Data Harmonisation & Categorisation

– Insurance


Our client is a home construction insurance and warranty provider whose product basis surrounded offering insurance cover for builders and developers undertaking large construction projects. The insurance company provides insurance cover based on the information given by the various building companies about their proposed build – therefore it is imperative for our client to maintain assurance that the information provided by these companies is correct.

There are often discrepancies with the information given and the design of the completed property, which is usually intentional and can be very dangerous for our client. Many construction companies claim that they will be undertaking a small construction project so that they can minimise their insurance costs. However, the end product of their building is a house worth with large monetary value. Construction firms will often do this to lower their insurance costs.

The Challenge

Our client has an embedded requirement to validate their datasets against external sources to confirm its integrity; and to also augment their dataset by adding data from missing fields to ensure a complete database. This process eradicates the chance of fraud and allows for an efficient allocation of resources.

However, our client found that manually verifying the datasets against external sources was extremely time-consuming and a costly task in terms of both time and money. Their previous solution of verification was incredibly labour intensive and often diverted employee attention from important development-focused tasks to time consuming processes.

The Solution

Using DDCAS’s deployed data solutions, our client was able to pull in and harmonise data from external sources to match against their own. These included Land Registry records and other open sources, including Zoopla, Rightmove and property-development databases, in order to populate missing data including the property sales price and property size. This data was then harshly analysed against the data within the insurance company’s database which validated the sales prices of those with acceptance of cover details – our data solutions were able to collate, harmonise and align data from external sources to then analyse against our client’s multi-faceted database.

Our data solutions even found additional data on larger complex builds, such as a block of flats or apartments. There can often be sparse information given on these as the builder may purchase cover for the one building and neglect to provide information of the individual properties within the building to lower their costs of insurance. However, our data solutions gave our client the powerful capability to understand all of the predatory moves being made against them. Our Digital Workforce integrated with our clients’ employees to provide actionable insights and paths of actions so that our client could efficiently mitigate against any misinformation both purposeful and accidental.


The data solutions that we configured reported a matching success rate of 95%, which far exceeded the required proposed success rate of 75%. The turnaround time was shortened by 70% due to the intelligent automation process and our data solutions continue to work efficiently within the insurance company in day-to-day operations, leading to more effective insurance policies that correctly fit the proposed properties, therefore maximising sales and profit for our client and simultaneously offering a massive reduction in the insurance fraud this firm faces.

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