Automating Analysis of Proof-of-Loss Forms to Flag Irregular Submissions for Property & Homeowners and Specialty Lines & Marine — A Field Guide for SIU Investigators

SIU investigators in Property and Homeowners as well as Specialty Lines and Marine face a growing surge of sworn proof-of-loss submissions, each accompanied by sprawling packets of supporting documentation. Within these packets, subtle inconsistencies and omissions can make or break an investigation: a misstated deductible on the proof-of-loss, an invoice date that precedes coverage inception on the declarations page, or a repair receipt that uses boilerplate language recycled from other claims. The challenge is volume, variability, and velocity. Manually inspecting every page decreases speed, increases fatigue risk, and leaves room for costly leakage.

Nomad Data’s Doc Chat was built to solve exactly this problem. Doc Chat is a suite of AI-powered agents that ingests entire claim files, extracts the fields that matter from proof-of-loss forms and related documents, and cross-checks them for irregularities. It can flag incomplete fields, spot unusual patterns, and compare proof-of-loss data against declarations, repair receipts, photos, marine survey reports, bills of lading, and other supporting documentation in minutes. Learn more about Doc Chat for insurers on the product page at Doc Chat for Insurance.

Why the proof-of-loss moment is high risk and high leverage for SIU

For SIU investigators, the sworn proof-of-loss provides a single consolidated statement of claimed damages, basis of loss, and coverage interactions. It is also the moment when inconsistencies are most likely to emerge. In Property and Homeowners, the proof-of-loss often summarizes dwelling or contents loss amounts, additional living expense calculations, endorsements and sub-limits that apply, and policyholder signatures and notarization. In Specialty Lines and Marine, the stakes and the documents differ but the dynamic is similar: a shipper or assured may file loss statements tied to hull damage, cargo spoilage, or general average assessments, referencing survey reports, bills of lading, manifests, charter party terms, certificates of insurance, and correspondence with carriers. Small deviations across these materials can forecast inflated claims, organized fraud, or simple misunderstandings that require fast clarification.

Traditional SIU workflows rely on painstaking reading of proof-of-loss forms, declarations, repair receipts, estimates, prior loss run reports, ISO claim reports, photos, police or fire service reports, public adjuster estimates, vendor invoices, and adjuster notes. When hundreds of pages come in at once or when multiple proofs-of-loss arrive from a catastrophe zone, SIU teams must triage with blunt instruments. Critical early windows are lost, evidence ages, and opportunities to steer an honest claimant toward correction or a suspect file toward deeper inquiry slip by.

Line-of-business nuance: Property and Homeowners

In Property and Homeowners, proof-of-loss review frequently hinges on granular details buried in dense files. Contents inventories must align with receipts, serial numbers, or credit card statements. Additional living expense tallies must match the dates of displacement, hotel folios, or landlord invoices. Water or fire causation must reconcile with photos, restoration vendor notes, and official reports. Endorsements on the declarations page often change the calculus: mold sub-limits, jewelry or fine arts schedules, ordinance or law coverage, wind or hail deductibles by percentage, and actual cash value vs replacement cost determinations can each materially change the proof-of-loss total.

Public adjusters may present sophisticated demand packages with advocacy language. Contractors may submit repair receipts or estimates generated from templates. SIU investigators need to verify that dates of service post-date the loss, that contractor entities are legitimate and licensed, and that receipts are not reused from unrelated claims. Prior loss history matters, too: overlapping item descriptions across prior ISO claim reports or prior contents schedules is a red flag. None of this is obvious until every page of the file is reconciled, which is precisely where automation helps.

