Decisioning at the Edge: Policy Matching at Scale | Towards Data Science

Towards Data Science

by Erika Gomes-Gonçalves

February 24, 2026

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The article discusses how a global insurance company optimized the assignment of online insurance policies to independent insurance agencies (IIAs) using a lightweight integer programming approach with PuLP, an open-source optimization framework. The goal was to replace manual, inefficient processes with a scalable and efficient solution that considers key constraints like agency capacity, geographic eligibility, fairness, and policy mix. This shift from traditional methods, such as round-robin assignment, aimed to reduce delays, improve transparency, and align outcomes with strategic goals like profitability and quality. Traditionally, policies were assigned manually or through simple heuristics, leading to inefficiencies like delayed assignments, uneven distribution of policies among agencies, and missed opportunities for optimal matches. These methods often prioritized fairness over effectiveness, resulting in suboptimal outcomes for both clients and agencies. The manual process also lacked scalability, causing further delays as the volume of policies grew. The solution involved developing a modular optimization model that balanced business rules with operational constraints. This pragmatic approach focused on delivering results that were "good enough" while allowing for future improvements through decomposition techniques or stronger solvers if needed. By measuring KPIs such as time to assignment, optimal agency selection, and fairness in distribution, the system ensured tangible benefits without compromising on complexity. The article highlights how even basic optimization tools like PuLP can solve high-value problems in real-world scenarios. By avoiding overly complex methods that struggle with scalability or computationally heavy solutions, the approach achieved a practical balance between theoretical perfection and operational feasibility. This demonstrates the power of lightweight optimization in addressing large-scale challenges while maintaining readability, auditability, and adaptability. For readers interested in AI and data science, this article underscores the importance of aligning technical solutions with business needs. By focusing

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Originally published on Towards Data Science on 2/24/2026