The bordereaux team that spent three days reconciling data from seven DA partners can now complete the same task in four hours. Not by working faster. By not doing the manual work at all. This post covers how automated bordereaux ingestion, validation, and exception routing works — and what the 78% reduction in data error rates means for the DA relationship between MGA and capacity provider.
Seventeen Submissions. Four Days. Data Already Three Weeks Old.
It is the last working day of October. The delegated data team at a Lloyd's managing agent is waiting for premium bordereaux from 17 MGAs. By 11:00, nine have arrived. Of those nine, four are in the correct format. Two use field labels that differ from the required schema. One is missing the unique risk reference column. One contains inception dates formatted as text strings rather than dates. One has a premium currency column that alternates between 'GBP' and 'NOK' with no consistent pattern.
The team has four days to reconcile, correct, validate, and aggregate all 17 submissions before the syndicate's underwriting systems close for the month. They will spend those four days on the telephone to MGA back-office contacts, in spreadsheets, and in email chains requesting corrected files. The data they are working with is already three weeks old. The portfolio it describes has continued to write business while they have been building the file.
This is the bordereaux problem. Not the data. The process of producing, submitting, validating, and consuming it. Every manual step introduces delay, error, and cost. Delegated authority automation removes those steps. The data flows. The team manages exceptions. The capacity provider sees what is being written on its paper as it happens.
Key Figures
| Figure | What it means |
|---|---|
| 40–60%[3] | Reduction in bordereaux production time at MGAs using AI-automated data extraction and validation. A process that takes three to five working days per month reduces to same-day automated production. |
| 8–15% → <2%[3] | Field-level error rate on manually produced bordereaux submitted to Lloyd's managing agents, requiring correction before data can be consumed. AI-validated bordereaux achieve error rates under 2%. |
| 3–5 FTE days[3] | Monthly reconciliation effort per capacity provider relationship under manual bordereaux processes. With AI bordereaux processing, the same reconciliation reduces to exception-only handling, typically under four hours per month. |
| 2–4 weeks[3] | Average age of portfolio data at the point a capacity provider receives a manual monthly bordereaux. Real-time delegated authority automation delivers portfolio data within minutes of each bind. |
| Blueprint Two[4] | Lloyd's digital transformation programme driving standardisation of delegated authority data across the market, creating the schema framework that AI bordereaux automation aligns with automatically. |
Why Delegated Authority Automation Changes the Economics of the MGA Model
Delegated authority automation is the application of AI data extraction, schema mapping, and validation to the bordereaux workflow: the periodic data submissions that MGAs provide to capacity providers summarising the policies bound, claims made, and premiums collected under their binding authority. Under a manual process, this consumes three to five working days per month at the MGA, an equivalent effort at the capacity provider's delegated data team, and produces data that is two to four weeks old by the time it is received and processed.
The cost of that delay is not just administrative. A capacity provider that does not know its current accumulation position by postcode, class, and sector cannot manage its book in real time. It discovers binding authority breaches at month-end rather than when they occur. It receives reinsurance premium calculations based on data that was already stale when the cession was made. And it evaluates MGA performance against a data set that reflects the portfolio as it was three weeks ago, not as it is today.
How AI Automates Bordereaux Processing: A Step-by-Step View
Real-time data extraction
In a delegated authority automation deployment, the bordereaux data pipeline begins at the moment a policy is bound, not at the end of the month. As the underwriter issues the bind instruction in the MGA's underwriting workbench, the AI data layer extracts the relevant policy fields: unique risk reference, insured name, class of business, inception and expiry dates, premium, coverage structure, limits, deductibles, geographic scope, and any supplementary fields required by the specific binding authority terms. This extraction is instantaneous and does not add any step to the underwriter's workflow. The bordereaux data entry happens automatically as a consequence of the bind instruction, not as a separate administrative process downstream of it.
Schema mapping and authority validation
Each capacity provider relationship has its own data schema: the field definitions, naming conventions, date formats, currency codes, and reference structures the capacity provider's systems require. In a manual process, the MGA back-office team maps the underwriting system's output to each capacity provider's schema by hand — which is why format errors account for a disproportionate share of bordereaux correction queries. The AI layer maintains a schema mapping configuration for each binding authority, applying the correct field labels, date formats, and reference codes automatically. Simultaneously, the system validates the bind against the binding authority terms. Bindings clearly within authority are processed and added to the real-time portfolio feed. Bindings approaching authority boundaries generate an automated alert to the lead underwriter. Bindings that exceed a defined tolerance require manual review before the policy can be issued.
