What Is Claims Leakage? Causes, Cost, and How to Reduce It

Key Takeaways:

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• Claims leakage is the gap between what an insurer should pay on a claim and what it actually pays, through overpayment, underpayment, inefficient handling, or missed subrogation. Most of it traces back to process failure, not fraud.
• Leakage accumulates across ordinary handling decisions. Disconnected systems, adjuster variance, and manual rework create the conditions for it. Closed-file audits reveal it after the fact.
• Outcome data tells you a claim took 34 days to settle. It doesn't tell you where those days went, or which handling decisions drove the cost. Process-level visibility is what closes that gap.
• Enterprise process mining tools require significant investment and can take multiple quarters to return actionable results. Mid-market carriers and TPAs need a faster path to the same visibility.
• Insightful's Workflow Optimization connects to Salesforce Service Cloud, maps your claims process in weeks, not months, and surfaces decision-ready action items. All without a heavy, services-led implementation.

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What Is Claims Leakage?‍

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Claims leakage is the gap between what an insurer should pay on a claim and what it actually pays. It happens through overpayment, underpayment, missed subrogation, and inefficient handling.

When adjusters work from incomplete information, disconnected systems create rework, and no one can see where the handling broke down. The leakage accumulates claim by claim, until someone runs the numbers and wonders where that margin went.

Mid-market insurers running high claim volumes across distributed teams often don't catch the full cost until a quarterly review forces the conversation, months too late.

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How Much Does Claims Leakage Cost Insurers?

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The scale of the problem depends on who you ask and how honestly they're measuring it. What's consistent across the industry is that leakage accumulates across ordinary claims handling decisions, not just exceptional ones, and most carriers don't have a clean way to quantify it until they look hard for it.

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Aviva's transformation of its motor claims operation offers the clearest public benchmark. Working with McKinsey's QuantumBlack unit and deploying more than 80 AI models across the claims lifecycle, Aviva cut liability assessment time for complex cases by 23 days, improved claims routing accuracy by 30%, and reduced customer complaints by 65%. The company told investors the transformation saved more than £60 million, roughly $82 million, in 2024 alone.

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That gap between efficiency and savings has a name. Risk & Insurance describes what it calls the return on investment paradox: AI can make claims faster while organizations still fail to see bottom-line impact. Process improvements show up in cycle times before they show up in profitability.

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These cases hint at what becomes possible when an insurer can see how claims work actually moves at every stage. Most mid-market carriers aren't running 80 AI models. But the underlying problem Aviva solved is the same one claims teams face across the industry, at every scale and budget: no visibility into where handling breaks down.

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The cost of not solving it shows up in reserve leakage, missed subrogation, inconsistent adjuster decisions, and duplicate payments that clear without anyone catching them. All of it adds up.

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Claims Leakage vs. Underwriting Leakage

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These two terms get used interchangeably, but they describe different problems at different points in the insurance lifecycle.

Claims leakage happens during claims handling. An adjuster overpays a settlement, a subrogation opportunity goes unrecovered, a duplicate payment clears unnoticed. The leakage results from errors during the resolution of the claim.

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Underwriting leakage happens earlier, at pricing and risk selection. Risks get mispriced before a loss ever occurs, and the gap between what was charged and what should have been charged doesn't surface until the claims start coming in.

Both matter. But they require different fixes at different points in the process, which is why conflating them leads to solutions that address neither well.

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What Causes Claims Leakage?

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Claims leakage rarely traces back to a single failure. It builds across people, processes, and systems, often in ways that don't show up until a carrier runs a closed-file audit or a TPA gets scrutinized on a contract renewal.

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Manual Rework and Disconnected Systems

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When claims move across systems that don't talk to each other, adjusters fill the gaps manually. They rekey data, rebuild timelines, and reconcile information that should have flowed automatically. Every manual step introduces a chance for error. Duplicate payments, missed reserves, and inconsistent documentation accumulate across high-volume operations faster than any audit can catch them.

Inconsistent Process Adherence Across Adjusters
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‍Adjuster variance is one of the hardest leakage drivers to quantify because it looks like normal operations until you compare files side by side. The same claim type, handled by two different people, will produce different cycle times, different reserve levels, and sometimes different settlement amounts. Experience gaps account for some of it. Habit accounts for more. The problem compounds on large teams where no one has a view across how files are actually being worked.

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No Visibility Into How the Work Actually Happens

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Outcome data tells you a claim took 34 days to settle. It doesn't tell you that 11 of those days were spent waiting for a file to move between queues, or that the adjuster spent twice as long in one system as the workflow intended. That process-level detail is where leakage lives, and most claims operations have no systematic way to capture it. Closed-file audits surface it eventually. By then the money is already gone.

