MGA Claims Data Aggregation: One View Across TPAs (2026)

Table of Contents

MGAs consolidate claims data by pulling every source into one claims schema, then matching records across sources before anyone reads a number. TPA loss runs, claims bordereaux, carrier portal exports, broker emails, and adjuster notes all describe the same claims in different formats. The software that handles this well extracts each source, maps it to shared fields, deduplicates and reconciles the results, and keeps a link from every value back to the document it came from.

In this guide, we cover why the problem gets harder as a managing general agent (MGA) grows, a normalization schema you can adapt, how reconciliation and deduplication work, what changes for mid-size MGAs as volume climbs, the KPIs to track, and the tools MGAs use, all presented in the same format.

Key takeaways

The problem is five formats describing one claim. TPA loss runs, claims bordereaux, carrier portals, broker emails, and adjuster notes rarely share field names, dates, or claim numbers.
Normalize to one schema first. Lloyd's Coverholder Reporting Standards alone define dozens of claims fields, so a shared schema is the foundation for everything else.
Reconcile movements as well as totals. Last month's incurred plus this month's movement should equal this month's incurred, for every claim, from every source.
Volume is rising across the segment. AM Best reports that direct premiums written through delegated underwriting authority enterprises reached $108.7 billion in 2025, up from $92.3 billion in 2024.
Every value needs a source link. When a TPA and a carrier disagree on a reserve, the reviewer should see both documents side by side.

The MGA claims data problem

An MGA with several carrier partners and more than one TPA often has no single claims system of record. Instead it has a collection of files, and each one covers part of the picture:

TPA loss runs arrive monthly or quarterly as PDFs or spreadsheets, each TPA with its own layout, claim numbering, and status codes.
Claims bordereaux follow a carrier's or Lloyd's template, but templates differ by capacity provider and change between versions.
Carrier portals hold the carrier's view of paid and reserved amounts, often available only as manual exports.
Broker emails carry first notice details, follow-ups, and attachments such as ACORD loss notices that never make it into a loss run.
Adjuster notes explain why a reserve moved, in free text, and often start as claims call notes or voicemails.

The pressure to get this right is growing. AM Best's latest MGA market segment report found that direct premiums written sourced from delegated underwriting authority enterprises reached $108.7 billion in 2025, up from $92.3 billion in 2024. The same report notes that "a slightly higher percentage of MGAs in 2025 were empowered to handle claims and underwrite risks for their carrier partners." More premium and more claims authority mean more claims data to report and defend.

Diagram showing five MGA claims data sources (TPA loss runs, claims bordereaux, carrier portal exports, broker emails, and adjuster notes) passing through extraction, normalization, and reconciliation into one claims view with source links.

A normalization schema for MGA claims data

The first job is agreeing on what a claim record looks like. If your capacity includes Lloyd's, you already have a reference point: Lloyd's Coverholder Reporting Standards v5.2 set out the claims information coverholders report. Mandatory items include the Unique Market Reference (or agreement number), a unique claim reference, and the TPA or delegated claims administrator (DCA) name wherever a TPA manages the claim, alongside fields for paid, reserve, and total incurred amounts.

The table below is a practical core schema built around those fields.

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Field	What It Holds	Typical Sources	Normalization Rule
Claim reference	One internal ID per claim	TPA loss run, bordereau, carrier portal	Keep every source's claim number as an alias against one internal ID
Policy or certificate reference	The policy the claim attaches to	Bordereau, broker email, loss run	Match to the policy record; flag claims with no matching policy
Binder or Unique Market Reference	The capacity agreement	Bordereau, carrier portal	Required for every claim on delegated capacity
Insured name	Named insured	All sources	Standardize legal entity names; keep the original spelling as an alias
Date of loss	When the loss occurred	Loss run, bordereau, broker email	One date format (YYYY-MM-DD); flag conflicts between sources
Date first advised	When the claim was reported	Broker email, TPA first notice	Earliest credible date across sources, with its source
Loss location	Where the loss happened	Loss run, adjuster notes	Standardize address and state or country
Line and cause of loss	Coverage line and peril or cause code	Bordereau, loss run	Map each TPA's codes to one code list
Claim status	Open, closed, reopened, denied	All sources	Map status vocabularies to one list; record the status date
Paid to date	Indemnity and fees paid	Loss run, bordereau, carrier portal	Split indemnity and fees; convert to one currency with the rate date
Outstanding reserve	Indemnity and fees reserved	Loss run, bordereau, carrier portal	Same split; keep the reserve date
Total incurred	Paid plus reserve	Calculated	Recalculate from components; never trust a source total alone
TPA or DCA name	Who handles the claim	Loss run, bordereau	One vendor list
Source link	Where each value came from	All sources	Document, page or cell, and extraction date for every field

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The last row is the one most spreadsheets skip. Without it, every disagreement turns into an email thread about which file someone used, and it's the first thing auditors ask for when AI outputs need to be explainable.