Real-time portfolio feed and accumulation monitoring
Each validated bind updates the real-time portfolio data feed accessible to both the MGA and the capacity provider: current premium volume by class, geography, and inception period; binding authority utilisation as a percentage of each authority's limits; running loss ratio as claims data is added; and accumulation by sector and postcode where concentration risk is relevant. Accumulation alerts are configured against the specific binding authority parameters. When premium in a class approaches 80% of the authority limit, the system flags the position automatically. At 90%, it escalates to the MGA's senior underwriter and notifies the capacity provider's delegated authority team simultaneously — before the limit is breached, not after.
Exception handling and month-end formal submission
Bindings with low schema mapping confidence, unusual coverage structures, or parameter boundary flags route to the MGA's operations team for review. These exceptions are typically 5 to 10% of total bind volume in a well-configured deployment. The operations team works exclusively on exceptions rather than processing every transaction from scratch, reducing the monthly reconciliation effort from three to five FTE days to under four hours. Where the capacity provider systems require a formal month-end bordereaux submission, the AI layer generates it automatically from the accumulated bind data — pre-validated against the capacity provider's schema, with exceptions already resolved. The correction cycle that previously consumed the first week of the following month disappears.[3]
The Before and After Comparison
The table below summarises the operational differences across the dimensions that matter most to MGA principals, delegated data teams, and capacity provider underwriting management.
| Dimension | Manual bordereaux process | AI-automated bordereaux process |
|---|---|---|
| Data production frequency | Monthly batch submission | Continuous real-time feed per bind |
| Time to produce monthly file | 3–5 working days | Same day, automated |
| Data currency at receipt | 2–4 weeks old | Minutes old |
| Error rate at submission | 8–15% of fields requiring correction | Under 2% with automated validation[3] |
| Capacity provider visibility | Retrospective portfolio view | Live binding authority utilisation dashboard |
| Accumulation monitoring | Identified at month-end, post-breach | Real-time alerts before limit is reached |
| Reconciliation effort | 3–5 FTE days per month per capacity provider | Exception-only: under 4 hours per month |
| Lloyd's Blueprint Two alignment | Manual compliance with digital data standards | Automated alignment with LM TOM data schema[4] |
The Lloyd's Market Context: Blueprint Two and LM TOM
Lloyd's Blueprint Two digital transformation programme has been driving standardisation of delegated authority data across the market since 2020. The London Market Target Operating Model (LM TOM) defines data standards for coverholder and MGA submissions that AI bordereaux automation systems are designed to align with. MGAs that have adopted Blueprint Two data standards — including the ACORD data schema and the Lloyd's-specified coverholder reporting formats — are significantly better positioned to deploy AI bordereaux automation than those still operating on bespoke legacy formats.[4]
For MGAs not yet aligned with Blueprint Two standards, a Lloyd's delegated authority AI deployment typically includes a data standardisation workstream alongside the automation implementation. This standardisation is a prerequisite, not a post-deployment task. The AI schema mapping layer can only automate to a standard it has been configured against — a bespoke legacy format is by definition not a standard.
