Claims leakage isn't a data problem. It's a timing problem.

Ask a Head of Claims about data quality and you'll get a polite shrug.

Ask them about overpaid claims and the conversation gets serious very quickly.

This distinction is not semantic. It's the reason most attempts to solve claims leakage from a data angle go nowhere — and why the problem persists at an estimated 5–10% of total claims spend across the industry.

The wrong diagnosis

The standard framing goes like this: insurers have data inconsistencies, those inconsistencies lead to bad claims decisions, bad decisions lead to leakage. Ergo, fix the data.

It's not wrong. But it's incomplete in a way that matters.

Because the question isn't just whether the data is correct. It's when you find out it isn't.

A data error detected before a claim is approved costs almost nothing. The adjuster gets flagged, checks the discrepancy, makes the right call. The process absorbs it.

The same data error detected during a post-payment review — or worse, in an audit cycle three months later — is a different category of problem. The money is gone. Recovery is manual. If the issue is systemic, the exposure multiplies.

The cost of leakage isn't the error itself. It's the timing of detection.

Why claims data is structurally fragile

A single claims decision draws on more systems than most people outside the industry realise. Policy data. Coverage rules. Historical claims records. Third-party inputs. Fraud scores. Each system has its own update schedule, its own ownership, its own definition of what "current" means.

Without end-to-end data lineage across those systems, inconsistencies develop without anyone noticing. They don't trigger errors. They don't surface alerts. They just quietly propagate — until a claim is approved on the basis of incomplete or misaligned information.

By the time the issue is visible, the financial impact has already happened.

The question worth asking

In every conversation with claims and ops teams, there's one question that cuts straight to the core of this:

Where do data issues in your claims process typically get caught — before or after payout?

The answer, almost universally, is after.

Not because the teams aren't diligent. Not because the processes are broken. But because the systems and tools in place are oriented toward review and reconciliation — activities that, by definition, happen after the fact.

The gap isn't process. It's detection timing.

What moving detection earlier actually changes

This isn't about building a perfect data environment. That doesn't exist in a multi-system, multi-team insurer and it never will.

It's about shifting the moment of detection from after approval to before approval. From the audit cycle to the decision point.

That shift changes the economics of leakage entirely. Issues caught at the point of decision are cheap to act on. Issues caught after payout require recovery effort, potential regulatory reporting, and in some cases, customer remediation — the kind that ends up as a headline.

Data observability applied at the business layer — not just the infrastructure layer — is what makes that shift possible. The distinction matters: a healthy pipeline is not the same as trustworthy data. Your data can be fresh, on schedule, and correctly structured, and still be wrong in a way that costs you money at the point of a claims decision.

The combined ratio is a lagging indicator. Claims decisions are where it's actually built. Getting the data right at that moment — not afterwards — is where the real leverage is.

Claims leakage isn't a data problem. It's a timing problem. The tools and processes most insurers have are oriented toward detection after the fact. The opportunity is to move that detection earlier — before money leaves the business.

Sifflet monitors the data feeding claims decisions in real time, catching inconsistencies before they become payouts. Learn more →

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