The slip had been agreed in principle on a Tuesday. By Thursday, there were four versions in circulation across three email threads, and nobody on the operations desk was certain which one the lead underwriter had actually signed off. A paralegal spent Friday afternoon reconstructing the negotiation history from inbox searches.
That is not an edge case. For brokers managing more than a handful of treaty relationships simultaneously, it is the default state. The treaty slip — the document that binds the placement, sets the terms, and determines how every subsequent bordereaux will be structured — sits in email and shared drives rather than in the system that governs the placement.
The cost is not just time. It is data that has to be re-entered at every handoff, a reconciliation cycle that opens every time a bordereaux arrives, and an audit trail that exists only as a reconstruction.
1. The Problem
Where manual processing breaks down
A treaty slip moves through six stages: creation, distribution, negotiation, amendment, binding, and bordereaux setup. Manual intervention concentrates at four of them.
- At creation, risk details extracted from the submission – limits, retentions, cedant details, coverage conditions — are entered by hand into the slip template. The Society of Actuaries’ 2026 analysis of international reinsurance structures found that complex treaty sidecar setups take nine to 24 months to launch, much of which is consumed by documentation cycles rather than commercial negotiation.
- At negotiation, amendments circulate as email attachments. A standard property treaty involves five to twelve rounds of amendment before binding. Each version introduces the possibility of a material change being overlooked, a previous agreed term being overwritten, or the wrong version being forwarded to a reinsurer.
- At binding, the agreed terms leave the email thread and have to be re-entered into the system of record. This is where the disconnection between the system of work and the system of record is most expensive — data that has been negotiated and agreed must now be typed in again, by a person, with no automated validation.
- At bordereaux setup, the same data is entered a third time into the reporting template. If anything was miskeyed at the binding stage, the error transfers to every subsequent bordereaux for the life of the treaty.
2. The Automation
How AI handles slip creation and data extraction
AI-native platforms approach slip creation in two stages: extraction and validation.
On extraction, Natural Language Processing reads the submission documents — PDFs, scanned slips, email attachments, prior treaty versions — and identifies the key data fields: limits, retentions, effective dates, party names, premium rates, clause-level conditions. It populates the slip template without manual input. Klippa’s 2026 analysis of insurance document automation found that multi-layered NLP and OCR achieves 95 to 99 per cent accuracy on insurance documents.
On validation, the system cross-references extracted data against internal records and flags exceptions for broker review. Ambiguous fields — a limit stated inconsistently across two documents, a cedant name that does not match the system record — are held for human confirmation rather than passed through silently.
Key Distinction
Automation at this level does not remove broker judgement from the process. It removes the manual labour surrounding it, so broker attention is directed at exceptions rather than routine transcription.
AI can also suggest appropriate slip templates based on the risk type, class of business, and market practice identified from the submission. For standard property and catastrophe structures, this removes a preparatory step. For more complex or bespoke arrangements, it provides a starting point that the broker adjusts rather than building from scratch.
3. Negotiation
Negotiation tracking and version control
Multi-party negotiations are where manual processes fail most visibly. When amendments arrive simultaneously from three reinsurers, tracking what has been agreed, what is pending, and who needs to approve what requires either a highly disciplined broker or a significant amount of back-and-forth. In practice, both.
AI systems monitor communication channels and document repositories continuously. When a new version arrives, the platform compares it against the current working slip and classifies every change. Material changes — coverage alterations, limit adjustments, new exclusions — are flagged for broker review. Administrative changes — clause numbering, typographical corrections, formatting — are logged without requiring intervention.
30%+
Variation in supervisory observance of reinsurance documentation and audit trail requirements across jurisdictions — an automatically generated audit trail addresses this variance simultaneously across all markets
IAIS Aggregate Peer Review Report, April 2026
When negotiation concludes, the final agreed version is the only version in the system. There is no ambiguity about which document governs the placement. The binding slip is the system record.
4. Downstream
From binding to bordereaux without re-entry
This is where the compounding effect of manual processing is most measurable, and where automation delivers its clearest operational return.
Under a manual workflow, the terms agreed in the slip have to be re-entered into the bordereaux reporting template. A finance or operations analyst extracts limits, retentions, and party details from the binding document and populates the reporting structure by hand. If anything was entered incorrectly at the slip stage, the error transfers. If the bordereaux template uses different field structures from the slip, someone reconciles them manually.
