엣지에서의 의사결정: 대규모 정책 매칭

This article was written in collaboration with César Ortega, whose insights and discussions helped shape the ideas presented here. Independent insurance agencies are privately owned intermediaries that sell insurance policies from multiple insurers. Unlike large insurance companies, they don’t design products, set prices, underwrite risk, or pay claims; instead, they compare options across carriers, and place coverage that best fits the client’s needs, typically earning commissions for doing so. Here, the idea is to work together to deliver the best value for both the agency and the client. Reducing complexity Optimization in the real world is a spectrum. At one end are exact methods that can prove optimality, but they often can be computationally heavy at scale and can struggle as the problem grows in size and operational detail. At the other end are heuristics, ranging from simple rule-based baselines that are easy to explain but hard to maintain as complexity grows (often living in large excel sheets), to more advanced metaheuristics that scale well computationally but can be harder to justify, audit, or debug. In practice, the most effective approach often sits in the middle: pragmatic “good-enough” formulations, built with carefully chosen constraints that reflect both business rules and real operational limits as human workload and service quality. The goal is not theoretical perfection, but a solution that is deliverable, comparable against baselines, and easy to iterate. With a modular structure and a staged modeling strategy, we can start simple, measure impact with KPIs: tangible (time to assignment, optimal agency selection, etc.) and intangible (avoid unfair concentrations of policies in a few agencies, etc.), and evolve the system through small, safe improvements rather than waiting months for a textbook-optimal model. That’s why we chose a lightweight optimization formulation. It captures the constraints that matter (capacity, geographic eligibility, fairness, and bucket mix) and delivers a deterministic, auditable answer fast enough for real-time latency requirements. If needed, we can later extend the approach with decomposition techniques, stronger solvers, or heuristics without changing the system’s core contract. The baseline Historically, these digital policy-to-agency of assignments have been done manually, guided by non-standard criteria and individual judgment. While this approach sometimes works, this often resembled a round-robin approach: policies were distributed sequentially among available agencies (iia’s), with little consideration for differences in capacity, expertise, or expected performance. While simple and seemingly fair, it often leads to delays, missed opportunities, and uncertainty about which agency (iia) is the best fit. The process also did not scale well, creating further assignment delays, and the outcomes did not consistently align with strategic goals such as profitability, quality, reproducibility, and transparency. For this reason, we present how we solved an important problem using a lightweight integer programming approach that matches incoming online insurance policies to agencies in real time. The method maximizes a productivity score (reflecting how well an agency has performed in the past) while balancing agency capacity, fairness, and geographic admissibility constraints based on ZIP codes. We outline the mathematical formulation, the live-update logic, and the PuLP implementation. What problem are we solving? When a new online policy is purchased for a client, someone still has to decide which agency should handle it. We rely on agencies because they add value beyond the usual, such as advocating at claim time, servicing changes and renewals, cross-selling, and more. Importantly, agencies also originate demand: they bring new clients (and consequently new policies) into the funnel through their relationships and local presence, which compounds growth for the insurance company. From a customer perspective, this matters because the agency is often the primary point of contact: the quality and speed of agency (iia) service can shape the overall experience, especially during high-stress moments like claims or urgent coverage changes. Since agencies differ in licensing, geography, product strengths, sales reach, and day-to-day capacity, the “best” agency can vary from moment to moment. A real-time assignment optimization system routes each new policy to eligible, available agencies that are most likely to deliver value to both the business and the client, are treated fairly under clear rules, and are best positioned to drive future growth. Good Old-Fashioned optimization To create a clear assignment process, it’s essential to consider broader business goals: such as making sure the right agency handles the right type of policy to maximize key performance indicators (KPIs) like policy volume and quality. It’s also important that agencies understand how these