Line-of-business nuance: Specialty Lines and Marine

Marine and Specialty Lines intensify complexity. Consider a cargo loss with a proof-of-loss citing particular average. Validation requires linking items claimed to bills of lading, packing lists, manifests, and surveyor findings. Terms like INCOTERMS, seaworthiness concerns, temperature logs for reefer cargo, outturn reports, and notice of protest details introduce specialized logic. For hull and machinery, survey reports, class certificates, maintenance logs, and voyage records must be compared against the proof-of-loss statement. Deductible treatment may depend on the policy form, endorsements, and charter party provisions. Mismatches are common: claimed quantities that exceed manifest counts, repair invoices dated before the vessel returned to port, or survey conclusions that contradict described causation in the proof-of-loss.

The cross-document reasoning required exceeds any single template or keyword search. SIU investigators need systems that read like domain experts, linking the POL to declarations, endorsements, survey findings, bills of lading, and voyage logs to find what does not belong and what is missing.

How the process is handled manually today

In a manual workflow, SIU investigators or assigned analysts:

Open the proof-of-loss and re-key key fields into a working spreadsheet: policy number, insured name, date of loss, claimed amounts by category, signature and notarization details, and inventory line items.
Pull the declarations page to confirm limits, deductibles, endorsements, and schedules that impact the proof-of-loss totals.
Assemble supporting documentation: estimates, repair receipts, photos, invoices, credit card statements, police or fire reports, marine surveyor reports, bills of lading, manifests, and adjuster notes.
Cross-check line items: manufacturer model numbers, serials, quantities, unit prices, taxes, depreciation, and dates of purchase.
Verify reasonableness: Do receipts predate the policy? Do repair costs align with location and date? Do photos align with claimed items and damage description? Is causation consistent across statements and official reports?
Scan for red flags: identical language across multiple receipts, overuse of cash purchases, inconsistent formatting across pages, or references to vendors that cannot be validated.
Document findings and create an SIU referral note, then request EUO, additional documentation, or field inspection if warranted.

This can take hours per file or multiple days for complex Marine or catastrophe-driven residential claims. Under surge conditions, backlogs grow, fatigue sets in, and the likelihood of missing subtle but material inconsistencies increases.

Proof of loss fraud detection: what to look for and why volume beats humans

Recurring risk indicators cut across lines of business yet manifest differently per claim type. The following list highlights patterns that Doc Chat can detect systematically at scale during proof-of-loss fraud detection:

Missing or inconsistent required fields: absent notarization, unsigned attestations, missing policy numbers, or mismatched insured names vs declarations.
Temporal anomalies: repair receipts dated before loss; hotel receipts that exceed known displacement dates; cargo repair invoices before vessel arrival.
Coverage conflicts: claimed categories exceeding policy sub-limits; jewelry or fine arts items claimed without a schedule; mold, ordinance or law, or ALE claims inconsistent with endorsements on the declarations page.
Duplicate content and unusual language patterns: identical phrasing across receipts or medical notes used in separate claims; boilerplate contractor descriptions with minimal variation; content partially reused from another file in the claim system.
Itemization oddities: serial numbers that do not correspond to manufacturer formats; item counts exceeding manifest quantities; luxury items whose purchase proof is missing or inconsistent with financial statements.
Vendor legitimacy issues: non-existent contractor licenses; PO boxes only; disconnected phone numbers; mismatched business names across invoices and receipts; marine surveyor firms that do not appear on professional registries.
Prior loss overlap: previously claimed items reappearing in current contents lists; similar damage narratives in prior ISO claim reports.
Geospatial inconsistencies: photos with metadata that do not map to the loss location; voyage logs inconsistent with the described casualty position.

Humans can and do find these issues, but not reliably under high volume. Automation expands diligence, checking every page in minutes and surfacing the specific inconsistencies that matter.

Flag incomplete proof of loss AI: automating completeness and compliance checks

Doc Chat operationalizes a completeness model tailored to your playbook and forms. For each incoming proof-of-loss, the system verifies that required fields are present and consistent. For Property and Homeowners, checks include insured name, policy number, date of loss, cause of loss, dwelling vs contents vs ALE categorization, claimed amount totals, deductible, signatures, notarization, and any required public adjuster disclosures. For Marine, checks extend to vessel or voyage identifiers, bills of lading references, survey attachments, cause and nature of loss, and any general average or salvage entries. It flags gaps instantly and generates a checklist for outreach.

