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

For a Chief Underwriting Officer managing reinsurance portfolios, the pressure is relentless: January 1 renewals, limited market windows, and a crush of heterogeneous cedent submissions arriving in every imaginable format. Buried in those PDFs, spreadsheets, and scans are the truths that define your portfolio—loss frequency, severity, development patterns, ALAE behavior, subrogation flows, and true tail risk. The challenge isn’t just reading the documents. It’s turning mixed-format Loss Run Reports, Schedule F (NAIC) pages, Cedent Loss Bordereaux, and Claim Register Exports into a normalized, auditable dataset that drives pricing, capacity, and treaty structure decisions—at scale.

Nomad Data’s Doc Chat for Insurance was built for that exact challenge. It is a suite of purpose‑built, AI‑powered agents that automates end‑to‑end document review, extraction, normalization, and analytics across the entire submission. Doc Chat ingests entire claim files and loss runs—thousands of pages and rows at a time—answers natural language questions instantly, and produces standardized outputs that drop straight into your portfolio review and pricing models. If you are searching for AI to extract claims from loss runs for reinsurance or a way to achieve bulk loss run data digitization for portfolio review, this article explains how CUOs and their teams can finally operate at portfolio speed and precision.

The CUO’s Reinsurance Reality: Nuances That Break Manual Processes

Reinsurance due diligence is not a simple OCR problem. As a Chief Underwriting Officer, you are asked to synthesize a fragmented reality: cedents differ in definitions, ledger structures, and claims handling philosophies, even when they write the same lines. You face compressed cycles and complex placements: proportional versus non-proportional, per-risk versus catastrophe, aggregate stop loss, clash, facultative layers, inuring treaties, and varying treatment of ALAE/ULAE. Add to that multi-currency exposures, occurrence versus claims-made triggers, and claim system migrations that change coding midstream, and the complexity grows exponentially.

Loss runs rarely reconcile out-of-the-box. One cedent’s “paid ALAE” includes surveillance and IMEs; another pushes those into indemnity. Case reserves can be gross or net of salvage/subrogation. Closed-without-payment may still show ALAE leakage. Bordereaux may summarize by accident year, while claim registers report by report year. Footnotes on NAIC Schedule F can fundamentally change your interpretation of intercompany reinsurance, credit risk, or aging of recoverables. For tail lines, development lags and coding practices matter as much as raw incurred. Traditional manual review buckles under these nuances because they demand inference and domain judgment, not just extraction.

How This Work Is Handled Manually Today

Most reinsurance underwriting shops still run this process with armies of analysts in spreadsheets. The typical workflow looks like this: receive a stack of PDFs and Excel files, copy/paste fields into a working template, rewrite inconsistent headers, create pivot tables, reconcile totals to the cedent’s Cedent Loss Bordereaux summary, and then redo mappings when a second tranche of “final” loss runs arrives with different column names and added fields. Every handoff adds risk—typos, missed filters, broken formulas, forgotten currency conversions—and every reconciliation step steals hours you can’t afford.

Analysts then try to build an underwriting narrative: compare paid-to-incurred ratios by line and accident year, calculate incurred but not reported (IBNR) signals from late-reporting claims, identify reopen rates, isolate catastrophe-impacted months, and spot claims > attachment that may pierce layers in non-proportional structures. They cobble together severity curves for key segments and ask cedents for missing details. Each iteration takes days—especially when cedents deliver Claim Register Exports with tens of thousands of rows and note fields that must be parsed for cause, peril, or litigation posture. As volumes surge, cycle time expands. The underwriting window does not.

Why Generic OCR Falls Short—and Why Inference Matters

Many teams have tried generic OCR or template-based IDP tools and found them brittle. Reinsurance loss runs vary too much in structure, nomenclature, and embedded logic. As Nomad Data explains in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, this work is less about “finding the number on the page” and more about reconstructing the cedent’s unwritten rules. You need a system that can read like an experienced reinsurance analyst: detect whether ALAE is inside or outside limits, infer accident versus report year from context, and reconcile footnotes that alter the economic meaning of the numbers. That is inference, not template matching.

