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

Reinsurance portfolio managers face an escalating challenge: evaluating mountains of loss run reports, cedent bordereaux, Schedule F extracts, and claim register exports to price, accept, or reject portfolio transfers and treaty renewals — all on compressed timelines. Files arrive in every imaginable format, with inconsistent fields, mixed currencies, overlapping claim identifiers, and shifting definitions of “incurred,” “paid,” and “case.” When the clock is ticking toward a retrocession or treaty decision, the difference between a rapid, accurate read and a slow, error-prone review can materially impact loss ratios and capital allocation.

Nomad Data’s Doc Chat changes this equation. Doc Chat is a suite of purpose-built, AI-powered agents designed to ingest entire submission packages, extract line-level claim details from Loss Run Reports, Cedent Loss Bordereaux, Schedule F (NAIC) attachments, and Claim Register Exports, normalize them to a consistent schema, and answer portfolio-scale questions in seconds. Instead of spending weeks harmonizing cedent formats, a reinsurance portfolio manager can ask: “Show top 20 losses by Accident Year, paid-to-incurred ratio by cedent, and tail emergence over the last 8 quarters,” and receive an auditable, source-linked response immediately. For organizations searching for AI to extract claims from loss runs for reinsurance or considering bulk loss run data digitization for portfolio review, Doc Chat provides a real, defensible path to instant diligence.

The Reinsurance Portfolio Manager’s Dilemma: Volume, Variability, and Velocity

In reinsurance, the same book of business can look dramatically different depending on how a cedent structures and reports it. One cedent’s “incurred” includes ALAE, another’s excludes it. One groups claims at policy level; another breaks out multiple claim lines per occurrence. On top of that, reinsurers often evaluate assumed portfolios across multiple cedents at once, each with distinct loss run templates, mixed Accident Year (AY), Report Year (RY), and Underwriting Year (UY) conventions, and uneven data depth.

For a Portfolio Manager, these inconsistencies complicate core decisions: tail selection, LDF assumptions, case reserve adequacy, attachment-point behavior, and reinsurance structure fit (quota share vs. per-risk or cat XoL). When deadlines drive renewals and acquisitions, there’s little time to build clean triangles or reconcile cedent totals to Schedule F. The result: sampling instead of full-file review, reliance on cedent-provided rollups, and increased risk of missed red flags in severity, reopened claims, or emerging perils.

How Manual Review Works Today — And Why It’s Breaking

Despite modern tooling, much of reinsurance due diligence is still manual. Analysts download loss runs and bordereaux as PDFs, Excel workbooks, CSVs, and occasional Word tables. They create ad-hoc mappings, stitch columns with VLOOKUPs, write brittle macros to parse multi-row headers, and spend nights reconciling totals back to cedent statements. This slows everything — from initial triage to final pricing.

Typical steps include: profiling column names (e.g., Paid Loss vs. Loss Paid), rekeying missing dates, mapping claim statuses, deduplicating occurrences that appear in multiple files, restating local currency to home currency, aggregating to AY/UY/RJ levels, and building provisional triangles to support LDFs or Bornhuetter-Ferguson blends. Every step introduces risk. Tight timelines often mean “good enough” mappings, which become the foundation for multi-million-dollar decisions.

Why Cedent Data Is So Hard to Normalize

From discovery to pricing, cedent variability is the core obstacle. The following real-world challenges illustrate why normalize ceded loss data with AI is now a priority across reinsurance organizations:

• Schema volatility: Column headers vary by cedent and year (Paid vs. Loss Paid; Case vs. Outstanding; Indemnity vs. Loss).
• Mixed year conventions: Accident, Report, Underwriting, and Fiscal Year appear inconsistently — sometimes within the same file.
• Partial definitions: Incurred may or may not include ALAE; recoveries might net at the line or the file level.
• Currency complexity: Multiple currencies and historic exchange rates are rarely explicit; decimal separators vary by region.
• Identifier drift: Claim numbers, policy numbers, and occurrence IDs change across systems or after migrations and M&A.
• Nested structures: Multi-tab Excel with different logic per tab; PDFs with embedded tables, footnotes, and rollups.
• Reopenings and status codes: Closed-without-pay vs. closed-with-pay; latent reopenings; inconsistent status taxonomies.
• Coverage and peril ambiguity: Peril codes, catastrophe tags, and cause-of-loss often appear as free text.
• Layer and treaty leakage: Attachment-point behavior hidden inside combined files that mix primary and excess layers.
• Rollup misalignment: Bordereaux totals that don’t reconcile with cedent financials or Schedule F triangles.

