The client is one of the largest insurance brokers in Germany, providing policies to private and corporate customers.
The client wanted to standardize their partner numbers codes and create a primary key to uniquely identify each customer and its relationship with channel partners.
Customer names, contact details, contract history and other critical identifying attributes were consistently incomplete or contradictory, which lead to policy errors and the need for excessive manual intervention.
As a result of merging data sheets over time, individual customers had multiple channel partners assigned to them, making it difficult to distinguish individuals across the client’s distinct branches.
Design and execution of an AI-driven solution to reconcile and clean customer and partner data records using natural language processing (NPL) techniques that produced distance-based similarity scores across all customers, stratified by key variables to not only clean but also enhance the data, enabling accurate customer scoring.
Identification of valid partner numbers using waterfall logic constructed to prioritize the client’s branch and active cases for the purpose of re-assigning unique primary identification IDs to each customer.
Built the technical framework powered by R, Excel and Equipped’s proprietary NLP engine that simplified future reiterative deliverables.
Results & Benefits
Client was able to easily identify each customer and its attributes by newly assigned primary number codes which facilitated both highly accurate cluster building and segmentation.
Customer identification and proper cohort grouping aided pricing models for new client policies, renewals and underwriting.