AI to Extract Claims from Loss Runs for Reinsurance—How Doc Chat Automates the Entire Pipeline

Doc Chat is not a generic summarizer. It is a set of trained agents, tuned to reinsurance workflows, that ingests large, messy submissions and returns clean, normalized, analysis-ready data plus defensible citations. For the CUO, this means you can ask, “Which cedent segments drive long-tail severity above our selection?” and get an answer in seconds, backed by page-level references.

Here’s what happens under the hood when Doc Chat processes reinsurance due diligence materials:

1) Ingest everything, at once. Doc Chat ingests PDFs, Excel, CSV, and scanned images across Loss Run Reports, Cedent Loss Bordereaux, Claim Register Exports, and Schedule F (NAIC). It handles multi-tab spreadsheets, merged cells, password-protected worksheets (with provided credentials), and scanned tables with advanced OCR. It processes entire claim files and attachments, including demand letters and legal notes if provided as part of the submission.

2) Normalize to a reinsurance-ready canonical schema. Fields are automatically mapped to a standard structure: claim_id, policy_number, occurrence_id/event, loss_date, report_date, jurisdiction, coverage, cause/peril, line of business, paid_indemnity, paid_ALAE, case_indemnity, case_ALAE, total_incurred, subrogation, salvage, recovery_type, reopen_flag, closed_flag, currency, FX_rate_as_of, cedent_id, treaty_id, participation_percent, attachment_point, limit, and layer descriptors. Doc Chat distinguishes occurrence versus claims-made triggers, recognizes accident year versus report year, and flags the cedent’s ALAE treatment (inside/outside limits, pro-rata share) when documentation allows.

3) Reconcile, deduplicate, and cross-check. The system identifies duplicates across multiple loss run versions, resolves claim key collisions, and validates totals against Cedent Loss Bordereaux summaries. It cross-references Schedule F (NAIC) disclosures to highlight counterparty credit considerations or unusual ceded balances. If event tags exist (e.g., hurricane code, wildfire catastrophe ID), Doc Chat stitches these to the claim-level records to enable layer pierce analysis on non-proportional treaties.

4) Standardize currencies, dates, and development views. Multi-currency portfolios are normalized using as-of FX rates with transparent assumptions. The engine builds triangles by accident year and report year for paid, case, and total incurred—at cedent, line, treaty, and portfolio levels—so you can see development speed and tail.

5) Answer underwriting questions in real time. With the normalized dataset, Doc Chat supports natural language Q&A: “Show top 25 open claims by case reserve in the layer attaching at $1M for Cedent A,” “List claims reopened more than once in AY 2017 across casualty lines,” “Estimate link ratios for paid severity by AY for workers comp,” or “Which claims were closed and then reopened after catastrophic events?” Every answer includes citations to the source page or cell, so reviews and audits move quickly.

6) Export and integrate seamlessly. Outputs publish to CSV, Parquet, or JSON and plug directly into your actuarial, capital, and pricing models. Doc Chat also pushes structured extracts into data warehouses and BI tools through modern APIs, enabling portfolio dashboards for CUOs and senior leadership.

Bulk Loss Run Data Digitization for Portfolio Review—What Normalization Really Means

“Normalization” must go beyond header renaming. For reinsurance, it means creating economic comparability across cedents with different operating philosophies. Doc Chat codifies the nuances your team already uses when judging cedent quality and performance:

ALAE/ULAE treatment. Identify whether ALAE is inside limits or outside, whether it is shared pro-rata or borne fully by the insurer, and how that treatment interacts with treaty terms. These choices significantly affect layer exhaustion and non-proportional pricing.

Gross vs. net lenses. Create consistent lenses—gross, net of subrogation/salvage, and ceded—so you can measure true severity and net exposure for each treaty or portfolio segment.

Occurrence vs. claims-made. Correctly classify the trigger and then assemble the appropriate time buckets—accident versus report versus underwriting year—and the link ratios that matter for each line.

Reopen behavior and late-reported claims. Track reopen flags and reporting lags to detect cedents with slow reporting, systemic reopen risk, or adverse litigation cycles.

Event and layer mapping. Tie claims to catastrophe IDs or occurrence groupings, and then evaluate per-risk and catastrophe reinsurance layer interactions. Compute when and how layers might be pierced given paid/case trajectories.