These issues are not edge cases; they are the norm. They explain why attempts to harmonize cedent submissions with generic RPA or brittle parsers fail at scale, and why reinsurance portfolio reviews often devolve into manual triage.

Automating the Pipeline: How Doc Chat Normalizes Loss Runs and Bordereaux at Scale

Doc Chat by Nomad Data automates the end-to-end flow, from ingestion through analysis and audit. Built for insurance documents, it handles the nuance and the noise — not just clean tables. For reinsurers seeking automated loss bordereaux analysis reinsurance, Doc Chat provides an agentic system that reads like a domain expert and outputs data the way your portfolio team needs it.

Here is how it works in practice:

• Bulk ingestion without friction: Drag-and-drop hundreds of cedent packages (PDF, XLSX, CSV, DOCX, ZIP). Doc Chat ingests entire claim files — even thousands of pages — without adding headcount.
• Adaptive extraction: AI reads multi-structure documents and extracts line-level facts: claim ID, policy ID, insured, policy period, accident date, report date, LOB, peril/cause, jurisdiction, paid loss, paid ALAE, case reserve, incurred loss, subrogation/salvage, recoveries, status, close/reopen flags, currency, and more.
• Normalization to your schema: Nomad trains Doc Chat on your taxonomy, mapping cedent-specific fields into your canonical model (including AY/RY/UY and your coverage lines). The output “fits like a glove.”
• Cross-file reconciliation: The system reconciles bordereaux totals to loss runs and checks alignment to external references (e.g., NAIC Schedule F views shared by the cedent) to flag inconsistencies.
• Duplicate detection and entity resolution: Fuzzy matching resolves claim and occurrence identifiers across files and months; reopenings are tracked and development is trended.
• Currency standardization: Doc Chat captures reported currency and applies client-provided FX logic or historic rates so trend metrics are consistent.
• Triangle-ready outputs: Accident-Year, Report-Year, and Underwriting-Year views are built automatically, with monthly or quarterly development.
• Page-level citations: Every extracted data point is linked back to the source page and cell location for defensibility.
• Real-time Q&A: Ask portfolio questions in plain English and get instant answers plus citations: “Top 10 largest paid losses for AY 2018 across all cedents,” “Which cedent shows the highest case-to-paid ratio at 24 months?”

This is not “web scraping for PDFs.” It’s a domain-trained agent that infers meaning across inconsistent structures, as described in Nomad’s perspective on complexity in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs. The agent’s goal is not only to extract data but to apply your institutional definitions to that data.

From Documents to Decisions: Portfolio-Level Questions You Can Answer Instantly

Because Doc Chat was designed for real-time Q&A across massive document sets, reinsurers can move straight from ingestion to insight. Portfolio managers no longer wait for bespoke ETL; they interrogate live submissions and iterate on hypotheses as if each cedent submission were already in their data warehouse.

Examples of questions a Reinsurance Portfolio Manager can ask Doc Chat — on day one:

• “Rank cedents by the last-eight-quarter paid emergence for AY 2016–2019 and show any cedent with >10% deterioration in the last 12 months.”
• “List all claims above $1M incurred with reopen dates in the past 18 months; show paid vs. case movement by quarter.”
• “Build AY triangles for Paid and Incurred Loss and ALAE, monthly, by LOB, and calculate age-to-age factors and implied LDFs.”
• “Identify mismatch between bordereaux totals and shared Schedule F entries by cedent and period; provide the variance and source pages.”
• “Which cedents net subrogation and salvage at the claim level vs. the file level? Show the impact on paid-to-incurred ratios.”
• “Detect unusual attachment-point behavior in layers scheduled as XoL; show any claims breaching below attachment with explanations.”

This capability echoes the transformation seen at Great American Insurance Group, where adjusters moved from days to seconds when finding answers in complex files, with page-level explainability. See the GAIG story in Reimagining Insurance Claims Management. The same paradigm applies in reinsurance due diligence, only at portfolio scale.

Tying It Together: Loss Runs, Bordereaux, Schedule F, and Claim Registers

Reinsurers rarely evaluate a single document in isolation. A robust due diligence process validates cedent loss runs against bordereaux summaries, interrogates claim register exports for development patterns, and reconciles portfolio views to Schedule F “Assumed & Ceded” data points (as provided by the cedent). Doc Chat is designed to stitch these together automatically.

