Anonymous
Situation: At Dayforward, our manual underwriting process for policy approvals was slow, error-prone, and inefficient. This resulted in delays and inaccuracies, which affected our overall operational efficiency and customer satisfaction.
- Research & Design: I began by analyzing the existing manual process to identify key areas for automation. I worked closely with the underwriting team to understand their criteria for policy approvals and designed an automated system using machine learning algorithms.
- Development: I developed the system using Python, integrating it with our policy management platform. The system included modules for data extraction, risk assessment, and decision-making based on historical data.
- Testing & Deployment: I rigorously tested the system with historical data and conducted user acceptance testing with the underwriting team. After successful validation, I implemented the system in phases, starting with a pilot program to gather feedback and refine the solution.
- Monitoring & Improvement: I continuously monitored the system’s performance and made iterative improvements based on feedback and real-time data.