Problem Statement
High volume of Request for Pricing (RFPs) caused delays and wasted efforts on low-probability RFPs, affecting underwriter efficiency and conversion rates.
Solution Approach
Solytics developed a Machine Learning (ML) model to prioritize RFPs with a high chance of winning. The approach included:
1. Preparing and collating data from multiple sources.
2. Performing data validation and preliminary Exploratory Data Analysis (EDA).
3. Designing pilots and conducting User Acceptance Testing (UAT).
4. Leveraging the NIMBUS Uno pipeline functionality for quick and seamless deployment.
Client Impact
- Identified RFPs with a higher chance of winning.
- Increased conversion rates by 5-10% through efficient triaging of RFPs with experienced underwriters.
Case Studies
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