Address Cleansing for Insurance Underwriting and Operations
Inaccurate property location data is a direct underwriting risk. Semilariti ensures every policy address is clean, standardised, and matched to its authoritative UPRN — so your risk models are working from accurate inputs.
The Risk of Dirty Address Data in Insurance
Insurance pricing and underwriting depends on accurate property location. Flood risk, subsidence risk, crime risk, property value — all of these models use address coordinates as a primary input. When an address is imprecisely located or incorrectly matched, the risk model is working on flawed data — potentially mispricing risk in either direction.
The problem is compounded by the fact that address data enters insurance systems through multiple channels: broker submissions, direct applications, policy renewals, claims forms. Each introduces the possibility of formatting inconsistencies, typos, and partial addresses that don't resolve cleanly to a specific property.
How Semilariti Supports Insurance Operations
Underwriting Accuracy
Match every policy address to its UPRN and retrieve precise coordinates — ensuring flood zone lookups, subsidence risk queries, and property characteristic models are based on the correct location.
Policy Portfolio Cleansing
Clean your existing policy book — correcting historical address inconsistencies, assigning UPRNs, and standardising formatting across your entire portfolio.
Broker Submission Standardisation
Process incoming broker submissions to standardise address data before it enters your core systems — preventing dirty data at source.
Claims Processing
Verify and standardise property addresses on incoming claims — ensuring the claimed property is correctly identified and consistent with the policy record.
Renewals Data Quality
Clean address data ahead of portfolio renewals — ensuring your risk reassessment is based on accurate, precisely located property records.
How Address Errors Affect Insurance Pricing
Insurance pricing models are location-sensitive. Flood risk varies street by street. Subsidence risk depends on local geology. Crime risk data is indexed to specific postcodes. Property rebuild costs vary by region. All of these models take an address as their input and return a risk assessment based on that property's location. When the address is incorrect — even slightly — the model may be returning risk scores for the wrong property. A policy address that is mis-geolocated by a few hundred metres can place a property in a different flood zone, a different subsidence risk band, or a different crime risk category. Across a book of tens of thousands of policies, these errors can mean that a meaningful proportion of properties are priced on inaccurate risk assessments. The fix is not more sophisticated risk models. It is more accurate address data feeding into the models you already have. Assigning a UPRN to every policy address means the property is precisely identified — and the coordinates returned with the UPRN are used to pull risk data for exactly the right location.Address Data Quality at Renewal
Renewal time is when address data problems that have gone unnoticed throughout the policy year tend to surface. A property that was correctly entered at inception may have had its address change. A property that was slightly incorrect at inception may now cause a matching failure against updated flood or subsidence risk data. Running a UPRN matching exercise on your renewal book ahead of the renewal cycle identifies these issues before they affect pricing or processing. Properties that cannot be matched are flagged for review. Properties whose UPRN has changed because of address changes in AddressBase are updated. The result is a renewal portfolio built on accurate, current address data rather than records that have drifted out of alignment over the policy year. Semilariti can process a full renewal book in a single batch, returning clean results in 5 to 30 minutes — making address data quality checks a practical part of standard renewal preparation rather than an additional burden.Broker Submissions and Address Inconsistency
A significant proportion of address data quality problems in insurance originate at the point of broker submission. Brokers work at speed, handling high volumes of client data, and address information is not always entered with the same care as financial figures. Common problems include abbreviated street names that do not match the standard form, postcodes that are one digit out, flat numbers missing from multi-occupancy buildings, and addresses entered from memory rather than checked against a document. Each of these errors is small and none of them is immediately obvious, but all of them can cause the property to be incorrectly located in your risk models — or to fail a matching check further down the claims or renewal process. The most effective point to fix this is before the data enters your core system. Processing broker submission data through Semilariti before import corrects formatting inconsistencies, assigns UPRNs, and flags any addresses that could not be resolved. Your underwriting team receives clean data