Lloyd's Blueprint Two · Delegated Authority Digital Transformation Progress Report [4]Frequently Asked Questions
We have multiple capacity providers each requiring different bordereaux formats. Can the AI handle that?+
Yes. The AI schema mapping layer maintains a separate configuration for each binding authority, mapping the same underlying policy data to each capacity provider's required format automatically. Adding a new capacity provider requires configuring a new mapping schema, which typically takes two to four days depending on the complexity of the required format. MGAs with 10 to 15 capacity provider relationships, each with different schema requirements, typically achieve the greatest productivity gain from automation because the manual mapping cost is highest in those operations.[3]
What happens if the AI maps a field incorrectly and the error reaches the capacity provider?+
AI schema mapping errors are caught at the validation step before the data is transmitted. The validation layer checks the mapped output against the capacity provider's schema specification and flags any field that does not conform before it leaves the MGA's system. The small volume of errors that pass validation — typically under 2% of fields in a mature deployment — are identified when the capacity provider's system processes the data and generate a correction request through the standard query process. Correction queries on AI-generated bordereaux are significantly lower in volume than on manually produced equivalents.[3]
How does real-time bordereaux data change the capacity provider relationship in practice?+
Capacity providers with real-time visibility into MGA portfolio data manage their delegated authority positions fundamentally differently from those relying on monthly batch submissions. They can identify accumulation trends as they develop, not after month-end. They can monitor loss ratio development in real time and raise queries while the relevant claims are still in early development. And they can evaluate MGA performance against current data rather than data that was three weeks old when it arrived. In authority renewals, MGAs providing real-time data consistently report better terms and higher authority limits than those on batch reporting cycles.[3][4]
Does our underwriting platform need to be replaced to implement bordereaux automation?+
No. AI bordereaux automation is typically implemented as an integration layer that connects to the existing underwriting workbench via API, extracting bind data at the point of transaction without requiring any change to the underwriter's workflow. The integration complexity depends on the API capabilities of the existing platform: most modern underwriting workbenches support the data connections needed. Older proprietary systems may require a data extraction adapter rather than a direct API connection. Implementation time ranges from six to 14 weeks depending on the number of capacity provider schemas and the quality of the existing data.[3]
How does bordereaux automation apply to Nordic market programme business operations?+
Programme managers and delegated authority operators in Norwegian and Nordic markets face the same bordereaux data challenges as Lloyd's market MGAs, though the specific data standards differ. Nordic capacity providers typically require bordereaux data aligned with their own policy administration system schemas rather than the Lloyd's Blueprint Two standards. The AI schema mapping approach is identical; the configuration targets are different. Norwegian-market data handling requirements, including Finanstilsynet's data governance expectations, apply to the automated pipeline in the same way as to manual processes. Specific regulatory requirements should be verified with qualified Norwegian legal counsel.[5]
What is the typical implementation timeline for bordereaux automation at an MGA?+
For an MGA with two to five capacity provider relationships, clean underwriting data, and an API-connected underwriting workbench, a full bordereaux automation deployment including real-time feeds, accumulation monitoring, and automated month-end file production can be operational in 10 to 16 weeks. MGAs with more capacity provider relationships, legacy underwriting platforms, or data quality remediation needs will take longer — typically 20 to 28 weeks. The data quality assessment and schema configuration work, rather than the AI components, typically accounts for 50 to 60% of implementation time.[3]
References
All statistics sourced from documented deployments and third-party research organisations. Links verified 2026. Click any citation to jump to its source.
The bordereaux team that spent three days reconciling data from seven DA partners can now complete the same task in four hours. Not by working faster. By not doing the manual work at all. This post covers how automated bordereaux ingestion, validation, and exception routing works — and what the 78% reduction in data error rates means for the DA relationship between MGA and capacity provider.
Seventeen Submissions. Four Days. Data Already Three Weeks Old.
It is the last working day of October. The delegated data team at a Lloyd's managing agent is waiting for premium bordereaux from 17 MGAs. By 11:00, nine have arrived. Of those nine, four are in the correct format. Two use field labels that differ from the required schema. One is missing the unique risk reference column. One contains inception dates formatted as text strings rather than dates. One has a premium currency column that alternates between 'GBP' and 'NOK' with no consistent pattern.
The team has four days to reconcile, correct, validate, and aggregate all 17 submissions before the syndicate's underwriting systems close for the month. They will spend those four days on the telephone to MGA back-office contacts, in spreadsheets, and in email chains requesting corrected files. The data they are working with is already three weeks old. The portfolio it describes has continued to write business while they have been building the file.
This is the bordereaux problem. Not the data. The process of producing, submitting, validating, and consuming it. Every manual step introduces delay, error, and cost. Delegated authority automation removes those steps. The data flows. The team manages exceptions. The capacity provider sees what is being written on its paper as it happens.
Key Figures
| Figure | What it means |
|---|---|
| 40–60%[3] | Reduction in bordereaux production time at MGAs using AI-automated data extraction and validation. A process that takes three to five working days per month reduces to same-day automated production. |
| 8–15% → <2%[3] | Field-level error rate on manually produced bordereaux submitted to Lloyd's managing agents, requiring correction before data can be consumed. AI-validated bordereaux achieve error rates under 2%. |
| 3–5 FTE days[3] | Monthly reconciliation effort per capacity provider relationship under manual bordereaux processes. With AI bordereaux processing, the same reconciliation reduces to exception-only handling, typically under four hours per month. |
| 2–4 weeks[3] | Average age of portfolio data at the point a capacity provider receives a manual monthly bordereaux. Real-time delegated authority automation delivers portfolio data within minutes of each bind. |
| Blueprint Two[4] | Lloyd's digital transformation programme driving standardisation of delegated authority data across the market, creating the schema framework that AI bordereaux automation aligns with automatically. |
Why Delegated Authority Automation Changes the Economics of the MGA Model
Delegated authority automation is the application of AI data extraction, schema mapping, and validation to the bordereaux workflow: the periodic data submissions that MGAs provide to capacity providers summarising the policies bound, claims made, and premiums collected under their binding authority. Under a manual process, this consumes three to five working days per month at the MGA, an equivalent effort at the capacity provider's delegated data team, and produces data that is two to four weeks old by the time it is received and processed.
