The Client
- 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.
The Challenges
- 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.
Solution Delivered
- Design and execution of an AI-driven solution to reconcile and clean customer and partner data records using natural language processing (NLP) 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.
- Granular customer insights fuelled unpreceded modelling depth, scenario analysis and pricing optimisation.