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Compliance and Audit Gaps

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TPA contracts get renewed on trust, and trust gets tested at audit time. When a client or regulator asks how a file was handled, the answer has to come from somewhere. Manual logs and system exports can reconstruct a timeline, but they take time, they're incomplete, and they invite questions about what's missing. Carriers and TPAs that can't produce a clean audit trail on demand face compliance exposure and a harder conversation about whether their handling process is actually under control.

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How Do You Measure Claims Leakage?

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The standard method is closed-file auditing: sample a population of settled claims, compare what was paid against what coverage, liability, and damages justified, and calculate the variance. It works well enough as a periodic check, but it's a rearview mirror. By the time the audit runs, the files are closed and the patterns that caused the leakage are still in play.

The metrics that get closer to the root cause are process-level ones. Average handle time broken down by claim type, adjuster, and workflow stage tells you where the same work is taking longer than it should. Degree of difficulty relative to settlement amount reveals whether complex files are being resourced appropriately or processed the same way as straightforward ones. Cycle time at each stage of the workflow shows where files are stalling between handoffs. How often files take non-standard paths through the queue is one of the clearest signals that the intended process isn't the actual one.

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These signals come from process data, not outcome data, and most claims operations aren't capturing it systematically. Insightful's Workflow Optimization captures this at the activity level inside Salesforce and across claims teams’ tech stack, connecting what adjusters actually do to the outcomes those decisions produce. Claims leaders can see handling patterns across the entire operation without waiting for the next audit cycle to tell them something went wrong.

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How to Reduce Claims Leakage Without a Multi-Quarter Rollout
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The conventional answer to claims leakage is a technology overhaul: new claims management software, a process mining implementation, a services engagement that runs six to twelve months before anything changes. For mid-market carriers and TPAs, that timeline is a non-starter. The leakage is happening now. The next contract renewal or audit isn't waiting for an implementation to finish.

The more practical path starts with visibility into what's already happening inside the systems that claims teams use every day. Most mid-market operations run on Salesforce Service Cloud. The handling decisions, queue movements, and workflow deviations that drive leakage are already generating data inside that environment. The problem isn't that the data doesn't exist. It's that no one is reading it at the process level.

Third-party pricing aggregating analyst research and procurement data put Celonis entry-level contracts starting around $150,000 per year. For large enterprise clients, full deployment can take months depending on scope and organizational readiness. That investment makes sense for a global insurer with a dedicated process excellence team and a multi-quarter transformation roadmap. For a 200-person claims operation with a lean technology team, it can be a structural mismatch.

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Insightful's Workflow Optimization connects directly to Salesforce Service Cloud and begins capturing process-level data without a lengthy services-led implementation. Within weeks, claims leaders can see how files are actually moving through the operation, where cycle time is inflating, which adjusters are deviating from the standard workflow, and where handoff delays are creating the conditions for leakage.

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See Your Claims Leakage in 14 Days
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Most claims operations know they have a leakage problem before they can prove it. An audit confirms it. The reserve review confirms it. Then the contract renewal conversation confirms it again. What's missing isn't awareness. It's the process-level data to show where the handling is breaking down and what it's actually costing.

Insightful's Workflow Optimization connects to your Salesforce Service Cloud environment and begins mapping claims workflows from day one. It gives claims leaders a faster process-level view of how files are actually moving, where cycle time is inflating, and which handling patterns are producing the most variance.

Get access to Insightful’s Workflow Optimization beta and see where your claims leakage is coming from, before waiting for your next audit to tell you.

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FAQs

What is an example of claims leakage?

‍A common example is a missed subrogation opportunity. A carrier pays a claim in full, then fails to pursue recovery from the liable third party. The payment was correct. The recovery never happened. Another example is duplicate payment, where the same service or repair gets reimbursed twice across disconnected systems without anyone catching it before the file closes.

What's the difference between claims leakage and underwriting leakage?

Claims leakage happens after a loss occurs, during the handling and settlement of the claim. Underwriting leakage happens before a loss, at the point of pricing and risk selection. An overpaid settlement is a claims problem. A mispriced policy is an underwriting problem. Both erode margin but at different stages of the insurance lifecycle and through different mechanisms.

What is a claims leakage benchmark or rate?

Industry estimates for claims leakage vary widely depending on line of business, carrier size, and how rigorously leakage is defined and measured. No single benchmark applies across the market. Carriers typically establish their own baseline through closed-file audits, comparing settled claims against expected outcomes. The more useful number is the one specific to your own operation.

Can claims leakage be reduced without buying new claims management systems?

Yes. A significant portion of leakage comes from process visibility problems rather than system limitations. Understanding how claims are actually being handled, where files stall, and where adjuster variance is highest can surface leakage drivers without replacing core systems. Insightful's Workflow Optimization connects to Salesforce Service Cloud and captures process-level data from the systems claims teams already use.