The cost of that delay is not just administrative. A capacity provider that does not know its current accumulation position by postcode, class, and sector cannot manage its book in real time. It discovers binding authority breaches at month-end rather than when they occur. It receives reinsurance premium calculations based on data that was already stale when the cession was made. And it evaluates MGA performance against a data set that reflects the portfolio as it was three weeks ago, not as it is today.
How AI Automates Bordereaux Processing: A Step-by-Step View
Real-time data extraction
In a delegated authority automation deployment, the bordereaux data pipeline begins at the moment a policy is bound, not at the end of the month. As the underwriter issues the bind instruction in the MGA's underwriting workbench, the AI data layer extracts the relevant policy fields: unique risk reference, insured name, class of business, inception and expiry dates, premium, coverage structure, limits, deductibles, geographic scope, and any supplementary fields required by the specific binding authority terms. This extraction is instantaneous and does not add any step to the underwriter's workflow. The bordereaux data entry happens automatically as a consequence of the bind instruction, not as a separate administrative process downstream of it.
Schema mapping and authority validation
Each capacity provider relationship has its own data schema: the field definitions, naming conventions, date formats, currency codes, and reference structures the capacity provider's systems require. In a manual process, the MGA back-office team maps the underwriting system's output to each capacity provider's schema by hand — which is why format errors account for a disproportionate share of bordereaux correction queries. The AI layer maintains a schema mapping configuration for each binding authority, applying the correct field labels, date formats, and reference codes automatically. Simultaneously, the system validates the bind against the binding authority terms. Bindings clearly within authority are processed and added to the real-time portfolio feed. Bindings approaching authority boundaries generate an automated alert to the lead underwriter. Bindings that exceed a defined tolerance require manual review before the policy can be issued.
Real-time portfolio feed and accumulation monitoring
Each validated bind updates the real-time portfolio data feed accessible to both the MGA and the capacity provider: current premium volume by class, geography, and inception period; binding authority utilisation as a percentage of each authority's limits; running loss ratio as claims data is added; and accumulation by sector and postcode where concentration risk is relevant. Accumulation alerts are configured against the specific binding authority parameters. When premium in a class approaches 80% of the authority limit, the system flags the position automatically. At 90%, it escalates to the MGA's senior underwriter and notifies the capacity provider's delegated authority team simultaneously — before the limit is breached, not after.
Exception handling and month-end formal submission
Bindings with low schema mapping confidence, unusual coverage structures, or parameter boundary flags route to the MGA's operations team for review. These exceptions are typically 5 to 10% of total bind volume in a well-configured deployment. The operations team works exclusively on exceptions rather than processing every transaction from scratch, reducing the monthly reconciliation effort from three to five FTE days to under four hours. Where the capacity provider systems require a formal month-end bordereaux submission, the AI layer generates it automatically from the accumulated bind data — pre-validated against the capacity provider's schema, with exceptions already resolved. The correction cycle that previously consumed the first week of the following month disappears.[3]
The Before and After Comparison
The table below summarises the operational differences across the dimensions that matter most to MGA principals, delegated data teams, and capacity provider underwriting management.