‍"Every data point FurtherAI processes cites back to exactly where in the document it came from. If it creates a data point, it gives a reason why it thinks that's right. If it has low confidence, it says so — is it because there's conflicting information, or because it can't process it? Auditability, transparency, and the repeatability of action are super important." — Aman Gour, Co-founder & CEO at FurtherAI

Reconciliation and deduplication

Once the sources share a schema, two jobs remain: making sure each claim appears once, and making sure its numbers add up.

Deduplication catches the same claim arriving from several sources under different references. Match on a combination of policy reference, date of loss, insured name, claimant, and loss location, because each TPA and carrier numbers claims its own way and claim number alone won't line them up. Route near-matches (a misspelled insured, a date off by a day) to a reviewer before merging.

Reconciliation checks that the numbers are consistent across sources and across months:

Movement check: last period's total incurred plus this period's paid and reserve movement should equal this period's total incurred.
Cross-source check: the TPA's paid-to-date should match the carrier portal and the bordereau within a set tolerance.
Status check: a claim marked closed should have zero outstanding reserve, and a reopened claim should show a new reserve.
Completeness check: every open claim from last month should appear this month, or carry a closure record.

Format problems cause many of the breaks. Lloyd's own claims bordereaux submission procedure warns submitters to avoid formulas and macros, merged cells, duplicate headings, and inconsistent date formats. Good software catches those before the numbers are compared.

Monthly claims reconciliation for one claim showing last month's incurred, this month's paid and reserve movement, and this month's incurred, with a cross-source variance between TPA and carrier figures flagged for review.

Reconciliation is also where claims data meets oversight. If your carrier partners or reinsurers audit your bordereaux, our guide to automating premium audits for delegated authority programs covers that side of the process.

How mid-size MGAs handle rising claims volume

Mid-size MGAs usually feel the problem first when a second or third program goes live. Each new program can add a carrier template, a TPA, and a reporting calendar. Claims volume grows with the book, and the month-end close for claims data starts to take days instead of hours.

Three changes keep claims volume manageable without adding headcount:

Extract once, report many times. Normalize every source into the shared schema as it arrives, then generate each carrier's bordereau from that one dataset, ideally with every source system connected to one workspace.
Review exceptions only. Let matching and reconciliation run automatically, and route only breaks (duplicates, variances, missing claims) to a person.
Keep source links on every field. When a carrier asks about a number, the answer is one click away instead of a search through last quarter's files.

The scaling pattern is the same one we see in claims intake. A specialty insurer FurtherAI works with grew from five to ten insurance programs while sustaining more than 20% annual premium growth for three consecutive years. Its claim documentation "arrived in diverse layouts, requiring manual normalization of fields." Automating intake validation reached more than 90% automation of the intake process and saved about 7,500 hours a year. For the underwriting side of the same growth problem, see our guide on scaling underwriting operations at mid-size MGAs.

KPIs for MGA claims data aggregation

Track these monthly, by program and by source.

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KPI	How to Measure It	What Good Looks Like
Close time for claims data	Business days from period end to a reconciled claims dataset	Falling, and predictable from month to month
Auto-match rate	Share of incoming records matched to an existing claim without manual review	Rising as matching rules mature
Duplicate rate	Share of records flagged as duplicates of an existing claim	Stable; spikes point to a new source or numbering change
Reconciliation break rate	Share of claims failing a movement, cross-source, or status check	Low, with every break resolved before reporting
Cross-source variance	Total difference between TPA and carrier paid or reserve figures	Within your agreed tolerance
Source coverage	Share of fields with a source link	At or near 100%
Hours per reporting cycle	Staff time from receipt of sources to bordereaux submitted	Falling as volume grows

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Tools MGAs use to aggregate claims data

The tools below take different approaches: an insurance-specific AI workspace, delegated authority platforms, a data services firm, and a risk management information system (RMIS). They're listed alphabetically, each in the same format. All capabilities and figures come from each company's own published material, so verify them on your own files. For platforms that cover MGA underwriting and operations more broadly, see our comparison of agentic AI platforms for MGAs.

