Domain-Centric Agile Modeling for Legacy Insurance Systems

Legacy insurance systems have accumulated decades of complexity in their codebases and business logic. This complexity is spread across batch jobs and shaped by regulation, rather than architecture. Directly applying modern Agile modeling to such a landscape often throws developers off track and into frustration.

That is where Agile can work, but only when recentered around the realities of the domain. A domain-first perspective is captured by the fact that success in these environments cannot be achieved by providing screens and endpoints but by replicating the essence of how the business operates.

Where Agile Fails Without Domain Awareness

In many insurance transformation initiatives, every team begins by modeling the interface, which involves writing stories for forms, APIs, or dashboards. Legacy systems don't behave on the interface level, though. They act at the process level. The accurate units of business logic are actions such as policy renewal, claim escalation, underwriting override, etc. Unfortunately, those don't always show up in a UI.

The team I worked with was a mid-sized insurer that automated policy life cycles, specifically renewals. Stories about frontend behavior started as a model for the first, but implementation quickly hit a wall. Pricing logic was clogged in a 15-year-old script. Multi-state compliance tables were used to conduct eligibility checks. Unraveling legacy dependencies was needed for every "simple" task.

So we paused and started modeling from the domain behavior and from how the renewals were actually happening in the business. By letting us reorient, we could build more accurate, testable, maintainable functionality while still working in an iterative and Agile way, a necessity in intensely regulated environments like insurance domains and SaaS companies where business logic is tightly coupled with compliance. 

Why System Analysis Must Come First

The shift wasn't accidental. No coding began until the required System Analysis was complete. We mapped out how renewals worked: who triggered them, what data was relevant, where decisions were made, etc. This analysis revealed inconsistencies in existing systems and knowledge gaps across teams.

Without that upfront effort, the software we delivered would not have been of value. Such understanding is not a luxury in complex environments, such as the insurance industry. It's a precondition for success.

Design Grounded in Business Reality

Once we had a clear picture of the system's behavior, we started designing our modular functionality around it, ensuring that it truly met the business's needs. This wasn't just interface design work; it was more profound architectural Design work involving how information flowed, where the rules lived, and what would have to change for our modernization efforts to succeed.

Instead of focusing on the business events themselves, premium recalculations, claim reopenings, and compliance flagging, we centered our design approach around these events and the language that everyone, from the product team to the QA engineer to the developer, could speak. This made planning sessions more effective and significantly streamlined the process of clarifying requirements during the sprint.

Applying Agile Within This Structure

Execution was always kept fully Agile. Our team employed Scrum to structure sprints, manage velocity, and deliver continuous support. The change was in determining the source of truth: instead of extracting stories from features, we used business scenarios as the source of truth.

It enabled us to deliver software structured in a way that reflected the organization's workflow. The testing became more focused, acceptance criteria became more objective, and feedback loops to the stakeholders became shorter. Agile wasn't abandoned; it just got better because it came from the business, not just the product backlog.

Beyond Insurance: Lessons from Retail and SaaS

While this approach originated from insurance projects, it is applicable to any complex environment. One case involved working on a team with strong experience in Retail and Digital Product Domain, mainly in pricing systems across multiple brands. Region, season, inventory tier, and business rules all varied, and a traditional feature-first Agile approach repeatedly failed.

It wasn't that we moved faster; it was that by switching to domain-centric modeling, our backlog became more stable, and our delivery velocity grew as the one without pointless rewriting of misunderstood features.

For SaaS companies building for regulated markets, the same principles have been proven equally helpful. In this case, the challenge is not about legacy code at all but about ambiguous domain behavior. These identify how the software is used in real-world compliance workflows and help model against feature work so it remains aligned with business value.

Conclusion

Agile methodology provides structure and rhythm, but cannot replace understanding. Domain modeling offers the clarity to make Agile work in environments with decades of operational logic, such as those found in insurance, retail, and regulated SaaS.

Moving beyond surface-level story writing is necessary for teams working on Software Development and Implementation of complex software systems in retail or regulated industries. Using actual behavior as the basis for modeling, backed by meaningful system analysis and sound design, Agile can become something far more significant than a process, something that is advantageous.