Unlocking the power of clean data in insurance

The Hidden Cost of Dirty Data in Insurance

Data is the lifeblood of insurance decision-making. From underwriting to pricing and risk assessment, insurers rely on accurate, well-structured data to make informed decisions. Yet, many insurance professionals—primarily underwriters, who experience this challenge firsthand—find themselves burdened by the inefficiencies of data cleansing.

The challenge? Submission data often lacks standardization, varying from company to company and broker to broker, making it difficult to work with. PDFs and emails, in particular, do not structure data in a way that can be easily transferred into other tools or platforms.

These formats often contain crucial details—claims history, risk classifications, asset information, insured values, and policy terms—but extracting and formatting this data requires extensive manual effort.

Before insurers can even begin risk analysis, they must first restructure, reconcile, and enrich this unstructured data. This process is time-consuming, introduces opportunity for input errors, and ultimately leads to sub-optimal underwriting and risk decisions. When insights are built on incorrect or incomplete data, exposure assessments become flawed, pricing decisions suffer, and insurers take on more risk than they realize.

The Three Core Challenges of Data Cleansing

1. Restructuring: Making Data Usable

Submission data often lacks standardization, though the extent of this issue varies across companies and brokers. Columns are frequently mislabeled, formats differ across brokers, and critical details may be buried within PDFs or emails. Underwriters often spend hours manually reformatting and copying data into spreadsheets just to make it usable for pricing models.

There is a huge manual burden involved in data clean-up, so anything that can streamline the process is a welcome gift for underwriters who can then focus their time and effort on more sophisticated, value-add work.

How hx helps:

Restructuring Made Simple with Fuzzy Matching

2. Reconciliation: Spotting Errors and Changes

Validating data is a painstaking process—especially when comparing current submissions against prior-year data. Underwriters must be able to identify material changes, flag inconsistencies, and validate insured values. Without effective reconciliation tools, this remains a slow and error-prone task, requiring significant manual effort from underwriters to validate data accurately.

How hx helps:

Reconciliation and Validation: Instantly Spot Material Changes

3. Enrichment: Adding External Data for Deeper Insights

Enhancing internal datasets with third-party data (e.g. catastrophe modelling data from platforms like CatNet) is essential for optimal risk assessment. Yet, in most organizations, this is a fragmented process, requiring manual entry into separate systems—leading to time wasted on low value admin work and lost opportunities.

How hx helps:

Seamless Data Enrichment for More Powerful Analysis

The Business Impact of Unclean Data

Inefficiency: Studies show that actuaries and underwriters spend up to 50% of their time on manual data cleansing instead of high-value analysis. This delays pricing decisions and slows broker response times, impacting business agility.

Increased Risk of Errors: Manual data entry increases the likelihood of input errors—think of a miss-typed extra zero in a total insured value—leading to flawed risk calculations and mis-priced policies.

Compromised Underwriting and Risk Management: Skewed data results in inaccurate exposure assessments, and can create blind spots in risk evaluation. For example, incorrect location data can severely impact catastrophe risk modelling, potentially leading to unexpected losses.

hx: A Smarter Approach to Data Cleansing

the hx platform is purpose-built to solve these challenges, offering insurers a smarter way to ingest, validate, and enrich data seamlessly.

By leveraging hx, insurers transform data from a tiresome burden into a strategic asset. The result? Faster, more accurate underwriting, improved efficiency, and a competitive edge in risk assessment.

Key Benefits of hx:

Granularity: Unlocking Deeper Insights

One of hx’s biggest advantages is its ability to capture and utilize granular data from submissions. This enables underwriters to analyze risk at a more detailed level and apply historical insights to future business decisions. By leveraging structured, high-quality data, insurers can improve their understanding of new business opportunities and drive more precise underwriting strategies.

Clean data isn’t just about reducing inefficiencies—it’s about unlocking the full potential of insurance decision-making.

Ready to see hx in action? Request a demo today.