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

For reinsurance leaders, the biggest bottleneck in pre-bind diligence and portfolio steering is not a lack of data—it’s the overwhelming glut of unstructured, inconsistent, and late-arriving documents. Loss run reports, cedent loss bordereaux, claim register exports, and Schedule F exhibits all arrive in different formats, currencies, and levels of granularity. The result: high-stakes decisions made under time pressure with partial visibility into loss development, late emergence, and tail risk. Nomad Data’s Doc Chat changes the game by automating bulk extraction and normalization of ceded loss data from hundreds of cedent submissions, turning messy files into portfolio-grade intelligence in minutes.

This article shows Chief Underwriting Officers how to use AI to extract claims from loss runs for reinsurance, perform bulk loss run data digitization for portfolio review, normalize ceded loss data with AI, and run automated loss bordereaux analysis (reinsurance) at true portfolio scale. We’ll cover the nuances of the reinsurance due diligence challenge, how manual processes are handled today, exactly how Doc Chat automates this work, the quantifiable business impact, and why Nomad Data is uniquely positioned to deliver white-glove, 1–2 week implementations that meet the demands of modern reinsurance underwriting.

The CUO’s Reality: Diverse Cedent Files, Shrinking Timelines, Hidden Tail Risk

In reinsurance, underwriting windows keep narrowing while submission complexity explodes. A Chief Underwriting Officer must make layered decisions—by cedent, treaty, and retro program—using inputs that arrive in an unpredictable mix of formats:

• Loss Run Reports: Claim-level PDFs or spreadsheets with bespoke column names (e.g., paid loss vs. indemnity paid; ALAE/ULAE split; recovery lines; salvage/subrogation).
• Cedent Loss Bordereaux: Monthly or quarterly bordereaux with partial fields, varying delimiters, and shifting schema by reporting period.
• Claim Register Exports: CSV extracts from policy/claims systems with cryptic field codes, missing headers, or multiple rows per development update.
• Schedule F (NAIC): Used to assess cedent credit, reinsurance recoverables aging, and counterparty exposures—often provided as static PDFs.

The nuance is not merely volume. It’s the combination of inconsistent semantics and the longitudinal nature of claims. For example:

• The same cedent may define “incurred” differently across submissions.
• Reopened claims or reserve increases appear as a new row in one file and as an updated total in the next.
• ALAE can be combined with indemnity at the cedent level or split by transaction.
• CAT codes and perils vary (internal codes vs. ISO cause codes), complicating cat tail analysis across cedents.
• Currencies, valuation dates, and FX treatment vary—and are rarely documented consistently.
• Treaty context (occurrence/aggregate limits, attachment points, reinstatements, facultative vs. treaty splits) may live in a separate slip, schedule, or endorsement, not in the loss file.

For a CUO evaluating multiple programs in parallel, these differences directly affect assessments of late emergence, frequency-severity mix, large loss thresholds, and expected tail. Without consistent fields, development triangles, and defensible audit trails, pricing and capacity decisions risk becoming guesswork.

How the Process Is Handled Manually Today

Most reinsurers still mobilize armies of analysts to wrangle cedent files into temporary spreadsheets. The typical manual workflow looks like this:

1) Receive mixed-format documents: PDFs of Loss Run Reports, XLSX/CSV cedent loss bordereaux, and claim register exports with unknown delimiters or odd encodings. Schedule F (NAIC) arrives as PDF for reference.
2) Manually configure OCR for scanned PDFs with varying quality; copy/paste rows; hope no zero-width characters or footers derail formulas.
3) Build ad-hoc column crosswalks—mapping “Paid” vs. “Paid Loss (Indemnity)” vs. “Total Paid,” translating internal cedent codes into standard LOB and cause-of-loss values.
4) Attempt deduplication and longitudinal tracking: merge multiple rows for the same claim across monthly bordereaux; stitch together reopenings and reserve movements.
5) Normalize currencies and valuation dates; apply makeshift FX if needed; handle missing metadata via email back-and-forth with cedents.
6) Create pivot tables, frequency-severity distributions, and makeshift development triangles to estimate IBNR or late emergence risk.
7) Repeat the entire process when refreshed files arrive—often with schema drift or new field definitions.

