The Real Cost of Bad Data in Specialty Insurance

It would be true to say that cost of bad data to specialty insurers is often recognised but rarely measured. 

We all recognise the symptoms: manual rework across teams, endless exception reports and compliance teams reacting to unexpected issues, yet few carriers have attempted to quantify the cost to their operations.

With over 50% of the market now covered by DQPro’s comprehensive data monitoring, we’re now in a unique and privileged position to start to do just that.

Using real operational data aggregated from 25+ specialty insurers, we’ve analysed what material data issues look like in practice.

By “material”, we mean the kinds of data quality, control or compliance issues that create tangible operational, financial or regulatory impact for specialty carriers daily.  

Data flows in specialty can be complex and are often market specific.  Throw legacy systems, market regulations and human operators into the mix and you have the perfect landscape for data related problems, each incurring a cost to carriers.  Worse, these issues often impact multiple teams.

Issues like:

• Incorrect allocation of premium (impacts reporting, solvency, reinsurance)
• Miscoded policies or claims (impacts pricing, reserving, reinsurance recoveries)
• Risks written outside of underwriting authority (compliance and reputational issues)
• Missing policy data items required by the regulator (fine risk and public disclosure)
• FX mismatches between policy and finance systems
• Invalid risk codes
• Discrepancies in market messaging

These aren’t abstract data governance problems. They’re the everyday operational issues faced by underwriting and operations teams across specialty markets.  They also help explain why traditional solutions, like data governance teams pushing theory and technical DQ tooling, struggle to make progress. After all, business side data issues are best resolved by the business teams responsible. But what did the figures tell us about how much all of this is costing?

The Numbers are Even Bigger than We Expected

Our research continues to evolve (more on this to come!) but for now, let’s jump to the headlines:

Firstly, if you’re a diversified, multi-line specialty insurer, you can reasonably expect > 15,000 material, operational data issues per annum for every $675M of GWP written. 

This headline number includes a wide range of issues large and small, across the open market, delegated and reinsurance segments typically covered by our rulesets.  It omits edge cases and carrier specific quirks, so the actual numbers will, if anything, be higher.

Secondly, for carriers lacking a comprehensive approach to resolution at source, over 60% of these issues are not caught until they cause downstream impact, with many issuesremaining undetected in source systems long after, creating latent legacy risk.

Assessing the average cost per data issue is more difficult, with multiple factors involved, for example:

• The point in the data flow at which issues are detected.

When data issues are identified at the point of entry, often in underwriting or claims ops, the cost to resolve them is minimal. When the same issue is detected weeks later – after feeding data warehouses, capital models, financial reports, or reinsurance submissions – the cost multiplies. The same data issue, found later, can become a much bigger problem.

• Issue materiality

Is the issue a minor update, with minimal impact or a reportable breach that requires to be declared to a regulator? Does the issue mean missed reinsurance recoveries (there are millions here alone) or regulatory scrutiny and reputational damage from a fine?

• The time required to resolve issues (ideally so they don’t reoccur)

Some issues require a simple field update on the policy system. Others require a long conversation with your producing broker, MGA or TPA with supporting evidence and the resubmission of risk data.  Issues can also be process or training related, requiring ongoing work with the individual teams or users responsible to address.  Time incurred in extra training is an investment but also a cost.

Our take: after removing outliers, normalising data and considering time and motion data from our customers, we estimate an average operational cost of $88-95 USD per issue.  There are caveats here too. Large fines like the £9.6M ($12.4M) levied by the PRA on one London insurer in 2022, cannot be directly compared with the relatively minor cost of updating policy systems, but when volume and frequency are considered, we found both impact cost figures substantially.

Combined, these figures suggest a conservative impact of > 0.22% of COR or more than $1.4M cost per $675M of GWP.

Why This Matters Even More Now

As carriers continue to invest in automation and AI, the quality of underlying data becomes even more critical.

Machine learning models, capital analytics, pricing engines – they all rely on consistent, accurate inputs. If upstream data is unreliable, downstream tech, however sophisticated, only amplifies the noise.

Quantifying bad data financially reframes the conversation and quickly becomes an economic issue rather than a “data quality initiative.”

For a mid-sized specialty carrier writing $1B+ GWP, preventing even a portion of that $2M+ annual leakage changes the ROI equation entirely. It’s your business case for change.

The real question isn’t whether bad data exists. It’s how early you’re catching it and what it’s costing you today.

The Great Upstream Opportunity

We launched DQPro because of the gap we observed in the capabilities of business teams to find and fix data issues at a much earlier stage, upstream at source.

Beyond the operational time savings, earlier detection drives significant downstream benefits:

• Increased operational confidence
• Fewer compliance breaches
• Improved capital allocation accuracy
• Stronger exposure modelling
• Reduced missed reinsurance recoveries
• Faster statutory and regulatory reporting cycles
• Cleaner data warehouses

This firm focus on the front end complements the downstream work of technical data teams who have long struggled to make technical DQ tools work in a business context.

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