Managing general agents (MGAs) carry more delegated underwriting authority than ever, and in 2026 that authority is increasingly a technology question rather than a purely underwriting one. The MGA channel has grown into a large slice of specialty premium: in the United States, MGA premium reached approximately 85 billion dollars in 2022, according to Conning's managing general agent study, and the segment has kept expanding as carriers hand more binding authority to specialists. Delegated authority lets an MGA bind risk on a carrier's paper, but it also concentrates operational load. Every submission, every bordereau, and every audit trail has to be handled with the rigor a carrier would apply, usually with a far leaner team. The technology that supports that load is now a competitive differentiator.
Why delegated authority is a technology problem in 2026
Delegated authority works because a carrier trusts an MGA to underwrite inside a defined appetite and to report accurately on what it binds. That trust rests on data. The carrier needs clean, timely bordereaux to see exposure and performance; the MGA needs fast submission intake to win business from brokers who expect same-day answers. When either side runs on manual re-keying and email attachments, the arrangement gets slower and riskier at the same time.
At Lloyd's, delegated authority through coverholders accounts for a large share of the market's premium, according to Lloyd's coverholder reporting, which is a useful reminder that this is not a niche operating model but a mainstream one. As more premium flows through delegated arrangements, the operational and reporting demands scale with it, and the MGAs that handle that load cleanly are the ones carriers keep renewing capacity with.
Where the operational load actually sits
Three tasks absorb most of the effort in a delegated authority operation, and all three are document-heavy.
The first is submission intake. Submissions arrive as email bodies, PDFs, and spreadsheets in inconsistent formats, and a thin underwriting team has to read, structure, and triage them before any risk decision happens. Speed here directly affects hit rate. See how insurance submission intake automation works for the mechanics.
The second is bordereaux processing. Premium and claims bordereaux have to be validated, normalized, and reconciled before they can be reported to the carrier. Format drift across brokers and cover months makes this brittle and slow when it is done by hand.
The third is straight-through processing. The more clean risks flow end to end without manual touch, the more an MGA's scarce underwriters can spend their time on complex, high-value risk. Measuring and lifting that rate is a direct lever on capacity. See how to increase your straight-through processing rate.
An external AI layer, not a core replacement
The instinct in many operations is to fix this with a bigger core system or a multi-year migration. For an MGA, that is usually the wrong shape of solution. The core policy administration and bordereaux systems already work, capacity providers are integrated with them, and ripping them out introduces risk and delay for a problem that lives at the edges: reading unstructured submissions, extracting fields, enriching broker and risk context, and routing by appetite.
This is where an external AI layer fits. It sits on top of the systems an MGA and its carrier already run, reads the inbound material, structures it, and hands clean, decision-ready data to the existing workflow. Nothing in the core is replaced. The layer adds intelligence around the systems of record rather than becoming a new one. For a fuller treatment of that boundary, see AI underwriting without replacing the core system and what an MGA is and how MGAs use AI.
Compliance stays where it belongs: SUSEP and LGPD
In Brazil, delegated authority and specialty arrangements sit under SUSEP oversight, and the accountability for underwriting and reporting does not move because a task is automated. Any layer that touches personal or broker data also has to comply with the LGPD, which means clear purpose, data minimization, and traceability for how information is processed.
An external AI layer helps rather than complicates this when it is built for auditability. Because it structures and logs how each submission and bordereau field was read and routed, it produces the kind of traceable record that both the carrier's audit team and a regulator expect. The design goal is straightforward: add speed without removing the human accountability and record-keeping that delegated authority depends on.
How WIR fits
WIR is the external AI layer that runs on top of the systems an MGA and its capacity providers already use, and it never replaces the core policy or bordereaux system. It reads multichannel submissions, extracts and validates fields, enriches context, and routes work by appetite and exposure, leaving the systems of record and the underwriting authority exactly where they are.
In a proof of concept with a global insurer in the Transport line, WIR's external AI layer sat on top of the insurer's existing systems. That is the shape of deployment an MGA can adopt without a core migration or a long IT project: intelligence added at the edge, accountability and records preserved, and underwriters freed to spend their time on the risk that actually needs judgment.
Perguntas frequentes
What is delegated authority for an MGA?
Delegated authority is the arrangement that lets a managing general agent bind risk on an insurer's paper within a defined appetite. The carrier delegates underwriting authority and relies on the MGA to underwrite inside those limits and to report accurately through bordereaux. Accountability for the underwriting and the reporting stays with the carrier and the MGA, so the data and audit trail behind that authority matter as much as the risk decisions themselves.
Does adding AI to a delegated authority operation mean replacing the core system?
No. The core policy administration and bordereaux systems already work and are integrated with capacity providers, so replacing them adds risk and delay. The load that slows an MGA down lives at the edges: reading unstructured submissions, extracting fields, and reconciling bordereaux. An external AI layer sits on top of the existing systems and handles that edge work without touching the core.
How does an external AI layer help with bordereaux and submissions?
It ingests multichannel submissions and bordereaux in their inconsistent formats, extracts and validates the fields, enriches broker and risk context, and routes work by appetite and exposure. Clean, decision-ready data then flows into the existing workflow. The result is faster submission intake, more reliable bordereaux reconciliation, and a higher straight-through processing rate, without a new system of record.
How does this work with SUSEP and LGPD in Brazil?
Delegated authority and specialty arrangements sit under SUSEP oversight, and automating a task does not move accountability. Any layer that touches personal or broker data must comply with the LGPD, which requires clear purpose, data minimization, and traceability. An external AI layer built for auditability logs how each field was read and routed, producing the traceable record that both the carrier's audit team and a regulator expect.
Is WIR an MGA or an insurer?
No. WIR does not carry risk and is not an MGA or an insurer. It is the external AI layer that an MGA or specialty insurer runs on top of its existing systems to read submissions, structure data, and route work. The underwriting authority, the systems of record, and the risk stay with the MGA and its carrier.