Why insurers only find data problems after the claim is paid — and what it costs

There's a pattern that comes up in almost every conversation with claims and operations teams at P&C insurers.

Someone describes a data issue. An overpayment. A discrepancy that surfaced during reconciliation. A fraud case that only became visible in hindsight. And then, almost without fail, they say the same thing: "We only found out after the claim had already been processed."

It's not a rare edge case. It's the default mode of detection across the industry.

This post is about why that happens, what it actually costs, and why the problem is harder to fix than it looks — because it's not really a data problem at all.

The metric that tells the real story

P&C insurers measure profitability through the combined ratio. Below 100% means the business is profitable. Above 100% means claims and operating costs are outrunning premiums. Simple in theory. Brutal in practice.

What the combined ratio doesn't show you directly is how much of the cost side is driven not by legitimate claims, but by decisions made on unreliable data. Overpayments that shouldn't have happened. Settlements that didn't account for complete coverage history. Fraud that wasn't caught because the input data was inconsistent.

These costs don't appear as a line item called "data errors." They show up in the loss ratio, quietly, alongside everything else.

Why data issues don't look like data issues

Insurance data doesn't live in one place. A single claims decision draws on policy systems, coverage rules, historical claims records, third-party inputs, fraud scoring models — often managed by different teams, running on different systems, updated on different schedules.

These systems are rarely perfectly aligned. Data moves across them in batches. Ownership is fragmented. Nobody has a complete, real-time view of consistency across the full flow.

This is the core challenge of data lineage in insurance: tracing how a number was built, which systems it passed through, and whether every input along the way was current and consistent. Without that visibility, inconsistencies develop silently — and nobody sees them until a decision has already been made.

The result: issues are invisible at the point of origin. By the time a discrepancy surfaces — during a manual review, an audit, a reconciliation exercise — the claim has been processed and the money has left the business.

What late detection actually costs

Industry benchmarks put claims leakage — overpayments, duplicate payments, missed validations, incorrect settlements — at between 5% and 10% of total claims spend.

For a mid-sized insurer processing 50,000 claims at an average value of £8,000, that's an exposure of between £20M and £40M annually. At the midpoint, around £21M of that will already have been paid out by the time it's identified — making recovery manual, costly, and often incomplete.

These aren't theoretical numbers. UK regulators have forced insurers to repay over £200M to customers due to incorrect claims valuations. Direct Line alone repaid £30M following pricing and data errors. These incidents don't originate in failed processes. They originate in data inconsistencies that weren't caught at the point of decision.

The timing problem, not the data problem

Here's the reframe that matters: the issue isn't that insurers have bad data. Every insurer has data inconsistencies across a complex, multi-system environment. That's structural. It's not going away.

The issue is when those inconsistencies are found.

• Found before a claims decision: manageable, actionable, low cost.
• Found during review after approval: the financial impact has already occurred.
• Found during reconciliation or audit: recovery is expensive and often partial.

The current state for most insurers is detection at stage two or three. The goal isn't to eliminate every issue — it's to move detection to stage one, before money leaves the business.

This is exactly what data observability does when it's applied at the business layer — not just the infrastructure layer. The difference between knowing a pipeline is fresh and knowing the data feeding a claims decision is trustworthy is significant. One is a technical check. The other is a business safeguard.

Why this isn't getting fixed by existing tools

Most insurers have controls in place. The problem is that these controls operate after the claim has been approved — in review workflows, reconciliation processes, audit cycles. They're designed to catch issues that have already moved through the system.

Data tools, where they exist, typically operate at a technical layer. They tell you that something is wrong with a pipeline. They don't tell you whether that pipeline failure is currently influencing an active claims decision, or who in the business is affected.

That gap — between detecting a technical issue and understanding its business impact — is what the Sifflet control plane is designed to close. It catches data issues before they reach the business, shows why they happened, and how to fix them. In a claims context, that means knowing before approval whether the data feeding that decision is consistent, complete, and current.

What it looks like when detection moves earlier

The goal is confidence at the point of decision. That means knowing, before a claim is approved, that the data feeding that decision — policy coverage, claims history, third-party inputs — is reliable.

This changes the financial dynamic entirely. An issue caught before payout costs almost nothing to act on. The same issue caught after payout costs the payment itself, plus the operational overhead of recovery, plus the regulatory exposure if it's systemic.

The combined ratio looks better. The loss ratio is easier to defend. And the claims team — along with the CDO, the CFO, and the governance team preparing for their next regulatory examination — is working from reliable information when it actually matters.

The pattern is consistent: data issues in insurance are identified too late. The opportunity isn't to eliminate every issue — it's to catch them earlier, at the point of decision, before they create financial impact.

Sifflet is the control plane for Data and AI — we catch data issues before they reach the business, show why they happened, and how to fix them. See how it works for insurance →

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