This approach is fragile, slow, and error-prone. Deadlines force shortcuts. Important signals—like claim reopen rates, pattern of reserve strengthening, or CAT tail clustering—often remain buried. Worse, the knowledge lives in analysts’ heads and brittle spreadsheets, not in a standardized, audit-ready system that supports consistent decision-making across the underwriting organization.

Doc Chat: End-to-End Automation for Bulk Loss Run and Bordereaux Intake

Nomad Data’s Doc Chat is a suite of purpose-built, AI-powered agents that ingest, extract, and normalize entire reinsurance submission packages—across thousands of pages and dozens of files—so CUOs can move from document chaos to portfolio-grade answers in minutes. Where spreadsheets and generic OCR fall apart, Doc Chat excels by combining large-scale reading with reinsurance-specific inference. It ingests PDFs, spreadsheets, and mixed archives; builds a standardized ceded-loss schema; and provides immediate, real-time Q&A across the unified dataset.

Unlike one-size-fits-all tools, Doc Chat is trained on your underwriting playbooks and portfolio standards—the “Nomad Process.” It interprets cedent-specific quirks, finds implied values (like reserved but not yet paid ALAE), and enforces your definitions of incurred, paid, and recoveries. This approach is why Doc Chat can process approximately 250,000 pages per minute and still deliver page-level citations for every extracted figure, so underwriting, actuarial, and compliance teams trust the results.

What Doc Chat Extracts and Normalizes from Reinsurance Loss Files

At the heart of portfolio-scale diligence is a clean, consistent dataset. Doc Chat automatically extracts and normalizes fields from Loss Run Reports, Cedent Loss Bordereaux, and Claim Register Exports, then ties insights back to Schedule F (NAIC) where relevant for cedent credit and recoverables context.

Typical standardized outputs include:

• Claim identifiers: cedent claim number, policy number, account/insured, claim status, reopen flags
• Dates: loss date, report date, valuation date, closure/reopen dates
• Cause of loss: ISO/cedent codes mapped to normalized peril taxonomy; CAT indicators and CAT numbers
• Financials: paid indemnity, paid ALAE, total paid; case reserves (indemnity and ALAE); incurred (policy-consistent definition); recoveries, salvage, subrogation
• Treaty context: line of business, coverage part, attachment point, layer, occurrence/aggregate limits, reinstatements, ceded percentage, facultative vs. treaty flags
• Geography: state/province, country, CRESTA (if present), ZIP/postal codes
• Currency and FX: source currency, valuation currency, FX rate, FX date
• Development: time since report, time since loss, reserve and paid movement since prior valuation
• Data quality: missing fields, inconsistent definitions, schema drift across monthly bordereaux

Because cedents use different words for the same idea, Doc Chat builds a robust crosswalk (e.g., “LAE,” “ALAE,” “expense paid,” “defense costs”) and resolves semantics via context. It also recognizes layered reinsurance structures by reading treaty slips, schedules, or endorsements that may be provided separately. This is where most generic tools fail: the true meaning often lives in multiple documents simultaneously. As Nomad explains in “Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs,” document scraping in insurance is about inference across dispersed evidence—not just locating text on a page.

Automated Longitudinal Tracking and Deduplication

Reinsurance due diligence is inherently longitudinal. Claims develop. Reserves strengthen or release. Bordereaux arrive monthly with overlapping rows. Doc Chat maintains persisting claim keys, merges updates, flags reopenings, and compiles development histories automatically. It generates development triangles (paid, incurred, ALAE), highlights late-reported claims, and identifies “long-tail signal” patterns such as reserve creep concentrated in specific causes of loss or geographies.

For CAT programs, Doc Chat clusters losses by CAT code and event date, normalizes peril codes across cedents, and supports tail analysis for high-severity, low-frequency events. For casualty and specialty lines, it isolates large loss cohorts, detects outliers, and quantifies emergence by accident year and report year—practical insights a CUO needs to calibrate pricing and capacity.

Real-Time Q&A Across the Entire Submission Package

Once Doc Chat has ingested and normalized the dataset, underwriting leadership can ask plain-language questions like:

• “Show all claims over $1M incurred with reserve increases in the last six months; group by cause of loss and state.”
• “Which cedents show a 20%+ rise in reopen rates year over year?”
• “List the top 10 largest CAT losses in 2021–2023 and provide page citations from the source loss runs.”
• “Which programs show paid-to-incurred ratios below 40% beyond 24 months from report date?”