Unlike simple OCR, Doc Chat reads like an experienced SIU investigator. It applies your internal rules for what constitutes a complete proof-of-loss package, creates standardized checklists, and highlights the absent elements, supporting its findings with page-level citations from the file. This immediately helps claims intake specialists and SIU teams prioritize which submissions need correction, which merit inquiry, and which can proceed to settlement with added confidence.

Compare proof of loss to claim docs: cross-document validation in minutes

Doc Chat does more than validate fields. It compares the proof-of-loss to all supporting documentation and claim system notes. In Property and Homeowners, it reconciles contents inventories to receipts or bank statements, aligns ALE dates to hotel folios, aligns repair estimates to adjuster notes and photos, and ensures declared deductibles and sub-limits match the declarations page. In Marine and Specialty Lines, it aligns cargo counts to manifests and bills of lading, checks surveyor conclusions against causation statements, reconciles repair invoices to voyage timing, and inspects correspondence history for conflicting assertions.

Doc Chat’s real-time Q and A changes the game. Investigators can ask: list all items over 5,000 dollars claimed without receipts; show receipts that predate the policy inception on the declarations page; identify repair receipts using repeated language across unrelated claims; or extract every endorsement that modifies ALE or mold sub-limits. Each answer returns with citations back to precise pages, so verification takes seconds, not hours.

Typical cross-checks Doc Chat performs automatically

In Property and Homeowners:

Declarations to proof-of-loss: deductible amount, limits for Coverage A through D, endorsement presence, scheduled property validation.
Receipts and estimates to photos and notes: model numbers, brands, serials, and prices cross-referenced to imagery and adjuster observations.
Timeline validation: date of loss vs service dates, ALE stay dates, and inspection timelines.
Prior claims: overlap checks against ISO claim reports and internal loss runs for recurring items and narrative reuse.

In Specialty Lines and Marine:

Proof-of-loss to bills of lading and manifests: quantities, marks and numbers, container IDs, commodity descriptions.
Surveyor reports to causation: machinery failure, perils of the sea, improper stowage, reefer temperature variance, general average validity.
Invoices to voyage records: repair location and timing, port calls, laytime or demurrage notes, and whether invoices could be legitimately incurred during the stated timeline.
Policy endorsements and warranties: compliance with navigation warranties, trading limits, crew requirements, or maintenance schedules.

How Nomad Data’s Doc Chat automates this process end to end

Doc Chat ingests entire claim files — thousands of pages of PDFs, images, emails, and spreadsheets — and normalizes them into a machine-readable corpus. It then applies a set of AI-powered agents trained on your SIU playbooks and policy forms to extract, cross-check, and summarize. Key steps include:

1. Intelligent intake: Doc Chat detects document types on the fly — proof-of-loss forms, declarations, repair receipts, estimates, police or fire reports, marine survey reports, bills of lading, manifests, EUO transcripts, demand letters, and correspondence — without manual sorting. It tags each document and associates it to relevant claim IDs and policy numbers.

2. Structured extraction: Using your field schema, Doc Chat extracts proof-of-loss headers, item-level details, totals, and attestations; pulls endorsements and sub-limits from the declarations and policy; and captures dates, parties, and amounts from receipts and invoices. Extraction is consistent across wildly different layouts.

3. Cross-document validation: The agent compares proof-of-loss entries to supporting documentation and claim notes, highlighting mismatches, missing support, or suspicious patterns. It can verify quantities across manifests, ensure model numbers match photos, or find contradictions between EUO statements and repair records.

4. Risk signals and triage: Based on your SIU rules, Doc Chat computes a risk score and generates recommended next steps: request additional receipts, verify contractor licensing, schedule field inspection, initiate EUO, or escalate to SIU. All signals include traceable citations.