By performing this level of normalization, Doc Chat supports automated loss bordereaux analysis reinsurance workflows that actually stand up to peer review and treaty audits. You are not just digitizing; you’re improving comparability and underwriting signal quality.

From Submission to Quote: A Doc Chat-Enabled Workflow for a CUO

Day 0–1: Intake and triage. Your team drops the cedent’s submission package—Loss Run Reports in mixed PDF/Excel, Cedent Loss Bordereaux summaries, a year-over-year Claim Register Export, and relevant pages from Schedule F (NAIC)—into Doc Chat. In minutes, the system classifies, extracts, and begins normalization.

Day 1: Automated completeness check. Doc Chat compares expected fields to what’s present. If essential columns are missing—e.g., cause/peril or reopen flags—it generates a gap memo your broker team can send back to the cedent. If policy form notes indicate a change in ALAE treatment year-over-year, Doc Chat flags it and links the source page for confirmation.

Day 2: Normalized dataset and first insights. Your analysts open the portfolio dashboard. They see triangles by line and cedent, paid-to-incurred ratios, top-50 open claims, claim count trends by AY, and an early warning list of claims whose development patterns suggest potential layer pierce. Immediate Q&A yields lists and tables you can export straight into pricing worksheets.

Day 3–4: Pricing and structure iterations. As the CUO calibrates layer structures, Doc Chat recalculates expected exhaustion and notional losses under new attachment points. It answers questions like “What if we move the attachment from $2M to $3M on GL?” and “How does excluding medical malpractice change tail risk?”

Day 5–7: Governance, memos, and auditability. The system produces a defensible underwriting memo with embedded citations, reinforcing governance standards and supporting your reinsurance committee review. If actuaries or capital teams want additional cuts, you ask Doc Chat and append the outputs. Cycle time compresses from weeks to days without sacrificing diligence.

Where Doc Chat Delivers Portfolio-Scale Analytics Out of the Box

Doc Chat translates normalized data into the metrics CUOs need to manage tail risk and capacity allocations:

Development analytics. Accident year and report year triangles for paid, case, and incurred, with link ratios and tail selections that align to your playbook, not someone else’s template.

Severity and frequency. Distribution curves and top-loss lists, by cedent, line, and treaty, including trend views and breakout by jurisdiction or peril when the data allows.

Attachment and layer stress. Fast simulations showing expected hit frequency and potential exhaustion given historical development paths; instant recalculation when you change attachment points or limits.

Catastrophe tagging. Where cedents provide event IDs or catastrophe months, Doc Chat separates cat and non-cat experience to avoid contaminating your non-cat pricing selections.

Subrogation and salvage controls. Identify cedents with unusually high net severity after recoveries, or inconsistent recovery recording across AYs—signals of operational friction or data quality issues.

One Paragraph, Two Lists: What Makes Reinsurance Loss Runs So Hard

Even experienced teams struggle because hidden variations multiply across hundreds of cedent files. In reinsurance due diligence, you must detect and reconcile issues like:

• ALAE treatment differences (inside/outside limits, pro-rata versus first-dollar) that alter layer economics.
• Gross versus net after subrogation/salvage reported inconsistently, or only at the AY summary level.
• Accident year versus report year views intermingled within the same packet.
• Reopen flags missing or coded as free-text notes in Claim Register Exports.
• Multi-currency portfolios lacking as-of FX; amounts restated mid-triangle without disclosure.
• Event IDs not tied to claims consistently, obscuring catastrophe impacts on non-prop treaties.
• Case reserve philosophies changing after policy form updates—buried in footnotes.
• Different cedent systems across time causing field drift and header changes.

Doc Chat was engineered to find and normalize these differences automatically, so your CUO decisions rely on consistent, defensible inputs.

Automated Loss Bordereaux Analysis Reinsurance Teams Can Trust

For proportional treaties, bordereaux is the source of truth for ceded premiums, losses, and recoveries—until varied definitions erode comparability. Doc Chat conducts automated loss bordereaux analysis reinsurance by aligning cedent-provided bordereaux to normalized claim-level records. It highlights mismatches, quantifies reconciliation gaps, and ensures that your share is calculated against the correct basis. When proportional and non-proportional placements sit side by side (and sometimes inuring), Doc Chat separates the economics so you can price each on its merits and avoid double counting.