Answers return with page-level citations and links back to source pages, so actuaries and CUOs can validate insights in seconds. As highlighted in our client story, “Reimagining Insurance Claims Management,” page-level explainability is crucial for earning trust across compliance, legal, and audit stakeholders.

From Document Chaos to Portfolio-Ready Intelligence

Doc Chat’s automation turns document sprawl into structured, portfolio-grade outputs ready for pricing, capacity allocation, and retrocession planning. Deliverables include:

• Normalized, field-level datasets exported to CSV, Excel, or data warehouses (e.g., Snowflake).
• Automated dashboards for frequency-severity, AY/UY development, large-loss cohorts, and CAT tail clustering.
• Development triangles (paid/incurred/ALAE) and configurable factors to support actuarial methods like chain ladder or Bornhuetter–Ferguson.
• Data quality checks and exception lists for immediate cedent follow-up (missing valuation dates, undefined currencies, negative incurred, inconsistent flags).

This is “AI’s Untapped Goldmine: Automating Data Entry” applied to reinsurance: precise, scalable extraction where the cost curves and timelines shift dramatically in your favor.

How Nomad Data’s Doc Chat Automates the End-to-End Process

Doc Chat’s reinsurance-focused agents orchestrate the full pipeline from ingestion to insight:

1) Ingest: Drag-and-drop PDFs and spreadsheets (Loss Run Reports, bordereaux, claim registers, Schedule F). Doc Chat can consume thousands of files concurrently—no headcount surge required.
2) Classify: Auto-detect cedent file types, reporting periods, currencies, and likely schema.
3) Extract: Apply high-accuracy OCR where needed and parse tables, footnotes, and attachments. Identify entity names, policy numbers, CAT codes, and claim movement details buried in narrative.
4) Normalize: Map cedent-specific fields to your standard schema (paid, incurred, ALAE vs. LAE, recoveries), align currencies/FX, reconcile rounded totals, and harmonize cause-of-loss taxonomies.
5) Resolve Longitudinal Continuity: Merge monthly updates, deduplicate rows, track reopenings, and compute development metrics.
6) Cross-Check & Validate: Reconcile subtotals, tie back to page-level citations, flag anomalies (e.g., negative paid, missing loss dates), and compare against cedent-provided aggregate summaries.
7) Enrich & Analyze: Build triangles, compute severity distributions, quantify late emergence, and segment by LOB, layer, peril, or geography.
8) Answer in Real Time: Use natural language Q&A across the unified dataset with hyperlinks to the exact page in the original file.

Because Doc Chat is tuned to reinsurance workflows, it captures the unwritten rules of your best analysts—codifying them into consistent, repeatable steps. As we note in “Beyond Extraction,” the real challenge is inference across disparate documents. Doc Chat institutionalizes that expert judgment.

The Business Impact for Reinsurance CUOs

When a CUO can turn entire submission packages into consistent, validated data in minutes, the strategic advantages compound.

• Time-to-Quote: Move from days/weeks of manual wrangling to same-day portfolio views, even during seasonal surge.
• Cost: Reduce reliance on large analyst teams and overtime. Redeploy experts to higher-value tasks like pricing strategy and portfolio steering.
• Accuracy: Page-level citations, consistent definitions, and automated anomaly checks reduce leakage and rework.
• Negotiation Leverage: Quickly surface late-emerging patterns, reserve creep, and CAT clustering to inform terms, attachments, reinstatements, and pricing.
• Portfolio Resilience: Compare cedents on reopen rates, paid-to-incurred ratios, and development shapes; steer capacity to best-performing programs.
• Scalability: Instantly absorb surge volumes without adding headcount—critical for peak renewal seasons.

In our broader claims automation work, carriers report shifting multi-day document reviews to minutes with sustained accuracy, as described in “Reimagining Claims Processing Through AI Transformation.” The same speed and consistency benefits accrue in reinsurance due diligence—now applied to ceded loss history at scale.

Why Nomad Data Wins: Speed, White-Glove Deployment, and Defensible Outputs

CUOs adopt Doc Chat for three reasons: speed to value, tailored implementation, and defensibility.

1–2 Week Implementation: We start with a small set of representative cedent files and your target schema. In days, Doc Chat ingests, normalizes, and returns verified datasets with page-level citations—no data science staffing required. Once validated, we scale to additional cedents and automate exports into your underwriting workbench or data warehouse.

