Clean Data, Clear Risks: How Data Cleansing is Powering Re/Insurance Precision

Statistia’s staggering figure underscores the importance of data management across industries, particularly in re/insurance. In the re/insurance sector, data is not just a byproduct of business processes – it is the foundation upon which risk assessments, underwriting decisions, and compliance are built.

So it follows that for our sector, the rapidly evolving global insurance market, the sheer volume of data generated and processed is also increasing almost exponentially. Yet, with such a massive influx of information, the quality of data often becomes compromised. Inaccurate, non-standardised or incomplete data can set off a chain reaction of errors, undermining risk models, misguiding exposure management, and leading to compliance breaches.

As we approach 2025, re/insurers are increasingly investing in robust data cleansing processes to mitigate these risks. In this editorial, we will explore the latest trends in data cleansing, the impact of poor data on exposure management, and the tools and techniques reshaping the insurance landscape.

The Impact of Poor Data on Exposure Management and Risk Models

At the core of re/insurance operations is exposure management—the process of evaluating and pricing risk across portfolios. This process relies on accurate data, such as property valuations, geographical information, and historical loss data. Exposure models, such as catastrophe models for natural disasters, are fueled by this data, and the quality of the input directly impacts the accuracy of the output.

However, exposure schedules or Statements of Value (SOVs) often come riddled with errors—outdated property valuations, missing fields, or inconsistencies across datasets. These data discrepancies not only skew the models but can also lead to mispricing of risk, which may result in significant financial loss or, worse, inability to pay claims accurately in the event of a disaster.

Inaccurate data also hampers decision-making. For instance, a re/insurer may overestimate their exposure in a particular region, leading to unnecessary capital allocations or reinsurance purchases. Conversely, underestimating exposure may leave the company vulnerable to catastrophic losses. The cascading effect of poor data is clear, as it touches every part of the insurance value chain—from underwriting to claims handling.

Compliance Risks Tied to Data Quality

Beyond the financial implications, incorrect or unverified data also brings a host of compliance issues. The insurance industry is one of the most heavily regulated sectors, and regulatory bodies are scrutinising data quality and governance more closely than ever. Regulators demand that insurers have robust systems in place for managing and verifying their data, particularly in light of increased transparency requirements and evolving risk landscapes.

Data sharing within the insurance chain also poses risks. Re/insurers often work with multiple partners – brokers, agents, and reinsurers – and need to share exposure data to ensure that all parties are aligned on risk levels. If one party is operating on incomplete or incorrect data, the entire insurance chain can become compromised, leading to disputes, delays in claim settlements, or breaches in service-level agreements (SLAs). Insurers must, therefore, ensure that their data is clean, accurate, and compliant before sharing it with partners.

Garbage in = garbage out

Though nearly 20 years old, Hurricane Katrina is the storm that really focused the market’s attention on loss modelling, data quality and the impact of bad or incomplete data. In the exposure management world this event was a game changer.

After the event, it became clear that a number of locations were coded as concrete ‘on land’ Casino’s whereas actually they were floating Casino’s built on barges, which were far more susceptible to storm damage. Incorrect location data translated to a poor understanding of where exactly levees were located and an underestimation of the ultimate flood risks.

Incorrect location data can put risks into or outside of hazard zones leading to an incorrect assessment of the risk. To increase efficiency around geocoding some Insurers opt to model locations based on ZIP codes over street addresses, however this has proven to increase the amount of inaccurate loss estimations.

Trends in Data Cleansing

The process of data cleansing involves cleaning, validating, and standardising data, particularly exposure schedules, to ensure that models run on reliable information. In recent years, technological advancements have transformed how this cleansing process is performed.

One of the biggest trends is the adoption of augmented data platforms that automate much of the data cleansing workload. These platforms use machine learning and AI to scan vast datasets, flag inconsistencies, and apply fixes at scale. The advantages are clear: speed, accuracy, and consistency. Augmented tools can cleanse large volumes of data more efficiently than it would take human analysts while reducing the risk of manual errors.

Techniques such as anomaly detection and pattern recognition are also becoming commonplace. These allow insurers to identify outliers in datasets – whether it’s an abnormally low property valuation or a misclassified exposure – and correct them before they cause issues downstream. Data enrichment, another technique gaining traction, involves enhancing raw data with third-party information (such as location intelligence) to fill in gaps and improve overall data quality.

Despite these advancements in the automation of data cleansing functions, human oversight remains vital. While AI-driven tools are excellent at automating routine tasks, they still require human input to set parameters, validate results, and make judgement calls on more complex data issues. Additionally, insurers must balance the need for rapid data cleansing with the risk of over-relying on automation. There’s always the risk that in an effort to streamline processes, insurers may inadvertently bypass critical validation steps, leaving errors undetected.

The Future of Data Cleansing in Re/Insurance

Looking ahead, the role of data cleansing in the re/insurance sector is only set to grow. As insurers continue to face pressure to innovate and reduce operational costs, we can expect to see even greater reliance on automated data platforms. Advanced AI systems are likely to evolve, allowing for real-time data cleansing that seamlessly integrates into exposure models. This will dramatically reduce the time it takes to underwrite policies, giving companies a competitive edge in the fast-paced market.

Moreover, data governance and compliance will continue to be a key driver. Regulatory bodies are unlikely to ease their demands on data quality; in fact, with the ever-expanding scope of environmental, social, and governance (ESG) considerations, insurers will need to ensure that their data is not only clean but also aligned with broader compliance and reporting standards.

In 2025, the re/insurance sector will likely see a convergence of data cleansing, compliance, and exposure management. Clean, validated, and standardised data will become the linchpin of the re/insurance industry, and those who fall behind risk not only financial loss but also regulatory and reputational consequences.

Insurers must ensure they have strong robust data validation, data cleansing and data enhancement, which are key to accurate risk assessment. Technology works best when monitored and controlled by a team with subject matter expertise and processes established by experience.

For those insured, providing as much data as possible around locations, occupancies, constructions and additional modifiers is also key. The more current and accurate the data, the more accurate the loss estimate and the premium.