5. Investigator-friendly outputs: The system produces a standardized proof-of-loss variance report, an exceptions dashboard, and a concise SIU referral summary that can be exported to the claim system. Each point is linked to the exact pages from which it was derived, aiding audit and litigation readiness.

Business impact: speed, cost, accuracy, and fewer missed red flags

Doc Chat compresses the proof-of-loss review window from days to minutes. It processes approximately 250,000 pages per minute, delivering structured extraction and cross-check results fast enough to influence same-day investigative decisions. In practice, carriers report that claim summaries that once took 5 to 10 hours can be produced in about a minute, and complex packages that previously required weeks of manual review can be triaged with actionable insights within an hour.

The cost and accuracy benefits compound:

Lower loss adjustment expense: far fewer manual touchpoints on routine completeness and reconciliation checks.
Reduced leakage: consistent detection of coverage conflicts, sub-limit overreach, and duplicate or recycled documentation.
Higher investigator productivity: SIU staff shift from reading to deciding, spending time on interviews, field work, and strategy.
Scalable surge response: catastrophe events or marine incidents often generate a wave of proofs-of-loss; Doc Chat scales instantly without overtime hiring.
Audit-ready defensibility: page-level citations and reversible transformations simplify reinsurer, regulator, and internal audit reviews.

In short, faster triage, fewer misses, and more consistent decisions translate to materially improved financial performance and better service to honest policyholders.

Why Nomad Data is the right partner for SIU and complex proof-of-loss workflows

Insurance documentation is not a generic AI problem. It requires domain-specific inference and the ability to catch nuance that only seasoned adjusters and SIU investigators typically see. Nomad Data brings the technology and the process that captures your unwritten rules and turns them into repeatable automation.

What sets Nomad Data apart for SIU teams handling Property and Homeowners and Specialty Lines and Marine:

Volume and complexity: Doc Chat ingests complete claim files, not cherry-picked pages, and reads every document with uniform attention.
The Nomad process: we train the system on your playbooks, forms, and decision standards so outputs mirror your team’s best practices.
Real-time Q and A: investigators can interrogate entire files in plain language, receiving instant answers with citations.
Thorough and complete: Doc Chat surfaces every reference to coverage, liability, damages, and inconsistencies to minimize blind spots and leakage.
White-glove delivery: implementation typically completes in one to two weeks, including preset templates for proof-of-loss variance reports and SIU referrals tailored to your lines of business.

For a deeper look at how Doc Chat transforms complex claim review, see our case study with Great American Insurance Group, where adjusters moved from days of scrolling to instant answers with link-back citations. Read the story here: Reimagining Insurance Claims Management.

From manual to automated: what changes for the SIU investigator

With manual review, investigators spend disproportionate time reading and re-reading. With Doc Chat, the emphasis shifts to judgment and action. The system handles the rote reading, extraction, cross-checking, and pattern analysis. Investigators make calls, conduct interviews, and direct fieldwork informed by a complete and consistent view.

In practice, this looks like:

An exceptions dashboard that highlights proofs-of-loss missing key attestations, discrepancies between claimed amounts and declarations-based limits, invoices with pre-loss dates, or receipts without corroborating payment evidence.
Hotlinks to source pages for each exception, enabling a quick sanity check and immediate documentation of the issue for the claim file.
Pre-formatted communications templates requesting missing documentation or clarifying statements, populated automatically with the items and dates at issue.
Proactive alerts for high-risk signatures such as repeated receipt language across claims, cross-claim item reuse, or vendor legitimacy concerns.

The result is a measurable increase in investigative throughput and a higher proportion of early, decisive actions such as EUO scheduling or targeted document demands.

Security, compliance, and page-level explainability

Any tool that touches proof-of-loss files must be secure and defensible. Nomad Data maintains rigorous controls, including SOC 2 Type 2 compliance, and ensures that sensitive policyholder information remains under enterprise control. Doc Chat’s page-level citations allow auditors, reinsurers, and counsel to verify every finding. This transparency is crucial when proofs-of-loss become part of litigation or arbitration in Marine and Specialty contexts.