White-Glove Service: The Nomad team interviews your actuaries, portfolio managers, and analysts to capture unwritten rules and nuanced definitions. We codify this expertise into Doc Chat presets and QA steps that mirror your internal standards. The goal is a solution that fits like a glove, not a toolkit you have to assemble.

Security & Compliance: Nomad Data maintains robust security controls, including SOC 2 Type II practices. Outputs are audit-ready with time-stamped provenance and page-level citations for each extracted value, preserving trust with internal model governance, compliance, and regulators.

Purpose-Built Inference: Generic OCR and off-the-shelf parsing break under schema drift and cedent idiosyncrasies. Doc Chat’s agents are designed for insurance complexity, reading treaty language, endorsements, and footnotes alongside bordereaux to extract true meaning—not just numbers on a page.

Key Use Cases for the Chief Underwriting Officer

Doc Chat supports work patterns that matter most to reinsurance leadership:

Pre-Bind Due Diligence: Perform bulk loss run data digitization for portfolio review across all cedents in your pipeline. Normalize historical losses, identify late emergence, and validate cedent narratives before you bind.

In-Renewal Season Triage: Ingest updated submissions overnight. Ask: “Which renewals show adverse development since last quarter?” or “Where did paid-to-incurred ratios deteriorate?” Respond to brokers with evidence-backed positions.

Portfolio Steering: Rank cedents by loss emergence, reopen rates, and development shapes. Allocate capacity where underlying performance and reporting quality warrant it; throttle where tail risk concentrates.

Retro & Capital Optimization: Quantify tail concentration and large-loss volatility to optimize retro purchases. Feed normalized outputs into internal capital models or third-party tools.

Cedent Monitoring: Track schema drift, data quality issues, and reporting timeliness by cedent. Trigger playbooked follow-ups or remediation steps based on Doc Chat’s exception lists.

Commutations & Claims Audits: Create defensible views of incurred history, reserve movements, and recoveries for commutation negotiations or audits—complete with linked source citations.

Schedule F Context: Use Schedule F (NAIC) to add credit risk perspective: aging of recoverables, counterparty concentrations, and overdue balances—captured in the same normalized view as loss development.

What “Normalize Ceded Loss Data with AI” Actually Means

For CUOs, “normalize ceded loss data with AI” is not just a marketing phrase. It means Doc Chat:

• Learns your standard schema and maps cedent-specific fields into it automatically.
• Creates a crosswalk for cause-of-loss codes and line-of-business values across cedents.
• Converts currencies and aligns valuation dates to apples-to-apples comparisons.
• Tracks longitudinal development and compresses it into actuarially useful structures (triangles, AY/UY views).
• Produces traceable answers with page-level citations, so underwriting committees and governance bodies can sign off with confidence.

FAQ: Applying AI to Reinsurance Loss Runs and Bordereaux

How do I use AI to extract claims from loss runs for reinsurance?

Drop Loss Run Reports, Cedent Loss Bordereaux, and Claim Register Exports into Doc Chat. The system auto-detects file types, extracts fields (paid, incurred, ALAE, reserves, recoveries), and returns a normalized dataset with page citations. You can immediately ask portfolio questions and export results to your workbench.

What does bulk loss run data digitization for portfolio review look like?

Doc Chat ingests all cedent submissions at once—hundreds if needed—and produces a single, standardized dataset. From there, frequency-severity curves, development triangles, and tail analyses update automatically, enabling same-day portfolio views during renewal crunch.

How does automated loss bordereaux analysis (reinsurance) work?

Doc Chat reads monthly or quarterly bordereaux, recognizes schema drift, and maintains longitudinal continuity (deduplication, reopen tracking, reserve movement). It builds exception lists for missing or inconsistent data and ties every figure back to source pages.

Can Doc Chat handle scanned PDFs and messy tables?

Yes. High-accuracy OCR and table parsing are built in. Doc Chat resolves headers, footers, and inconsistent spacing, then validates totals against subtotals and summary pages to catch errors early.

How fast is the process?

Doc Chat scales to thousands of files and approximately 250,000 pages per minute. Most CUOs see portfolio-ready outputs within hours, not weeks—reframing what’s possible in renewal season.