Normalize Ceded Loss Data with AI—Down to the Policy Form and Footnote

To truly normalize ceded loss data with AI, Doc Chat traces back assumptions to their sources. If a cedent’s footnote says “paid LAE includes containment costs within indemnity,” Doc Chat repeats this in the normalized metadata and flags it for reviewers. If a law change altered reserve setting practices in a key state, the system tags the impacted periods. This level of documentation supports internal governance, reinsurer/cedent discussions, and regulatory scrutiny—especially when reserve adequacy or tail selections are on the table.

Business Impact for the Chief Underwriting Officer

CUOs care about time, cost, accuracy, and defensibility. With Doc Chat, due diligence moves from labor intensity to intelligence at scale. Teams that once spent 60–80% of their time reconciling, cleaning, and organizing data now start with a normalized dataset and spend their time on pricing, structure, and portfolio strategy. The downstream effect is a faster quote cycle, higher hit ratios on the right business, and better capacity allocation across programs.

In practical terms, organizations see:

• Cycle time reduced from days or weeks to minutes and hours, even for large, multi-cedent packets.
• Lower loss-adjustment and due-diligence expense by eliminating manual copy/paste, header mapping, and repetitive reconciliation.
• Accuracy improvements through page-level citations, automated cross-checks, and removal of human fatigue effects on page 1,500.
• Scalability during peak renewal seasons without overtime or headcount spikes.

These outcomes mirror the patterns discussed in Reimagining Claims Processing Through AI Transformation and AI’s Untapped Goldmine: Automating Data Entry: enormous time savings, consistent quality, and happier teams who are freed to focus on higher-value judgment.

Real-Time Q&A Across Massive Submissions—Not Just Summaries

CUOs need answers, not just data dumps. Doc Chat’s real-time Q&A lets you interrogate entire submissions instantly: “Which AYs show late emergence in GL for Cedent B?” “What is the paid-to-incurred ratio for workers comp AY 2016–2019 by jurisdiction?” “List all claims with case reserves over $1M that had no subrogation activity.” Each answer links back to the source content—pages in a Loss Run Report, cells in a Claim Register Export, or paragraphs in an explanatory cover letter—so your team can audit and move forward quickly. This is the same efficiency cited by GAIG in Reimagining Insurance Claims Management: Great American Insurance Group Accelerates Complex Claims with AI: fast, accurate answers with transparent citations.

Security, Compliance, and Auditability

Reinsurance due diligence involves sensitive claim and policyholder data. Doc Chat is built for enterprise security and governance, with SOC 2 Type 2 controls and document-level traceability. Every transformation—currency conversions, field mappings, deduplication steps—is logged. Exports include data lineage so actuaries, portfolio managers, and auditors can reproduce results. Page-level citations provide legal and regulatory defensibility, addressing one of the CUO’s biggest concerns: can we prove how we got here?

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

Doc Chat stands out on volume, complexity, and service. It ingests entire claim files and loss runs—thousands of pages at a time—and maintains accuracy from page 1 to page 10,000. It is trained on reinsurance-specific challenges like ALAE treatment, occurrence grouping, attachment and limit logic, proportional versus non-proportional economics, and mixed AY/RY views. Most importantly, the Nomad team delivers a white glove implementation that captures your playbooks and coding standards, not a generic one-size-fits-all tool.

Implementation typically completes in 1–2 weeks. We start with drag-and-drop proofs where your team sees their own submissions transformed and validated with citations. Then we configure your canonical schema, map your recurring cedent idiosyncrasies, and integrate with your data warehouse and BI tools. Because Doc Chat is agent-driven and API-first, it becomes a durable part of your underwriting operating model rather than a point solution. For a broader view of how AI is transforming insurance operations, see AI for Insurance: Real-World AI Use Cases Driving Transformation.

How Doc Chat Compares to Prior Approaches

Before Doc Chat, teams tried rule-based extractors or “PDF to Excel” services. Those solutions cracked on the first unexpected header or footnote and could not reconcile economic facts that spanned multiple documents. As described in Beyond Extraction, document intelligence requires inference—detecting meaning from cross-references, footnotes, and patterns. Doc Chat is built to reason across entire submissions, not just scrape single pages.