When treaty terms are captured in an AI-native system, bordereaux fields are pre-populated from the binding slip at the point of binding. Premium and claims statements inherit the agreed structure without re-entry. Subsequent amendments to the treaty update future bordereaux automatically. The reconciliation step that drives the 60 to 90 day bordereaux cycle in manual operations becomes a validation step — confirming data rather than re-creating it.
For Agiliux’s reinsurance broker clients, the downstream effect on reserve accuracy and settlement timing is material. The bordereaux is an output of the system rather than a parallel manual exercise.
5. Architecture
AI-native versus bolt-on: why the architecture matters
Bolt-on AI extracts data from a treaty slip and then hands it off to a legacy system via an integration. Two problems follow. First, the extracted data must be mapped to the legacy system’s field structure, which rarely matches the source document. Second, the context that makes AI validation useful — policy history, cedant relationship data, prior treaty terms — lives in the legacy system, not in the AI layer. The bolt-on AI is working without the full picture.
An AI-native platform has these constraints removed. The AI sits inside the system of record. When it processes a treaty slip, it has access to the full cedant history, prior treaty terms, and the coverage context that makes exception-flagging accurate. Fluentao’s analysis found AI-native systems achieve straight-through processing rates of 70 to 90 per cent, compared to 10 to 15 per cent for traditional approaches.
The practical difference appears most clearly when source formats change. When a cedant changes their slip format or a reinsurer updates their standard clause library, a bolt-on integration requires reconfiguration. An AI-native system reads the new format and adapts without manual intervention.
6. Comparison
Manual versus AI-automated: what changes at each stage
| Process stage | Manual approach | AI-automated approach | Reduction |
|---|---|---|---|
| Slip creation & data entry | Manual input from PDFs and emails, prone to omissions | AI extracts, populates, and validates from source documents | 40–60% |
| Amendment tracking | Email threads, manual document comparison, version confusion | AI flags every change, classifies by materiality, maintains audit trail | 70–80% |
| Version control | Multiple copies circulating, risk of binding from outdated version | Single source of truth, automated reconciliation | Near 100% |
| Bordereaux setup | Manual re-keying of treaty terms into reporting template | Treaty terms flow directly into bordereaux fields on binding | 80–90% |
| Stakeholder notification | Ad hoc emails, fragmented communication, missed approvals | Automated routing and notifications by role and trigger condition | 60–70% |
| Audit trail | Reconstructed from email history, incomplete, time-consuming | Automatically generated, immutable, available on demand | Eliminated |
Key Takeaways
| Five things to retain from this article |
|---|
| 01 Treaty slip processing fails at four stages — creation, negotiation, binding, and bordereaux setup — because each requires manual data transfer between systems that were not designed to share information. |
| 02 NLP accuracy on insurance documents now reaches 95 to 99 per cent, according to Klippa’s 2026 analysis, making automated extraction reliable enough for straight-through processing on standard treaty structures. |
| 03 The IAIS April 2026 peer review found more than 30 per cent variation in reinsurance documentation controls across jurisdictions — an automatically generated audit trail addresses this across all markets simultaneously. |
| 04 Bordereaux automation only delivers its full return when it starts at the slip — firms automating reporting without addressing upstream data capture are compressing the back end of a problem that begins at placement. |
| 05 AI-native systems achieve 70 to 90 per cent straight-through processing rates against 10 to 15 per cent for traditional approaches, because the AI sits inside the system of record rather than outside it. |
Frequently asked questions
Treaty slip processing covers the creation, negotiation, amendment, and finalisation of the document that records the agreed terms of a reinsurance placement — limits, retentions, conditions, and party details. Once bound, the slip forms the legal foundation for the placement and determines how premiums and claims flow for the life of the treaty, including the structure of bordereaux reporting.
AI uses Natural Language Processing to read unstructured documents — PDFs, scanned slips, email attachments — identify key entities such as limits, retentions, parties, and effective dates, then populate structured data fields without manual re-entry. Klippa’s 2026 analysis of insurance document automation found that multi-layered NLP and OCR achieves 95 to 99 per cent accuracy on insurance documents.
Yes. AI monitors document repositories and communication channels continuously, compares incoming versions against the current working slip, and classifies every change. Material changes are flagged for broker review. Administrative changes are logged automatically. The result is a single source of truth throughout the negotiation, with an immutable audit trail of every amendment.