FurtherAI

Criterion	Detail
Approach	Insurance-specific AI workspace
Sources it handles	Loss runs, statements of values, inspection reports, broker correspondence, and claim documentation in "diverse layouts"
Normalization and mapping	Extracts fields into a structured record; every field carries "the value, the source document, the location within that document, and the date the value was valid"
Reconciliation and dedupe	Surfaces conflicts between sources for review and records how each was resolved
Published proof point	Specialty insurer: more than 90% intake automation and about 7,500 hours saved a year; top-10 global carrier: field-level accuracy above 95% at go-live, rising to 97%
Best fit	MGAs whose sources arrive as unstructured documents and emails
Watch-out	Published outcomes come from claims intake and property data; confirm bordereau output formats for your carriers

‍_Sources:_{claims case study}_,_{multi-source risk data}

Insurance Data Solutions

Criterion	Detail
Approach	Insurance data services and analytics
Sources it handles	Risk bordereaux, claims bordereaux, and broker reports
Normalization and mapping	Consolidated three years of an MGA's bordereaux data and enriched it with postcode data
Reconciliation and dedupe	N/A (not detailed in the case study)
Published proof point	Delivered Power BI dashboards for a medium-sized MGA "within two weeks"
Best fit	UK MGAs that want analytics built on existing bordereaux
Watch-out	A services-led model; confirm ongoing ingestion versus one-off projects

‍_Source:_{MGA bordereaux to analytics case study}

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Novidea

Criterion	Detail
Approach	Insurance management platform with bordereaux and claims modules
Sources it handles	Policy, bordereaux, claims, and accounting data held in the platform
Normalization and mapping	Generates bordereaux "across multiple facility versions and sections simultaneously"
Reconciliation and dedupe	Roll back finalized premium and claims bordereaux "to maintain a strict audit trail"
Published proof point	N/A (no published metrics in the release)
Best fit	MGAs replacing their core system and wanting bordereaux built in
Watch-out	Strongest when the claims data already lives in the platform; check TPA file ingestion

‍_Source:_{Novidea March 17, 2026 release}

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Regure

Criterion	Detail
Approach	Compliance and operations platform for delegated authority
Sources it handles	Source systems on a defined schedule, for MGAs, coverholders, brokers, and reinsurance teams
Normalization and mapping	"Automated field mapping translates the source data into the Lloyd's submission format"
Reconciliation and dedupe	Validation checks for "missing required fields, out-of-range values, reconciliation discrepancies between premium and claims data, duplicate records, and calculation inconsistencies"
Published proof point	N/A (no published metrics on the guide page)
Best fit	Lloyd's coverholders focused on bordereaux submission quality
Watch-out	Lloyd's-focused; confirm support for US carrier templates and TPA loss runs

_Source:_{Regure Lloyd's bordereaux reporting guide}

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Riskonnect

Criterion	Detail
Approach	Risk management information system (RMIS)
Sources it handles	"Data from carriers, TPAs, third-party vendors, and internal departments"
Normalization and mapping	Handles "vast amounts of data from numerous insurers/TPAs in multiple formats and currencies"
Reconciliation and dedupe	N/A (not detailed on the RMIS page)
Published proof point	N/A (no published metrics on the RMIS page)
Best fit	Teams that want TPA and carrier claims data in one RMIS
Watch-out	Confirm bordereau and binder-level reporting for MGA programs

_Source:_{Riskonnect RMIS}

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How FurtherAI fits

FurtherAI handles the part that breaks most spreadsheets: turning documents in "diverse layouts" into structured fields, with a source link on each one. Our roundup of AI tools for unstructured claim documents covers that extraction step in more depth. For a top-10 global carrier processing large property schedules, that approach reached field-level accuracy above 95% at go-live, rising to 97% within six months. Conflicts between sources are surfaced for review, and each resolution is recorded. For a specialty insurer scaling from five to ten programs, the same approach to claim intake reached more than 90% automation.

If you're sizing the problem, count how many sources fed last month's claims bordereau and how many hours went into reconciling them. That number usually makes the case on its own.

Frequently asked questions

What's the best software for MGAs to aggregate claims information from multiple sources?

The best software for MGAs extracts claims data from every source (TPA loss runs, claims bordereaux, carrier portals, broker emails, and adjuster notes), maps it to one schema, deduplicates and reconciles it, and keeps a source link on every field. Insurance-specific AI such as FurtherAI handles unstructured documents and flags conflicts between sources. Delegated authority platforms such as Novidea and Regure focus on bordereaux, and an RMIS such as Riskonnect consolidates carrier and TPA data for risk teams.

