Standardize company names to identify exposure aggregations and policy clash to build unique customer insights

Insurers can have thousands of company name variations across their underwriting data. Yet standardized company names are essential to building a complete picture of insureds and avoiding over-exposure to loss events.

Match incoming names to a comprehensive list such as Capital IQ
Create a harmonized view of insureds, including parent company
Augment with financial intelligence data to build unique insights
Build a set of approved mappings with Machine Learning tools


  • Disparate company names across multiple source datasets mean aggregations can remain hidden
  • Company name reference lists run to tens of millions of records, but matching to them needs to be automated and efficient
  • Human review and editing of automated matching results is critical for data quality

How Quantemplate helps

Quantemplate’s name matching solution combines word-frequency analysis with automated fuzzy matching, all whilst keeping you in the loop. For a technical deep-dive, check out our webinar.

Diagram of Aggregation and Clash data flow showing diverse data sources being cleansed and validated, then insured names being matched against an S&P Capital IQ reference dataset of 18 million rows. A process of exact matching, fuzzy matching and word frequency matching prepares data for AI-powered matching via Automap Values. Cleansed company names then allow potential aggregations to be identified and risks to be augmented and enriched with financial data.

How does Quantemplate work?

Quantemplate is a self-service, cloud-based, automated data solution built for re/insurance professionals

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