While managing fraud used to be the domain of claims professionals and while they undoubtedly remain the industry’s most seasoned fraud fighters, the battle is increasingly being fought by underwriting teams.
Over the past decade, the industry has successfully been using its own data combined with that available through public credit bureau and other sources to identify risks at the point of quote in order to minimise the potential threat to a book early on, before they are incepted.
CUE data is a great example of insurance intelligence that insurers and brokers have historically applied manually at the point of claim, but that for the last decade has been widely used at the point of quote and renewal.
There are five main elements to successfully automating CUE for data enrichment:
1. Search with every possible key
2. Cleanse the returned data
3. Summarise the cleansed data into a block of descriptive attributes
4. Perform a retrospective analysis to refine the interpretation strategy at each lifecycle stage and build a business case
5. Consider the deployment approach and implement
There is great value to be derived from CUE data in detecting fraud, but additional value can be added using a high level of expertise and interpretation.
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