webinar replay, show how explanation builds trust.

Deeper Insight

With documents normalized, Portfolio Managers can ask better questions: How does tail behavior differ by cedent? Are reserve practices changing? Where is defense cost inflation eroding margins? The answers cascade into smarter line allocations, attachment strategies, and retro purchases.

Why Nomad Data’s Doc Chat Is the Best Fit for Reinsurers

Nomad Data is more than a software provider. We deliver a personalized solution tuned to your treaty structures, field definitions, and diligence workflows.

• White glove service: We sit with your Portfolio Managers, underwriters, actuaries, and data teams to capture unwritten rules and cedent-specific nuances. These become reusable AI “playbooks.”
• 1–2 week implementation: Start in days, not quarters. Drag-and-drop to prove value; then move to API-based integrations with your data lake or pricing tools.
• Insurance-grade accuracy: Purpose-built for insurance semantics; it surfaces every reference to coverage, liability, or damages—and in reinsurance, every field that drives layer economics.
• Real-time Q&A with citations: Ask the portfolio-level question and instantly see the source pages.
• SOC 2 Type 2 security: Enterprise controls, audit trails, and documented governance keep compliance, IT, and audit teams aligned.

As Nomad underscores in Beyond Extraction, success comes from encoding your experts’ logic—not just scraping text. That is our core capability.

Example: Portfolio-Scale Diligence in 48 Hours

Consider a global reinsurer reviewing an inwards portfolio spanning 10 cedents, 8 lines, and 6 treaty structures. The data room included 320 loss run files (PDF and Excel), 36 bordereaux, 10 claim register exports, and several Schedule F snapshots.

With Doc Chat:

• All files were ingested and normalized in under 8 hours.
• Claim-level financials and statuses were reconciled to bordereaux totals with 98% automated matching; the remaining 2% were flagged for cedent follow-up.
• Paid and incurred triangles were generated by AY and maturity, with reserve roll-forwards created for each cedent.
• Event clusters were inferred for missing catastrophe IDs based on date and location proximity.
• Portfolio-level KPIs—paid-to-incurred, OS-to-incurred, ALAE intensity—were computed and exported to the reinsurer’s BI tool the same day.

The Portfolio Manager had a defendable, citation-rich view within 48 hours, empowering them to recommend attachments, caps, and conditions rooted in data—not anecdotes.

Key Use Cases Tailored to Portfolio Managers

Allocation and Appetite Decisions

Normalize cedent loss data and compare like-for-like across lines and regions. Identify loss ratio volatility, reserve strength, and ALAE patterns that drive capital allocation.

Treaty Structuring and Pricing Support

Quantify frequency of retention piercings, clash potential, and layer utilization. Evaluate per-occurrence vs. aggregate options with real data. Provide actuaries with clean inputs for curve fitting and tail estimation.

Event and Cat Aggregations

Infer event clusters and clash exposures when cedent event IDs are missing. Analyze correlations across geographies and sublines to avoid concentration surprises.

Reserve Behavior and Tail Risk

Track reserve strengthening/weakening by AY and maturity. Benchmark cedents against their own history and against peers to anticipate tail emergence.

What About Integration and Change Management?

Doc Chat is designed for immediate use. You can begin with drag-and-drop uploads and graduate to automated feeds:

• Zero-friction start: Upload files; get normalized data and dashboards.
• APIs when you’re ready: Push outputs to your data lake, pricing tools, or risk platforms.
• Audit-ready: Every transformation is logged and explainable.

As Nomad notes in the GAIG webinar, page-level explainability accelerates internal trust and adoption. Teams don’t have to “believe the AI”—they can verify it.

Frequently Asked Questions from Reinsurance Portfolio Managers

How does Doc Chat handle different cedent layouts?

Agents are trained to recognize multiple layouts and convert them into a common schema. They learn from each new mapping, reducing effort for future cycles.

Can I define my own target schema?

Yes. Many reinsurers have internal templates. Doc Chat maps to your model and preserves any custom flags or codes you rely on.

What about currencies and inflation?

Doc Chat preserves original currency, applies stated FX or your house rates if unspecified, and produces USD equivalents. It can also tag values for downstream inflation adjustments.

How do you ensure security and compliance?

Nomad Data maintains SOC 2 Type 2 controls, offers role-based access, and preserves a full audit trail. Your data is isolated and handled per your policies.

Measured Outcomes You Can Expect

Across reinsurance diligence programs, reinsurers implementing Doc Chat typically report:

• 70–90% reduction in time to normalized, reconciled loss data.
• 30–60% lower external and overtime costs tied to manual re-keying and validation.
• Consistent outputs across cedents, enabling portfolio-level comparisons and faster pricing cycles.
• Fewer misses on late emergence, reserve compression, and event aggregation risks.

The bigger, strategic payoff is the ability to review more programs with deeper analysis—without additional headcount. As highlighted in the AI for Insurance article, reinsurers that scale diligence with AI make faster, insight-driven decisions and improve portfolio profitability.

Implementation: White Glove, Fast Start, and Built to Last

Doc Chat’s deployment model is straightforward and collaborative:

1. Discovery (Days 1–2): We capture your templates, KPIs, and decision rules—how you define AY/UY, which fields are mandatory, how you reconcile to bordereaux.
2. Pilot (Days 3–7): Upload a real data room. We configure mappings, build validations, and generate your first normalized portfolio extract.
3. Scale (Week 2): Turn on automated feeds and dashboards. Train users on real-time Q&A and exception workflows.

This 1–2 week implementation period reflects Nomad’s “your partner in AI” philosophy. You’re not just buying a tool; you’re co-creating a solution that encodes how your best people already work.

A New Standard for Reinsurance Due Diligence

When reinsurance teams adopt Doc Chat, document complexity stops being the bottleneck. Portfolio Managers finally get what they’ve always needed: reliable, normalized loss data—backed by citations—arriving early enough to shape appetite, price, and structure. The organization gains the ability to review more opportunities, with more rigor, in less time.

If your team is exploring AI to extract claims from loss runs for reinsurance, planning bulk loss run data digitization for portfolio review, aiming to normalize ceded loss data with AI, or in need of automated loss bordereaux analysis reinsurance, it’s time to see Doc Chat in action. Visit the Doc Chat for Insurance page to learn more and schedule a tailored walkthrough.

Get Started

Upload a representative sample of your next data room—Loss Run Reports, Cedent Loss Bordereaux, Claim Register Exports, and available Schedule F (NAIC) exhibits. In a week or less, you’ll have clean, reconciled, analysis-ready data, with portfolio KPIs and triangles ready for pricing meetings. The speed, transparency, and consistency will redefine how your reinsurance due diligence gets done.