Identity Resolution allows you to bring together ML-powered matching technology with the best Experian commercial and consumer data sets to build a more reliable, flexible and accurate view of customers.
The benefits at a glance:
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With help from Experian’s tracing solutions, we were able to reduce our ‘goneaways’ back book and reunite more of our customers with funds owed to them. This solution helps us work towards our original purpose of helping people take responsibility for their future and achieve a lifetime of financial security. The communication with Experian throughout the project was fantastic and we’re pleased with the results.
Sarah Simpson, Aegon
Functionality that delivers your goals
Identity Resolution manages the entire process of building better customer data through a series of steps.
1. Managing Siloed Data - using a powerful workflow designer to manage data preparation to a common schema
2. Identifying and fixing data quality issues using reference data sets and Experian ExPin and PhPin solutions
3. Using ML to optimize matching rules for one or a number of use cases
4. Resolving duplicate records
5. Automating the processes to ensure consistency and reduce data decay
Cloud based, On prem/Bureau
Data Management For Identity Resolution
Data quality management is a foundational component to having a clearer view of your customers. With the right tool, you can effectively prepare and activate your identity data set.
The solution uses rules-based fuzzy-matching to aid transparency of the matching process across complex data sets. Using supervised machine learning you can train the system to automatically generate new rule sets for one or many use cases.
Pinning data is matched against you own data sets and the results are ingested into Data Studio as a data set for interrogation, further matching exercises and reporting
Potential duplicates are clustered and full control is given as to how rules can be built to either select or build a best, or golden, record
We have native connectors to applications (eg CRM) and big data sources as well as high-speed JDBC connections to relational sources.