An AI-native platform embeds AI in the system of record, so data captured at the slip stage flows automatically through every downstream workflow. Bolt-on AI extracts data but hands it off to a legacy system via integrations, meaning re-entry risk and context loss remain. Fluentao’s analysis found AI-native systems achieve straight-through processing rates of 70 to 90 per cent, compared to 10 to 15 per cent for traditional approaches.
Time savings vary by stage. Slip creation and data entry typically reduces by 40 to 60 per cent. Amendment tracking drops by 70 to 80 per cent. Bordereaux setup reduces by 80 to 90 per cent. Standard property and catastrophe treaty binding has moved from two to six weeks to days for firms operating AI-native platforms.
Yes. AI-native systems trained on reinsurance contract datasets handle quota share, excess of loss, stop loss, aggregate cover, and bespoke structured treaties. Complex multi-layer programmes require more initial configuration but benefit from the same accuracy and audit trail advantages as standard structures.
When treaty terms are captured in an AI-native system, they flow directly into bordereaux reporting structures without re-entry. Premium and claims statements inherit the agreed limits and conditions from the binding slip. Subsequent amendments update future bordereaux automatically, eliminating the reconciliation step that drives the 60 to 90 day manual bordereaux cycle.
Automated treaty processing generates an immutable audit trail at every stage — every amendment, approval, and version change is logged without manual reconstruction. Data flows directly from the slip into regulatory reporting structures, reducing the manual extraction that creates errors in Lloyd’s market returns and FCA conduct records.
Glossary
| Key terms used in this article |
|---|
| Treaty Slip The formal document that binds a reinsurance agreement between a cedant and a reinsurer. Records agreed limits, retentions, premium rates, conditions, and effective dates for an entire portfolio of ceded business. Errors introduced at the slip stage propagate through every downstream process. |
| Cedant The primary insurer that transfers, or cedes, a portion of its risk portfolio to a reinsurer under a treaty. The cedant is responsible for providing accurate bordereaux data to the reinsurer, making clean data at the slip stage operationally critical for both parties. |
| Bordereaux Periodic data reports submitted by the cedant to the reinsurer detailing premiums written, risks underwritten, and claims paid under the treaty. When treaty terms are captured in an AI-native system, bordereaux fields are pre-populated at binding without manual re-entry. |
| Natural Language Processing (NLP) A branch of AI that enables software to read, interpret, and extract structured data from unstructured text. In treaty processing, NLP reads PDFs, emails, and scanned documents to identify and capture key terms without manual input. |
| Straight-Through Processing (STP) The handling of a transaction from initiation to completion without manual intervention. AI-native platforms achieve 70 to 90 per cent STP rates in treaty placement against 10 to 15 per cent for traditional approaches. |
| System of Record The authoritative data source for a specific piece of information. When the system of record is also the system of work — where brokers perform tasks — data enters once and flows without re-entry. The separation of these two systems is the architectural root of manual re-entry. |
Conclusion
Manual treaty slip processing is not slow because reinsurance is complicated. It is slow because the data that governs the placement moves between systems that were not built to share it — three re-entries across six stages, each one an opportunity for a discrepancy that nobody finds until the bordereaux arrives.
Fixing the back end without fixing the front end does not work. Firms that have automated bordereaux reporting without addressing what happens at the slip stage find the same errors arriving in a cleaner format. The problem is architectural. Data needs to be captured once, in the system of record, and flow forward from there.
AI does not make the placement decision. The broker’s market knowledge, cedant relationships, and negotiating judgement remain what they always were. What changes is the infrastructure carrying the output of those decisions into the documents that govern them — without the re-entry, the version confusion, and the reconciliation cycles that currently sit in between.
See treaty slip automation in practice
Talk to the Agiliux team about how an AI-native platform handles the treaty lifecycle — from slip creation to bordereaux — without re-keying at each stage.
Sources cited in this article
- Society of Actuaries, International Reinsurance Structures for U.S. Life Insurers, February 2026. soa.org
- Klippa, Automate Document Processing in Insurance, 2026. klippa.com
- IAIS, Aggregate Report of Peer Review — Reinsurance and Risk Transfer Relative to ICP 13, April 2026. iais.org
- Fluentao, AI-Native vs Bolt-On AI Solutions, 2026. fluentaone.com
- Amwins, State of the Market — 2026 Outlook, 2026. amwins.com