For more on why high-accuracy document automation requires inference — not just keyword scraping — see our perspective on the difference between web scraping and document scraping: Beyond Extraction. And for details on how large-file review bottlenecks disappear with modern AI at industrial scale, see The End of Medical File Review Bottlenecks and Reimagining Claims Processing Through AI Transformation.

Examples: how Doc Chat flags irregular proofs-of-loss in the wild

Property and Homeowners example: contents inflation through template reuse

Scenario: A wind event leads to a surge of claims. One insured submits a sworn proof-of-loss with a large contents schedule and dozens of receipts. Doc Chat extracts all line items, detects repeated language patterns across multiple receipts, and cross-compares them to other claims in the event cohort. It flags that several receipts share identical phrasing and formatting with two other claim files, differing only by the item names and amounts. It also notes that three high-ticket electronics lack serial numbers or photos linking them to the residence. The system generates a concise SIU referral, including citations to each suspicious receipt and a side-by-side comparison across files. The investigator initiates targeted document requests and an EUO focused on purchase proof for the flagged items. Settlement is paused pending validation, preventing potential leakage.

Marine example: cargo count mismatch vs bills of lading

Scenario: A reefer container shipment arrives with alleged temperature excursions causing spoilage. The proof-of-loss claims destruction of 8,500 kg of product. Doc Chat aligns the claimed quantity with the bill of lading, packing list, and manifest, discovering that the shipped quantity documented was 7,250 kg. It highlights the delta with citations and notes that the surveyor report described partial salvage of 500 kg. Doc Chat also detects that the repair invoice for refrigeration equipment is dated two days before the vessel reached port and that the vendor address does not match any registered business in the port city. The investigator uses this early signal to request additional logs and challenges the invoice authenticity, reducing the claim exposure substantially.

ALE example: timelines that do not add up

Scenario: A fire loss displaces the insured for three months. The proof-of-loss includes an ALE total supported by hotel folios. Doc Chat compares ALE dates to the fire department report, adjuster site inspection notes, and a temporary occupancy permit, finding that the hotel charges continued two weeks after the permit allowed the insured to reoccupy. With citations ready, the SIU investigator requests an explanation and adjusts the ALE component to match the authorized displacement period.

How Doc Chat supports early fraud investigation workflows

Early identification is the difference between efficient resolution and prolonged, expensive disputes. Doc Chat helps SIU and intake teams trigger the right workflow at the right time:

Immediate completeness checks: as soon as a proof-of-loss is uploaded, missing fields, blanks, or signature issues are flagged with a corrective checklist.
Automated cross-checks: mismatches against the declarations page, policy endorsements, and sub-limits appear in the exceptions dashboard.
Anomaly scoring: repeated language across receipts, excessive cash transactions, out-of-pattern vendor behavior, or cross-claim item overlap automatically elevate a file to SIU review.
Task recommendations: request specific receipts, verify contractor licensing, order a site visit, schedule EUO, or consult a marine surveyor — all suggested based on the pattern detected.
Real-time answers for investigators: ask trend-focused questions across a surge cohort, such as show me all proofs-of-loss this month that assert mold damage above sub-limit or list cargo claims where claimed quantities exceed manifest by more than 10 percent.

The system serves as an intelligent front door, ensuring SIU gets the right cases early with clear reasoning and evidence.

Implementation: white-glove setup in one to two weeks

Nomad Data delivers a managed, white-glove onboarding that typically completes in one to two weeks. Our team learns your forms, proof-of-loss requirements, SIU triggers, and lines of business, then configures Doc Chat to mirror your standards. A typical rollout includes:

Discovery and rule capture: collaborative sessions with SIU investigators, claims managers, and compliance to codify completeness rules, red flags, and escalation paths.
Preset templates: custom proof-of-loss variance report, SIU referral summary, and exceptions dashboard tailored for Property and Homeowners and Specialty Lines and Marine.
Secure data pathways: drag-and-drop to start, followed by API integration with your claim system for automated ingestion and results export.
Validation and trust-building: hands-on testing using known files to confirm accuracy, supported by page-level citations for every extracted field and exception.
Training and change enablement: investigator workshops focused on asking better questions, not pushing buttons, so teams shift seamlessly from reading to deciding.