| Dimension | Manual bordereaux process | AI-automated bordereaux process |
|---|---|---|
| Data production frequency | Monthly batch submission | Continuous real-time feed per bind |
| Time to produce monthly file | 3–5 working days | Same day, automated |
| Data currency at receipt | 2–4 weeks old | Minutes old |
| Error rate at submission | 8–15% of fields requiring correction | Under 2% with automated validation[3] |
| Capacity provider visibility | Retrospective portfolio view | Live binding authority utilisation dashboard |
| Accumulation monitoring | Identified at month-end, post-breach | Real-time alerts before limit is reached |
| Reconciliation effort | 3–5 FTE days per month per capacity provider | Exception-only: under 4 hours per month |
| Lloyd's Blueprint Two alignment | Manual compliance with digital data standards | Automated alignment with LM TOM data schema[4] |
The Lloyd's Market Context: Blueprint Two and LM TOM
Lloyd's Blueprint Two digital transformation programme has been driving standardisation of delegated authority data across the market since 2020. The London Market Target Operating Model (LM TOM) defines data standards for coverholder and MGA submissions that AI bordereaux automation systems are designed to align with. MGAs that have adopted Blueprint Two data standards — including the ACORD data schema and the Lloyd's-specified coverholder reporting formats — are significantly better positioned to deploy AI bordereaux automation than those still operating on bespoke legacy formats.[4]
For MGAs not yet aligned with Blueprint Two standards, a Lloyd's delegated authority AI deployment typically includes a data standardisation workstream alongside the automation implementation. This standardisation is a prerequisite, not a post-deployment task. The AI schema mapping layer can only automate to a standard it has been configured against — a bespoke legacy format is by definition not a standard.
Lloyd's Blueprint Two · Delegated Authority Digital Transformation Progress Report [4]Frequently Asked Questions
We have multiple capacity providers each requiring different bordereaux formats. Can the AI handle that?+
Yes. The AI schema mapping layer maintains a separate configuration for each binding authority, mapping the same underlying policy data to each capacity provider's required format automatically. Adding a new capacity provider requires configuring a new mapping schema, which typically takes two to four days depending on the complexity of the required format. MGAs with 10 to 15 capacity provider relationships, each with different schema requirements, typically achieve the greatest productivity gain from automation because the manual mapping cost is highest in those operations.[3]
What happens if the AI maps a field incorrectly and the error reaches the capacity provider?+
AI schema mapping errors are caught at the validation step before the data is transmitted. The validation layer checks the mapped output against the capacity provider's schema specification and flags any field that does not conform before it leaves the MGA's system. The small volume of errors that pass validation — typically under 2% of fields in a mature deployment — are identified when the capacity provider's system processes the data and generate a correction request through the standard query process. Correction queries on AI-generated bordereaux are significantly lower in volume than on manually produced equivalents.[3]
How does real-time bordereaux data change the capacity provider relationship in practice?+
Capacity providers with real-time visibility into MGA portfolio data manage their delegated authority positions fundamentally differently from those relying on monthly batch submissions. They can identify accumulation trends as they develop, not after month-end. They can monitor loss ratio development in real time and raise queries while the relevant claims are still in early development. And they can evaluate MGA performance against current data rather than data that was three weeks old when it arrived. In authority renewals, MGAs providing real-time data consistently report better terms and higher authority limits than those on batch reporting cycles.[3][4]
Does our underwriting platform need to be replaced to implement bordereaux automation?+
No. AI bordereaux automation is typically implemented as an integration layer that connects to the existing underwriting workbench via API, extracting bind data at the point of transaction without requiring any change to the underwriter's workflow. The integration complexity depends on the API capabilities of the existing platform: most modern underwriting workbenches support the data connections needed. Older proprietary systems may require a data extraction adapter rather than a direct API connection. Implementation time ranges from six to 14 weeks depending on the number of capacity provider schemas and the quality of the existing data.[3]
How does bordereaux automation apply to Nordic market programme business operations?+
Programme managers and delegated authority operators in Norwegian and Nordic markets face the same bordereaux data challenges as Lloyd's market MGAs, though the specific data standards differ. Nordic capacity providers typically require bordereaux data aligned with their own policy administration system schemas rather than the Lloyd's Blueprint Two standards. The AI schema mapping approach is identical; the configuration targets are different. Norwegian-market data handling requirements, including Finanstilsynet's data governance expectations, apply to the automated pipeline in the same way as to manual processes. Specific regulatory requirements should be verified with qualified Norwegian legal counsel.[5]
What is the typical implementation timeline for bordereaux automation at an MGA?+
For an MGA with two to five capacity provider relationships, clean underwriting data, and an API-connected underwriting workbench, a full bordereaux automation deployment including real-time feeds, accumulation monitoring, and automated month-end file production can be operational in 10 to 16 weeks. MGAs with more capacity provider relationships, legacy underwriting platforms, or data quality remediation needs will take longer — typically 20 to 28 weeks. The data quality assessment and schema configuration work, rather than the AI components, typically accounts for 50 to 60% of implementation time.[3]
References
All statistics sourced from documented deployments and third-party research organisations. Links verified 2026. Click any citation to jump to its source.
AI in delegated authority: automating bordereaux processing