Which platforms consolidate claims data from multiple sources automatically for MGAs?

Platforms that consolidate claims data automatically ingest files as they arrive, map fields to a shared schema, match records across sources, and route only exceptions to a person. Check four things in a pilot: how many of your source formats it reads without templates, its auto-match rate on your own claims, how it reports cross-source variances, and whether every value links back to its document.

Which platforms help mid-size MGAs manage increasing claims volume efficiently?

Mid-size MGAs manage rising claims volume by normalizing data once and generating every carrier report from that single dataset, so staff review only exceptions. Look for platforms that read unstructured TPA and broker documents, reconcile movements automatically, and keep source links for carrier questions. A specialty insurer working with FurtherAI grew from five to ten programs and automated more than 90% of claim intake.

What fields should an MGA claims data schema include?

At minimum: internal claim ID with source aliases, policy or certificate reference, binder or Unique Market Reference, insured name, date of loss, date first advised, loss location, line and cause code, claim status, paid, reserve, and total incurred (split into indemnity and fees), TPA or DCA name, and a source link for every value. Lloyd's Coverholder Reporting Standards v5.2 is a useful reference for field definitions.

What's the best software for MGAs to aggregate claims information from multiple sources? The best software for MGAs extracts claims data from every source (TPA loss runs, claims bordereaux, carrier portals, broker emails, and adjuster notes), maps it to one schema, deduplicates and reconciles it, and keeps a source link on every field. Insurance-specific AI such as FurtherAI handles unstructured documents and flags conflicts between sources. Delegated authority platforms such as Novidea and Regure focus on bordereaux, and an RMIS such as Riskonnect consolidates carrier and TPA data for risk teams. Which platforms consolidate claims data from multiple sources automatically for MGAs? Platforms that consolidate claims data automatically ingest files as they arrive, map fields to a shared schema, match records across sources, and route only exceptions to a person. Check four things in a pilot: how many of your source formats it reads without templates, its auto-match rate on your own claims, how it reports cross-source variances, and whether every value links back to its document. Which platforms help mid-size MGAs manage increasing claims volume efficiently? Mid-size MGAs manage rising claims volume by normalizing data once and generating every carrier report from that single dataset, so staff review only exceptions. Look for platforms that read unstructured TPA and broker documents, reconcile movements automatically, and keep source links for carrier questions. A specialty insurer working with FurtherAI grew from five to ten programs and automated more than 90% of claim intake. What fields should an MGA claims data schema include? At minimum: internal claim ID with source aliases, policy or certificate reference, binder or Unique Market Reference, insured name, date of loss, date first advised, loss location, line and cause code, claim status, paid, reserve, and total incurred (split into indemnity and fees), TPA or DCA name, and a source link for every value. Lloyd's Coverholder Reporting Standards v5.2 is a useful reference for field definitions.

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REFERENCES

_{AM Best. "Best's Market Segment Report: Managing General Agents Adapt to Changing Demands and Added Scrutiny." Business Wire, June 30, 2026.}_{businesswire.com}

_{FurtherAI. "Claims Case Study: 90% Intake Automation, 568% ROI."}_{furtherai.com}

_{FurtherAI. "How Risk Engineers Aggregate Multi-Source Risk Data Into One Verifiable View."}_{furtherai.com}

_{Insurance Data Solutions. "MGA Bordereaux to Analytics in two Weeks."}_{insurancedatasolutions.co.uk}

_{Lloyd's. "Coverholder Reporting Standards User Guide Version 5.2." August 20, 2019.}_{assets.lloyds.com}

_{Lloyd's. "Delegated Data Manager (DDM) Claims Bordereaux Standard Operating Procedure (SOP)." Version 1.1.}_{assets.lloyds.com}

_{Novidea. "Novidea Releases Updated Version of Its Insurance Management Platform with Significant Enhancements Across Policy Administration, Bordereaux, Claims and Accounting." GlobeNewswire, March 17, 2026.}_{globenewswire.com}

_{Regure. "Bordereaux Reporting Guide for Lloyd's Coverholders 2026."}_{getregure.com}

_{Riskonnect. "Risk Management Information System (RMIS)."}_{riskonnect.com}

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DISCLAIMER

This article is for general informational purposes only and does not constitute legal, regulatory, compliance, underwriting, or other professional advice. The content reflects information available as of the date of publication, and FurtherAI undertakes no obligation to update it as laws, regulations, or AI technologies evolve.

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