Quantifying the Impact: Speed, Cost, and Decision Quality

Organizations adopting Doc Chat consistently report orders-of-magnitude time savings in document-heavy workflows. As described in “The End of Medical File Review Bottlenecks,” what took weeks can now take minutes, with quality improving as machines remain consistent from page 1 to page 10,000. In a reinsurance context, that means:

• Cycle Time: Pre-bind diligence compresses from days/weeks to same-day analysis across all cedents.
• Expense Ratio: Material reductions in manual data prep and overtime; analysts focus on pricing insights, not copy-paste.
• Loss Ratio: Better identification of late emergence, reserve creep, and tail clustering supports improved pricing and capacity decisions.
• Governance: Page-level citations and consistent definitions produce more defensible underwriting files.

The result: faster quotes, sharper negotiations, and a portfolio that reflects actual—not assumed—cedent performance.

Integration Without Disruption

Doc Chat starts simply—drag-and-drop—with no immediate need for system integration. As adoption grows, Nomad integrates with underwriting workbenches, data lakes, or BI tools. Thanks to modern APIs, most integrations complete in 1–2 weeks, not months. The platform exports clean datasets and machine-readable reports your actuaries and capital teams can feed into internal models or external tools.

How We Implement in 1–2 Weeks

Our white-glove process minimizes lift for your team and accelerates time-to-value:

Week 1:
• Discovery with CUO, actuaries, portfolio managers: confirm target schema and definitions.
• Load a representative set of cedent submissions (Loss Run Reports, bordereaux, claim registers, Schedule F).
• Rapid configuration of Doc Chat presets to your rules and QA steps.
• First-pass extraction and normalization; review exceptions and edge cases.

Week 2:
• Finalize crosswalks (LOB, cause-of-loss, currencies).
• Validate longitudinal tracking and development computations.
• Deliver portfolio-ready datasets and dashboards; enable real-time Q&A.
• Optionally wire up API exports to your workbench or warehouse.

Because Doc Chat is a partner, not just software, we continue to refine presets as your portfolio evolves or cedent schemas change. As highlighted in “AI for Insurance: Real-World AI Use Cases Driving Transformation,” the goal is durable impact, not a proof-of-concept that never scales.

A Day-in-the-Life with Doc Chat for a CUO

8:00 AM: Your inbox contains ten renewal submissions—four with complete bordereaux, three with claim register exports, three with scanned loss runs. You upload all files to Doc Chat.

9:00 AM: Doc Chat returns a normalized dataset. You ask, “Which cedents show a 10%+ increase in reserve additions for 2019 AY casualty?” Results appear with per-cedent breakdowns and page citations to the source loss runs.

10:00 AM: You review a CAT program. A single query surfaces all losses over $2M incurred with reserve strengthening in the last quarter, grouped by CAT event. You see late emergence concentrated in two cedents and adjust your capacity and attachment strategy accordingly.

11:00 AM: For a specialty liability treaty, you pull a cohort of reopenings beyond 24 months from report date, confirming a higher-than-peers reopen rate. Armed with evidence, you negotiate stricter reporting requirements and consider a pricing adjustment.

Afternoon: Your team exports the normalized datasets to your underwriting workbench and pushes a subset to the capital modeling team for retro optimization scenarios. No manual rework. No brittle spreadsheets. Just answers.

From Manual to Systematic: Institutionalizing Best Practice

Adjuster shorthand and analyst “tribal knowledge” often drive reinsurance decisions. Doc Chat captures those unwritten rules and bakes them into a scalable process that every underwriter can follow. The result is a defensible, repeatable due diligence pipeline that stands up to internal audits, model governance, and regulatory review—while dramatically accelerating the work.

The Competitive Edge

Reinsurers that master automated loss bordereaux analysis (reinsurance) and bulk loss run data digitization for portfolio review will quote faster, price more accurately, and deploy capital where risk is best understood. In a market where renewal season compression and cat volatility are the new normal, Doc Chat provides a durable operating advantage: portfolio clarity on demand.

Get Started

If your team is wrestling with diverse cedent loss files and shrinking quoting windows, it’s time to see Doc Chat in action. Upload a handful of recent submissions and watch them transform into a portfolio-ready dataset with page-level citations. Learn more or request a tailored walkthrough at Doc Chat for Insurance.