For an overview of how rapid adoption happens in practice, explore how a major carrier accelerated complex claim reviews and transformed daily workflows: GAIG accelerates complex claims with AI.

Integrations and scale without disruption

Doc Chat is built to fit your operation. Start with drag-and-drop uploads of proofs-of-loss and supporting documentation. As confidence grows, connect via API to automatically ingest new claim files, deposit outputs back to your claim system, and create tasks or referrals. The platform handles surge volumes, so catastrophe spikes or large marine events do not require emergency hiring or overtime. And because every output includes citations, your compliance, audit, and legal partners can adopt with confidence.

How high-intent workflows benefit from Doc Chat

Proof of loss fraud detection at scale

Doc Chat’s approach to proof of loss fraud detection blends extraction, cross-document reasoning, and patterned anomaly detection. It continuously compares statements to supporting documents, learns from confirmed fraud indicators, and adds those indicators to your organization’s signature set for future screening.

Flag incomplete proof of loss AI for intake teams

Completeness checks now run in real time. Intake specialists receive precise instructions on what to request, improving cycle times and reducing investigator back-and-forth. Missing notarization, absent signatures, non-matching names, omitted policy numbers, or blank fields are caught immediately.

Compare proof of loss to claim docs with one question

Investigators can ask compare proof of loss to claim docs and instantly receive a reconciliation of amounts, dates, and items against declarations, receipts, estimates, survey reports, and bills of lading. Discrepancies are ranked by severity and impact, with suggested next steps.

Tying it all together: from data entry to investigative intelligence

A surprising amount of SIU work begins as data entry: collecting fields, normalizing item lists, and checking them against other sources. Modern AI turns these steps into automated, reliable pipelines, freeing investigators to concentrate on higher-order questions and strategy. For additional context on why automating data entry at industrial scale delivers outsized ROI, see AI’s Untapped Goldmine: Automating Data Entry.

Frequently asked concerns: hallucinations, security, and defensibility

In document-grounded workflows like proof-of-loss review, modern AI performs exceptionally well. Doc Chat answers only from the provided materials and always links to the source page, which curbs hallucinations by design. On security, Nomad Data is SOC 2 Type 2 compliant, and enterprise deployments are architected to keep sensitive data under your control. When paired with the platform’s citation-first outputs, findings become easy to defend to regulators, reinsurers, and in litigation.

KPIs SIU leaders can track after deployment

SIU directors and managers in Property and Homeowners and Specialty Lines and Marine typically monitor:

Time to initial SIU decision post proof-of-loss submission.
Rate of incomplete proofs-of-loss at intake before and after automation.
Percentage of claims flagged with high-confidence anomalies; proportion validated by investigation.
Leakage avoided via corrections to deductibles, sub-limits, or unsupported line items.
Investigator caseload and cycle time improvements during surge events.
Audit exceptions and re-open rates due to documentation defects.

Doc Chat’s dashboards deliver these metrics out of the box, aligning operational visibility with financial outcomes.

Getting started

If your SIU team is evaluating tools that can flag incomplete proof-of-loss packages, compare proof-of-loss to claim documents automatically, and scale proof of loss fraud detection across Property and Homeowners and Specialty Lines and Marine, Doc Chat is a fast, defensible choice. Pilot the workflow by dragging a set of recent proofs-of-loss and supporting documentation into the platform; within minutes you will have completeness results, cross-document discrepancies, and an initial SIU referral summary with citations.

Ready to see it in action and tailor it to your playbooks? Visit Nomad Data Doc Chat for Insurance to connect with our team.