James Hunt, Account and Operations Manager, Pro Global, looks at data modelling;
According to Statista, the amount of data created, captured, copied, and consumed globally is projected to reach 180 zettabytes by 2025. This is an increase of about 50% from 2023. For those that don’t know off the top of their heads, a zettabyte is equal to one sextillion bytes, or 1,000,000,000,000,000,000,000 bytes.
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However, exposure schedules or Statements of Value (SOVs) often come riddled with errors—outdated property valuations, missing fields, or inconsistencies across datasets.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 scrutin