Before you assume experience determines accuracy, consider research from Nobel Prize-winning psychologist Daniel Kahneman:
even highly skilled professionals making judgements on identical cases can reach dramatically different conclusions. His studies show variations can exceed 50% of the claim value. This isn't about competence - it's about a phenomenon Kahneman calls 'noise', and it's costing your organisation far more than any system inefficiency.
The paradox is this: your expert claims handlers are brilliant at their jobs. But brilliance doesn't prevent inconsistency. And in specialty claims where individual decisions involve hundreds of thousands of pounds, inconsistency is extraordinarily expensive.
Understanding ‘the noise problem’ in claims management
Kahneman distinguishes between two types of error in human judgement: bias and noise.
Bias is systematic – when everyone consistently underestimates cyber claims, for example. Noise is random scatter – when equally qualified professionals reach wildly different conclusions about the same claim.
Think of a shooting target. If you’re biased, all your shots cluster together but miss the bullseye. You can correct for bias once you identify it. But if you’re noisy, shots scatter randomly across the target. There’s no pattern to fix – just inconsistency.
In specialty claims, noise manifests in:
- Reserve estimates that vary dramatically between handlers
- Inconsistent settlement authority decisions for similar claims
- Different assessments of fraud indicators or liability exposure
- Variable triage decisions about which claims need urgent attention
Our research across 250 claims professionals revealed that handlers frequently lack structured frameworks for complex decisions.
As one handler admitted: ‘It’s hard to quantify D&O claims. I wish we had a reserving tool’.
Without structure, each expert develops personal approaches. Different intuitions produce different outcomes – even when reviewing identical information.
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How does workload pressure or high caseloads degrade decision quality?
Cognitive science demonstrates that mental fatigue doesn’t just slow decision-making – it makes it noisier. When claims handlers are overwhelmed, they rely more heavily on instinct and less on systematic analysis.
The degradation occurs through several mechanisms:
- Reduced deliberation: Under time pressure, handlers make faster intuitive judgements rather than working through structured evaluation frameworks.
- Increased reliance on availability heuristics: Recent or memorable claims disproportionately influence reserves and settlement decisions.
- Sequential bias: The order in which handlers review claims affects their judgements – a phenomenon called ‘contrast effect’ where each claim is unconsciously compared to the previous one rather than evaluated on its own merits.
Our research found claims professionals describing exactly this pattern:
You have to clean up the data before you can even think about progressing the claim.
When expert capacity gets consumed by administrative burden, the quality of subsequent decisions suffers.
The marine claims director who told us ‘we’re doing six steps just to process one payment – it could be three’ wasn’t just describing inefficiency. He was describing cognitive load that leaves less mental capacity for the complex judgements that actually require expertise.
What cognitive biases commonly affect claims handlers?
Beyond noise, specific cognitive biases systematically affect claims decision-making:
- Anchoring bias: The first number mentioned – whether a broker’s estimate or a policyholder’s demand – disproportionately influences final reserve or settlement figures, even when handlers consciously try to evaluate independently.
- Confirmation bias: Once a handler forms an initial theory about a claim (fraud, coverage dispute, straightforward loss), they tend to notice evidence supporting that theory whilst discounting contradictory information.
- Availability bias: Dramatic or recent claims influence risk assessments out of proportion to their actual frequency. A handler who recently saw a large cyber claim may unconsciously over-reserve subsequent cyber notifications.
- Overconfidence bias: Experienced handlers can become overly certain in their judgements, particularly in domains where feedback is delayed or ambiguous. In long-tail claims, you might not discover whether your initial reserve was accurate for several years – too late to learn from the error.
- Loss aversion: Handlers show asymmetric risk preferences – they’re more motivated to avoid the appearance of under-reserving (which feels like a failure) than to avoid over-reserving (which feels prudent), even when over-reserving ties up capital inefficiently.
The challenge isn’t that claims handlers are uniquely susceptible to these biases – it’s that everyone is. The question is whether your claims process improvement strategy includes mechanisms to counteract predictable patterns of error.
What impact does systemic friction have on decision accuracy?
Kahneman’s research reveals something crucial: noise isn’t just about individual psychology. Environmental factors dramatically amplify or reduce it.
Our research identified several systemic friction points that worsen decision noise:
- Information fragmentation: When relevant data exists across disconnected systems, handlers make decisions with incomplete information – and different handlers access different subsets of available data.
As one claims handler explained:
Everything you do has to be manually re-entered... I don't trust the system.
When handlers don’t trust their data sources, they rely more heavily on instinct – introducing noise.
- Unclear prioritisation: Without intelligent triage, handlers can’t consistently distinguish genuinely urgent claims from routine ones.
As one TPA handler noted:
You can't tell which claims are actually sensitive just from the list.
This creates sequential effects where claims get attention based on factors (which broker called, what’s at the top of the inbox) unrelated to their actual importance – introducing randomness into outcomes.
- Missing decision context: When systems don’t surface relevant precedents or similar historical claims, handlers effectively re-invent approaches for each case. Without reference points, judgements become noisier.
Read more about insurance claims on our blog:
How do fragmented systems and unreliable systems lead to misjudgements?
System unreliability creates a particularly insidious problem: it trains handlers not to trust data, pushing them towards intuitive rather than evidence-based decisions.
The pattern emerges clearly in our research findings:
Claims handlers report systems that are ‘slow, fragile, and outdated’. They describe interfaces where ‘there’s too many different tabs… a lot of it isn’t even filled in anymore’.