Frequently Asked Questions for CUOs and Reinsurance Teams

How does Doc Chat handle mixed-format submissions?

Doc Chat ingests PDFs, Excel files, CSVs, and scans simultaneously, finds tables across pages, preserves row integrity, and logs all transformations. It can also parse cover letters and slip notes that specify ALAE treatment, limits, and participation percentages—information that often dictates how numbers should be interpreted.

What if the Loss Run Report is a poor scan?

Advanced OCR routines improve legibility and reconstruct tables even when borders are missing or skewed. If quality prevents reliable extraction for specific sections, the system flags uncertainties with confidence scores and prompts for clarification, keeping reviewers in the loop.

Can Doc Chat compare loss runs against Schedule F (NAIC)?

Yes. Doc Chat reads and extracts key fields from Schedule F and cross-checks ceded balances and disclosures against cedent-provided losses and recoverables. It highlights anomalies and creates a review queue with citations to both sources.

How do you ensure we can trust the AI?

Every answer includes a link to its source page or cell. This page-level explainability, discussed in our GAIG case study, fosters trust with compliance, legal, and reinsurance committees. Additionally, Doc Chat does not “hallucinate” values; it extracts and computes only from provided materials, surfacing confidence and exception flags where appropriate. See The End of Medical File Review Bottlenecks for performance at massive scale.

Will Doc Chat learn from our data?

Doc Chat mirrors your playbooks and preferred schema during configuration. Customer data is not used to train third-party foundation models by default. We maintain enterprise-grade privacy and security, including SOC 2 Type 2 controls, so your underwriting logic remains your advantage.

How does Doc Chat integrate with our modeling stack?

Exports to CSV, Parquet, and JSON fit into actuarial and pricing models. APIs push to your warehouse and BI tools, and you can trigger re-normalization jobs when revised loss runs arrive. Many CUOs operate hybrid: instant Q&A for exploratory analysis, scheduled extracts for nightly model runs.

A CUO’s Checklist for Choosing AI in Reinsurance Due Diligence

When evaluating solutions for bulk loss run data digitization for portfolio review, pressure-test these capabilities:

Scale without drift. Does accuracy hold at 10,000+ pages and 1M+ rows? Doc Chat was engineered for portfolio scale.

Economic inference. Can the tool infer ALAE treatment, attachment logic, occurrence grouping, and AY/RY distinctions from context and footnotes?

Citations and lineage. Will every data point be traceable to its source for audit, negotiation, and regulatory review?

Customization speed. Can it be tuned to your playbooks in 1–2 weeks, not months?

Human-in-the-loop. Does it guide analysts when confidence is low and route exceptions for targeted follow-up?

Tying It All Together: From Data to Portfolio Decisions

Reinsurance underwriting is ultimately about judgment under uncertainty. But judgment is only as good as the data it rests on. Doc Chat compresses the journey from raw submissions—Loss Run Reports, Schedule F (NAIC), Cedent Loss Bordereaux, Claim Register Exports—to normalized, analysis-ready datasets that defend your decisions. It puts real-time answers and citations in front of your analysts and actuaries, so your CUO discussions can focus on risk appetite, structure, and price adequacy rather than whether Column G actually meant case ALAE last year.

This is why carriers adopting Doc Chat report dramatic cycle-time reductions and better underwriting outcomes, consistent with the patterns outlined in Reimagining Claims Processing Through AI Transformation. When the manual toil disappears, the team’s best thinking rises to the top—quickly.

Get Started—See Your Own Submissions Transformed

If your team is actively searching for AI to extract claims from loss runs for reinsurance, a better way to normalize ceded loss data with AI, or truly automated loss bordereaux analysis reinsurance at portfolio scale, it’s time to see Doc Chat in action. In a short working session, we can process a representative packet, show normalized outputs, and answer your underwriting questions with citations in minutes.

Learn more and request a working demo at Doc Chat for Insurance. Your next renewal season can be driven by clarity, speed, and confidence—at reinsurance scale.