What Is an Algorithmic MGA? Model, Stack, Proof

An algorithmic MGA is a managing general agent that underwrites through code instead of through a queue of human referrals. It holds delegated authority from a carrier, prices and binds risk automatically inside an agreed appetite, and reports back to its capacity provider as data rather than as a monthly spreadsheet. The model is spreading because capacity is easier to raise when performance can be measured continuously instead of quarterly.

What is an algorithmic MGA?

An algorithmic MGA is a managing general agent whose underwriting decisions are made by models and rules running in software, under delegated authority from a carrier, with humans supervising the portfolio rather than touching every risk.

A traditional MGA does the same job a carrier's underwriting department does, but on someone else's paper. It receives submissions, applies the appetite agreed with the capacity provider, quotes, binds, and reports the bound business back through bordereaux. An algorithmic MGA does not change that legal structure. Delegated authority, the binding agreement, and the carrier's ultimate exposure all stay exactly where they were. What changes is where the decision happens.

In a traditional MGA the decision lives in an underwriter's head, informed by a manual, a rating sheet, and experience. In an algorithmic MGA the decision lives in a versioned artifact: an appetite ruleset, a rating model, a set of data enrichments, and a decision log. A human wrote that artifact and a human supervises it, but the artifact is what runs on every submission, at the same speed, in the same way, at three in the morning.

How an algorithmic MGA differs from a traditional MGA

The difference shows up in five places, and none of them are about how smart the underwriters are.

• Unit of work. Traditional: the account. Algorithmic: the cohort. The underwriter tunes a rule that moves a thousand accounts, instead of pricing one.
• Speed floor. Traditional: a quote turnaround measured in days. Algorithmic: measured in seconds for the clean majority, with humans reserved for the exceptions.
• Consistency. Two traditional underwriters can price the same risk differently on the same day. An algorithmic MGA produces the same answer twice, which is the precondition for measuring anything.
• Reporting. Traditional: bordereaux compiled after the fact. Algorithmic: the bordereau is a byproduct of the decision record, so the capacity provider can see the book in near real time.
• Change management. Traditional: retrain people. Algorithmic: ship a new version of the ruleset, with the old version still on record for anything written under it.

That last point is the one most often missed. An algorithmic MGA is not just faster. It is auditable in a way a human book is not, because every bound risk can be replayed against the exact ruleset version that priced it.

Why capacity providers care about the model

Capacity is the scarce resource in the MGA world, and it is allocated on trust. A carrier or reinsurer delegating pen to a third party is accepting adverse selection risk it cannot see directly until the losses arrive.

An algorithmic MGA changes the shape of that problem. When appetite is encoded, the capacity provider can inspect the rules before the first risk is bound rather than reconstruct intent from a loss run two years later. When every decision carries a reason code, the carrier can distinguish a book that drifted from a book that was mispriced. When the data flows continuously, a breach of the binding authority shows up in days instead of at the annual audit.

This is also why the technology question and the capital question are the same question. Delegated authority audits, bordereaux quality, and data lineage are not back-office chores for an algorithmic MGA. They are the product being sold to capacity. The operational side of that is covered in more depth in delegated authority technology for MGAs and in how to automate bordereaux processing with AI.

The stack behind an algorithmic MGA

Strip away the branding and the stack is consistent across the market. Six capabilities have to work, in order.

1. Ingestion. Submissions arrive as email, attachments, portal forms, spreadsheets, and API calls. Whatever the source, they have to become structured records without a person retyping them.
2. Extraction. ACORD forms, statements of values, loss runs, and broker PDFs get read and turned into fields with confidence scores. This is where most implementations stall.
3. Enrichment. The submission is thin. External data on the entity, the location, the exposure, the broker history, and prior claims is what makes the risk assessable.
4. Appetite and rules. The carrier's binding authority is encoded: classes in and out, limits, attachment points, territory, referral triggers. This is the legal boundary of the pen, expressed in software.
5. Rating and scoring. Technical price plus a model score. The score orders risks by expected performance. The rating engine turns the accepted risk into a number the market will trade.
6. Decision, issue, and record. Quote, decline, or refer. Bind and issue documents. Write the decision, its inputs, its ruleset version, and its reason codes to a record the carrier can audit.

The distinction between step 5 and step 6 matters commercially and is explained in pricing engine vs underwriting decision platform. A rating engine that produces a premium is not the same thing as a platform that decides whether the risk should be written at all.

Underwriter time is the constraint that makes all of this worth building. Underwriters spend around 40% of their time on administrative tasks rather than on risk selection, according to Deloitte. In an MGA, where headcount is thin and the economics run on commission, that 40% is the difference between a book that scales and a book that plateaus.

What algorithmic underwriting does not mean

Three misreadings are common enough to be worth naming.

It does not mean no underwriters. The most durable algorithmic MGAs employ senior underwriters whose job moved upstream, from pricing individual accounts to designing and supervising the rules that price them. Judgment did not leave. It was moved to where it compounds.

It does not mean every risk is automated. In practice a book splits. A clean majority flows through untouched, a middle band gets a human check on one or two flagged items, and a tail is fully manual because the exposure or the ambiguity justifies it. Trying to push the tail through the machine is how algorithmic books get into trouble.

It does not mean replacing the carrier's core system. The policy administration system, the general ledger, and the regulatory reporting stay where they are. The algorithmic layer sits in front of them, decides, and writes back. This is the same architecture described in AI underwriting without replacing your core system.

How an algorithmic MGA proves itself

Capacity providers, and increasingly regulators, ask for evidence rather than a demo. Four artifacts do most of the persuading.

• A replayable decision log. For any bound risk, the inputs, the ruleset version, the score, and the reason codes.
• A shadow period. The model ran alongside the incumbent process on live submissions and its answers were compared before it had authority over anything. The method is described in shadow mode underwriting.
• A breach report. Evidence that out-of-appetite risks were caught by the system, not by luck.
• Version discipline. Every ruleset change dated, attributed, and mapped to the policies written under it.

An MGA that cannot produce these is asking for capacity on narrative. An MGA that can is asking for it on data, and the terms are usually different.

Where the model works best

Algorithmic underwriting rewards lines with high submission volume, standardized exposures, and data that exists outside the submission itself. Small commercial property, cargo and transport, cyber for SMEs, and specialty niches with a repeatable structure all qualify. Large complex accounts, bespoke wordings, and heavily negotiated programs do not, and the honest answer for those is that a human should keep the pen.

Brazil is a useful case in point. The market has volume and a broker-intermediated distribution structure, but carrier IT capacity is the bottleneck rather than ambition. Around 70% of insurers do not execute innovation because of IT limitations, according to BCG. An external layer that automates the underwriting journey without a core migration is the only version of the algorithmic model that can ship inside that constraint.

WIR Innovation is an external AI layer for insurers and MGAs that automates submission intake, quotation, underwriting, and decisioning without replacing the core system, with every decision explainable and returning a full audit trail. It has applied this pattern in a proof of concept with a global insurer in the Transport line. For the broader MGA context, see what an MGA is and how MGAs use AI.