Concretely, the system:

• Parses Loss Run Reports for line-level claim detail and development history.
• Ingests Cedent Loss Bordereaux to compute premium-to-loss ratios and confirm period rollups.
• Maps Claim Register Exports for reopened flags, litigation indicators, exposure fields, and cause codes.
• Checks alignment to Schedule F (NAIC) snapshots shared in submission packs to spot gaps in assumed vs. reported totals.

The result is a unified, audit-ready view of the cedent’s book and its development — with every computed metric pointing back to original pages and cells.

Business Impact: Faster Quotes, Better Selection, Lower Leakage

Automating extraction and normalization changes the economics of diligence. Instead of sampling or relying on cedent summaries, portfolio managers can perform deep analysis on every page of every file. This drives material gains:

• Speed: Doc Chat processes up to approximately 250,000 pages per minute, eliminating the bottlenecks of manual review. That compresses diligence cycles from weeks to hours, so you can price and respond before competitors. See The End of Medical File Review Bottlenecks for what this means in practice.
• Accuracy and completeness: AI applies identical rigor on page 1 and page 1,500. Every value includes a citation for verification. This reduces leakage from missed reopenings, mis-coded recoveries, or “hidden” ALAE conventions.
• Cost: By removing repetitive extraction and mapping, teams refocus on analytics and negotiation. Research cited by Nomad indicates intelligent document processing often delivers triple-digit ROI, with investment recouped in months. See AI’s Untapped Goldmine: Automating Data Entry.
• Selection and terms: With sharper visibility into attachment behavior, tail risk, and case reserve adequacy, underwriters can adjust price, limits, and terms with confidence — or walk away earlier.
• Scalability: Surge volumes around renewal or M&A no longer require overtime or temporary staffing; Doc Chat scales instantly.

These outcomes echo Nomad’s broader impact in claims and underwriting workflows, described in AI for Insurance: Real-World AI Use Cases Driving Transformation. For reinsurers, the same capabilities translate to better portfolio construction and capital efficiency.

Why Now: AI That Understands Insurance Documents

Past attempts to automate loss run extraction often failed because they assumed uniform structure. Reinsurance is the opposite: the same field is named five ways, or the data you need is implied, not written. Nomad’s approach acknowledges this reality. As explained in Beyond Extraction, document intelligence for insurance requires inference — applying unwritten rules your team uses every day. That’s why Doc Chat is trained on your playbooks and standards, then continuously tuned as your portfolio evolves.

Security, Auditability, and Trust

Reinsurance diligence demands defensibility. Doc Chat delivers page-level citations for every answer and maintains a clear audit trail of how values were derived. Nomad Data maintains SOC 2 Type II certification and integrates with your existing governance frameworks. Answers are explainable, traceable, and easy to verify — critical for model governance, compliance, and internal audit.

How Doc Chat Fits the Reinsurance Portfolio Manager’s Day

A typical day during renewal or acquisition due diligence might look like this:

You receive ten cedent packages: two ZIP bundles of Excel and CSV, five multipage PDFs with embedded tables, one Word document with appendices, and two data rooms with rolling monthly bordereaux. Instead of farming out pieces to different analysts, you upload everything into Doc Chat. Within minutes, you have a normalized, canonical dataset ready for analysis — with AY and RY triangles, LOB splits, development factors, and variance checks against bordereaux totals.

Then you start asking questions. Which cedent’s AY 2017 tail is still moving materially at 72 months? Where are paid-to-incurred ratios spiking at 24 months? Which cedents net recoveries differently? What’s the distribution of reopened claims by LOB and jurisdiction? Every answer arrives with citations so you can confirm the outcome, share with actuarial colleagues, and bring to negotiations.

Implementation: White-Glove, Fast, and Tailored (1–2 Weeks)

Nomad’s deployment is designed for speed and fit. We configure Doc Chat to your taxonomy, portfolio logic, and target outputs, then validate against real cedent files. Typical steps:

• Discovery (Days 1–2): Review your LOB taxonomy, year conventions, canonical fields, FX policy, and reconciliation rules (e.g., how you net ALAE or handle recoveries).
• Preset design (Days 2–5): Build “presets” — customized output formats for loss runs, bordereaux, and triangles — so every run is standardized.
• Pilot on real submissions (Days 5–7): Run two or three cedents end-to-end, validate outputs and citations, and tune mappings.
• Go-live (Days 7–14): Connect to S3/SFTP or secure upload, define SLAs, and train users on real-time Q&A and bulk exports.

