Your TPA Runs Your Claims and Owns Your Data. Do You?

Plenty of insurers outsource claims handling to a third-party administrator, and for good reasons — scale, specialism, speed to a new line. But there's a quiet cost that shows up later: the TPA runs the claims, and the TPA's system holds the claims data. What comes back to the insurer is a periodic bordereau — a summarized feed, formatted the TPA's way, on the TPA's schedule. You carry the risk and the loss, but you don't hold clean, granular, timely data on your own book. That gap becomes a real problem the moment you want to analyze, price, or automate against those claims.

Why outsourced claims become a data blind spot

When a TPA handles claims, the rich operational data — the notes, the timeline, the granular transactions — lives in their environment. The insurer receives an extract designed for billing and basic reporting, not analysis. It arrives late, in an inconsistent structure, often missing the detail that would let you understand loss trends or feed a model. So the insurer ends up managing a book it can't fully see, and every question about those claims routes through a data request to the TPA instead of a query against its own foundation.

Where it breaks down

  • Summarized, not granular. The bordereau rolls up what you need at the transaction and note level to analyze anything meaningfully.
  • Inconsistent formats. Every TPA sends a different structure, so consolidating across administrators is a perpetual data-engineering chore.
  • Latency. Feeds arrive weeks after the fact, so you're always looking at a stale picture of your own losses.
  • No control or lineage. You can't trace how a figure was derived, and you can't get more detail without asking — and waiting.

Why it's a data-foundation problem

Outsourcing the handling doesn't have to mean outsourcing the data. The fix is to treat TPA feeds as a first-class ingestion problem: standardize each administrator's data into a common, granular model on your side, land it promptly, and govern it with lineage so it's analyzable and trustworthy. That's the same bordereaux-and-integration foundation that lets you consolidate any external data source — do it and your outsourced book becomes as visible as your in-house one. The TPA can run the claims; you should still own the data foundation underneath them.

What good looks like

  1. Granular ingestion that pulls transaction- and note-level detail, not just a summary bordereau.
  2. A common claims model that normalizes every TPA's format into one analyzable structure.
  3. Timely feeds so you see your losses close to real time, not weeks later.
  4. Governed lineage so every figure is traceable and you're not dependent on a data request to understand your own book.

Delegating claims handling is a legitimate strategy; losing sight of your own claims data is an avoidable side effect. Building the ingestion and governance foundation that keeps your outsourced book visible is exactly the kind of work we do with insurers at IntelliBooks.

You can let someone else handle the claim. You shouldn't let them be the only one who can see it.

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