Which Agents Send You Good Business? Your Data Knows and Won't Say

For insurers that distribute through agents and brokers, the single most important question about the business is also one of the hardest to answer: which of our distribution partners send us profitable business, and which send us the losses? Every insurer has the raw data to answer it — premiums, claims, and the producer code stamped on every policy. Almost none can answer it cleanly. The information sits in the systems, and the systems won't say, because nobody has connected the dots between what an agent produces and what it actually costs.

The question you can't quite answer

It sounds simple: join policies to claims, group by producer, and rank. In practice it collapses under the usual problems. Policy data lives in one system, claims in another, commissions in a third, and tying them together by producer — accounting for the fact that the same agency appears under different codes, that business moves between producers, that a "good" loss ratio this year might be a bad one once claims develop — is exactly the kind of entity-resolution-and-integration work most insurers haven't done. So the conversation about distribution performance happens on gut feel and relationships, not on a clean, defensible view of who's actually profitable.

What the data could tell you

  • True producer profitability. Not premium volume — everyone knows who writes a lot — but loss ratio and profitability by producer, adjusted for mix and development. The agent who writes less but cleaner is often worth more than the volume leader.
  • Adverse selection by channel. If certain producers consistently send you the risks that go bad, that's a signal about their book, their underwriting, or how they're using you — and it's invisible without the join.
  • Where to invest the relationship. Appointments, commission incentives, and support should flow to the producers who send good business. Without the data, they flow to whoever shouts loudest or writes the most.
  • Early warning. A producer's book deteriorating is something you'd want to catch early, but you can only see it if profitability-by-producer is a live metric rather than an annual guess.

Why it's a data problem, not an analytics one

The analysis itself is not sophisticated — it's a profitability calculation grouped by producer. What's hard is the foundation underneath: resolving producers and agencies across systems and code changes so "this agent" means one thing; joining premium, claims, and commission data reliably; and accounting for claims development so this year's flattering number doesn't mislead. Once that foundation exists, the insight is almost trivial to produce. Without it, no amount of dashboarding helps, because the underlying join is wrong. This is the same resolution-and-integration work that underlies customer 360 — pointed at the distribution side instead of the customer side.

What good looks like

  1. Producers and agencies resolved to stable identities across systems and over time, despite code changes and moves.
  2. Premium, claims, and commission joined reliably at the producer level.
  3. Development-aware profitability so long-tailed lines aren't judged on immature numbers.
  4. Producer performance as a live metric feeding appointment, incentive, and support decisions — not an annual retrospective.

Distribution is where insurers make and lose the most money, and most manage it half-blind because the data that would reveal producer profitability is scattered and unresolved. Building the foundation that answers "who sends us good business?" cleanly — producer resolution, reliable joins, development-aware analysis — is exactly the kind of work we do with insurers at IntelliBooks.

Your data already knows which agents are worth their commission. The only question is whether you've built the foundation that lets it tell you.

Comments

Post a Comment

Popular posts from this blog

Why Your Insurance Data Warehouse Didn't Fix Anything

You built the data warehouse. It took eighteen months and cost more than planned. Policy data, claims data, billing — it's all in there. The project was declared a success and everyone moved on. And yet: underwriting still can't see the whole customer. The AI initiative is still stalled. Someone still asks for a report and waits three weeks. Nobody says it out loud, but the question hangs there — what exactly did we buy? You're not alone, and it isn't because you built it badly. It's because a warehouse solves a problem you didn't have. The problem a warehouse solves Warehouses solve location . Before: your data was in eight systems. After: it's in one place. That's genuinely valuable — you can run a query across policy and claims without begging two teams for extracts. But notice what that fixes: it fixes where the data is . It doesn't touch what the data means , whether it's trustworthy, or whether the records refer to who you think t...
Read more

Embedded Insurance: Why the API Is the Easy Part

Embedded insurance is the idea that coverage should be offered at the moment of need, inside someone else's product — travel insurance at checkout, device protection when you buy the phone, cover for the rental car in the booking flow. It's one of the fastest-growing distribution models in the industry, and every insurer has a strategy slide about it. Most of those strategies underestimate the same thing, and it isn't the partnerships or the product. It's the data plumbing. Everyone assumes the API is the hard part. The API is the easy part. What embedded insurance really demands The promise of embedded is speed and invisibility: a customer buys a flight and, in the same breath, is offered exactly the right travel cover at exactly the right price, and accepts with one tap. No forms, no friction. Deliver that and you've won. But "exactly the right cover at exactly the right price in one tap" makes brutal demands on your back end: You must price i...
Read more

Insurance Knowledge Graphs: The Foundation AI Needs Before It Can Think

Every insurer wants enterprise AI. Few realise they're missing something much more fundamental. Knowledge. Not data. Knowledge. Insurance companies already possess enormous amounts of information. Policy systems contain customer records. Claims platforms hold loss histories. CRM systems track interactions. Document repositories store underwriting reports. Data warehouses consolidate analytics. Yet AI still struggles to answer surprisingly simple questions. Not because the information doesn't exist. Because the relationships between that information remain invisible. Data tells you what exists A customer database tells you who someone is. A policy database tells you what they bought. A claims system tells you what happened. An accounting platform tells you what was paid. Each system is internally consistent. Together they rarely behave like a connected enterprise. Humans mentally assemble those relationships every day. Computers usually canno...
Read more