Because Doc Chat works out of the box and integrates via modern APIs, the timeline is measured in days, not quarters. As adoption grows, we can push structured outputs into your warehouse and portfolio models. Learn more or get started at Doc Chat for Insurance.

Outputs You Can Count On

Doc Chat produces the artifacts a Reinsurance Portfolio Manager needs to drive decisions and documentation:

• Normalized, line-level datasets with canonical fields (claim ID, policy ID, insured, AY/RY/UY, jurisdiction, peril/cause, paid loss, paid ALAE, case, incurred, recoveries, status, reopen flag, currency).
• Triangle-ready tables for Paid and Incurred (Loss and ALAE), monthly or quarterly, with age-to-age factors, LDFs, and tail factors.
• Variance reports reconciling bordereaux totals, loss runs, and cedent-provided Schedule F extracts.
• Quality checks: currency detection, duplicate identification, missing field diagnostics, and netting conventions.
• Portfolio summaries: cedent scorecards with emergence trends, case-to-paid ratios, reopen rates, and severity curves.
• Audit packet: source citations for every computed value, ready for model governance and review.

From Automation to Advantage: Negotiation and Capital Implications

Better diligence drives better deals. With high-fidelity data at your fingertips, you can quantify tail risk with precision, understand layer behaviors, and structure terms that reflect reality, not averages. This improves capital allocation, informs retrocession strategies, and supports internal reserve decisions. Most importantly, it sharpens selection — the differentiator that separates strong portfolios from merely adequate ones.

Addressing Common Concerns About AI in Reinsurance Diligence

Three questions arise frequently:

1) Will AI “hallucinate” values? Doc Chat answers from your documents, and includes page-level citations so every value is verifiable. When asked to return structured fields, the model performs exceptionally well, as discussed in AI’s Untapped Goldmine: Automating Data Entry.

2) Can it handle wildly inconsistent formats? Yes — that is precisely what it’s built for. See Nomad’s deep dive on inference-driven document intelligence in Beyond Extraction.

3) Is it secure and compliant? Nomad maintains SOC 2 Type II compliance and provides document-level traceability. IT and compliance teams retain full control over data flows, which can be isolated by line of business or geography.

A Day-Zero Win: Bulk Loss Run Data Digitization for Portfolio Review

Many reinsurers begin with a quick-win scope: “Take last renewal season’s cedent files and produce a normalized dataset and triangles.” Within a week or two, Doc Chat turns a backlog of disparate files into a single, analyzable asset, complete with audit trails. That foundation accelerates the next renewal cycle, shortens quote turnaround time, and frees analysts to focus on pricing and negotiation — not document wrangling. For teams actively evaluating bulk loss run data digitization for portfolio review, day-zero wins de-risk the journey and prove value immediately.

Comparing Approaches: Why Doc Chat Outperforms Generic Tools

Generic OCR or RPA tools struggle with unstructured, multi-layout files and unwritten domain rules. Doc Chat was built for insurance and trained to apply your standards, not generic templates. It excels along the four dimensions that matter most to a reinsurance Portfolio Manager:

• Volume: Ingest and analyze entire submission packages — fast — with no headcount ramp.
• Complexity: Extracts and reconciles in the presence of inconsistent naming, missing fields, mixed currencies, and layered coverage.
• Thoroughness: Surfaces every reference to coverage, liability, damages, and recoveries to eliminate leakage and blind spots.
• Interactivity: Real-time Q&A enables iterative, hypothesis-driven diligence, like having a tireless analyst on demand.

Example: Normalizing Three Cedents in One Afternoon

Consider three cedents submitting for an aggregate XoL treaty:

Cedent A: Quarterly PDFs with embedded tables; incurred includes ALAE; currency in euros; cause-of-loss in free text.

Cedent B: Monthly Excel with separate tabs for loss, ALAE, and recoveries; some fields missing for older months; status codes “C,” “O,” “R.”

Cedent C: CSV from a data room export; policy numbers reformatted post-migration; paid reported net of salvage; decimal separators vary by month.