This isn’t just frustrating – it changes how handlers work. When systems are unreliable, handlers develop workarounds: personal spreadsheets, email-based tracking, mental notes about which fields can be trusted and which can’t.
These workarounds introduce several sources of decision error:
- Selective data use: Handlers unconsciously weight information differently based on how easily accessible it is, not how relevant it is.
- Knowledge silos: Important context exists in individual handlers’ heads or personal files rather than being systematically available.
- Inconsistent analysis: Without shared frameworks and data sources, each handler effectively operates with their own process – guaranteeing inconsistent outcomes.
The fragmentation compounds when claims require ecosystem coordination. As one Head of Claims at an MGA admitted:
We don't even know who to contact on the follow market sometimes.
When communication requires manual tracking across disconnected channels, critical information gets lost or delayed – leading to decisions made with incomplete understanding.
How can inconsistent policies or ambiguous guidelines exacerbate poor decisions?
Even well-intentioned guidance can worsen decision noise if it’s ambiguous or inconsistently applied.
Common sources of policy-driven noise include:
- Vague authority limits: When settlement authority guidelines use subjective criteria (‘obvious liability’, ‘reasonable prospects of defence’), different handlers interpret thresholds differently.
- Incomplete precedent: Specialty claims often involve genuinely novel circumstances. When guidelines don’t address edge cases, handlers fill gaps with personal judgement – introducing noise.
- Conflicting priorities: When handlers face competing objectives (speed vs. thoroughness, policyholder satisfaction vs. cost control) without clear priority frameworks, they make different trade-off decisions.
- Selective enforcement: If some policy requirements are rigorously followed whilst others are treated as aspirational, handlers learn to apply judgement about which rules matter – introducing inconsistency.
The solution isn’t necessarily more detailed policies (which can create their own complexity burden). It’s ensuring that guidance provides decision structure whilst remaining clear about where judgement is appropriate.
The hidden cost of non-effective claims management
Decision noise carries costs that rarely appear in operational dashboards:
- Capital efficiency: When some handlers systematically over-reserve whilst others under-reserve, the organisation locks up more capital than optimal risk management requires – affecting profitability without improving outcomes.
- Capacity provider confidence: In delegated authority arrangements, inconsistent handling erodes capacity provider trust. When they review your claims decisions and see unexplained variation, they question your governance.
- Broker relationships: Brokers notice when similar claims receive different treatment. Inconsistency damages their confidence in your handling, making them less likely to place business with you.
- Regulatory risk: The FCA’s Consumer Duty requires demonstrable fair outcomes. When identical claims produce dramatically different results based primarily on which handler reviewed them, explaining fairness becomes challenging.
- Talent attrition: Perhaps most costly, capable professionals become frustrated when inconsistent decisions undermine their work. As one claims director observed: ‘I watch brilliant people get frustrated with systems instead of excited about solving problems. That’s not why they came into this industry’.
The Clyde & Co MGA Opinion Report 2025 found 77% of MGAs believe claims processes require fundamental improvement. This isn’t dissatisfaction with individual decisions – it’s recognition that systematic issues affect consistency and quality.
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Decision hygiene: the solution nobody talks about
Kahneman’s research points to a surprisingly straightforward solution: decision hygiene. Just as hand hygiene prevents infection without requiring medical expertise, decision hygiene reduces noise without requiring superhuman judgement.
The principles applied to improving claims management include:
- Structured judgement frameworks: Rather than asking handlers ‘what should this reserve be?’, provide decision trees that break complex judgements into component questions. This doesn’t remove expertise – it organises its application.
- Sequential independence: When multiple people review a claim, ensure they form independent judgements before discussing. Group discussions tend to amplify the first opinion expressed rather than leveraging diverse perspectives.
- Reference class forecasting: Surface similar historical claims before handlers finalise reserves or settlements. This anchors judgement to actual outcomes rather than intuition.
- Validation checks: Automatic data quality flags and fraud indicators don’t replace expert judgement – they ensure experts have reliable information to judge.
- Fatigue management: Recognise that decision quality degrades under cognitive load. Streamlining administrative burden (as the payment processing example earlier highlighted) preserves capacity for judgements that actually require expertise.
Survey respondents specifically requested exactly these capabilities. One Claims Operations Director explained:
Smart technology could handle routine tasks much faster... freeing us to focus on what requires real expertise.
This is the key insight: improving the claims process isn’t about automating expertise away. It’s about creating environments where expertise can be applied consistently.
The airline industry didn’t reduce accidents by finding superhuman pilots. They gave skilled pilots checklists, standard procedures, and systems that made consistency easier. Aviation safety improved not despite pilot expertise but by supporting it.
The same principle applies to claims management. Your handlers don’t need to be superhuman – they need systems and processes that help them apply their considerable expertise consistently.
When improving claims management, the question isn’t whether technology can replace judgement. It’s whether your processes systematically reduce noise or accidentally amplify it.
The 77% of MGAs recognising that fundamental improvement is needed represent both competitive pressure and opportunity. Those who understand that expert inconsistency is a solvable problem – not a talent failure – will gain decisive advantages in outcome consistency, capacity provider confidence, and talent retention.
Your best claims handlers aren’t making bad decisions because they lack expertise. They’re making inconsistent decisions because expertise alone isn’t enough. Decision hygiene gives them what expertise can’t provide: systematic consistency.
That’s not replacing human judgement. It’s finally giving it the support it deserves.
Revolutionise your claims operations with futureClaims™
futureClaims™ is an advanced platform designed to meet the demanding requirements of complex commercial and specialty claims, including the London Market.