Upload all three into Doc Chat. Within minutes, you have unified claim-level data mapped to your schema, monthly AY/RY triangles, FX-normalized Paid/Incurred and ALAE, and a reconciliation report showing that Cedent B’s bordereaux totals differ from the loss run by 0.8% in 2021Q4 due to a missing recoveries tab — with citations to the exact sheets and rows. You can now price the layer using your actuarial workbench, supported by defensible evidence.

Beyond Extraction: Continuous Intelligence for the Portfolio

Once loss runs and bordereaux are normalized, reinsurers often extend Doc Chat into ongoing monitoring. Monthly or quarterly updates flow automatically; variances trigger alerts; reopenings and late reporters are flagged. Portfolios become more predictable, and surprises surface earlier. This is how document intelligence evolves from a one-time diligence utility into a strategic capability across the reinsurance lifecycle, echoing themes in Reimagining Claims Processing Through AI Transformation.

Natural SEO Phrases Your Team Is Already Searching

We hear these phrases from reinsurance professionals every week, and Doc Chat was built to answer them:

• “AI to extract claims from loss runs for reinsurance that can handle PDFs, Excels, and monthly bordereaux in one flow.”
• “We need bulk loss run data digitization for portfolio review — ASAP — without hiring an army.”
• “Can we normalize ceded loss data with AI and output directly to our canonical schema?”
• “We need automated loss bordereaux analysis reinsurance teams trust, with page-level citations for model governance.”

Why Nomad Data Is the Best Partner for Reinsurers

With Doc Chat, you are not just buying software. You gain a partner who co-creates with you and evolves as your portfolio does. Five reasons reinsurers choose Nomad:

• White-glove onboarding: We codify your rules and mapping standards. You get a solution tuned to your workflows, not a one-size-fits-all tool.
• Rapid time-to-value: Most teams are live in 1–2 weeks, often delivering a first portfolio review in days.
• Explainability: Every value is backed by a citation. Regulators, auditors, and rating agencies expect nothing less.
• Scalable infrastructure: Built to process millions of pages reliably, with enterprise-grade security and modern integrations.
• Proven in insurance: See real-world impact across complex claims and underwriting in our published case studies and blogs.

Integration and Data Flow

Doc Chat supports flexible integration patterns. Many reinsurers start with secure web uploads for diligence sprints. As usage expands, Nomad connects to your storage (e.g., secure object stores or SFTP), writing structured outputs to your warehouse and analytics tools. Real-time Q&A remains available through the UI and APIs, so portfolio managers can pivot between self-serve questions and batch exports without friction.

Governance: Consistency That Scales

One of the hidden costs in reinsurance operations is process inconsistency. Mappings and shortcuts live in analysts’ heads, producing uneven results and slow onboarding. Doc Chat institutionalizes best practices — through presets, extraction rules, and validation logic — so every new analyst operates with the judgment of your best veteran. This is how you safeguard institutional knowledge, standardize portfolio reviews, and lift the entire team’s performance.

From Proof to Production: A Pragmatic Path

We recommend a three-step path for reinsurance organizations:

• Proof: Pick 2–3 cedent submissions representative of your portfolio. Run end-to-end extraction, normalization, and Q&A. Validate citations.
• Pilot: Expand to a full renewal set or a prospective portfolio acquisition. Integrate with your actuarial models and pricing workbench.
• Production: Connect automated feeds for monthly or quarterly updates, with alerts for variances and late developments.

At each stage, your team maintains control and visibility. The goal is not just automation, but better portfolio decisions supported by defensible evidence.

Bottom Line: Turn Submissions Into Competitive Advantage

Reinsurance is a data advantage business. The cedent who reports the loudest isn’t always the best partner; the file that looks tidy at first glance may hide tail risk in the footnotes. Doc Chat turns messy submission packets into a clean, trusted view of loss history and development — fast enough to matter in the real world of auction deadlines and renewal sprints.

If your team is actively searching for AI to extract claims from loss runs for reinsurance, wants to accelerate bulk loss run data digitization for portfolio review, needs to normalize ceded loss data with AI, or is ready for automated loss bordereaux analysis reinsurance professionals can trust, now is the time to see Doc Chat in action. Explore the product at Doc Chat for Insurance and dive deeper into Nomad’s approach in AI for Insurance: Real-World AI Use Cases Driving Transformation.

When document complexity meets portfolio urgency, Doc Chat gives reinsurance portfolio managers the speed, accuracy, and explainability they need to win — without sacrificing rigor.