Why mainframe modernization in insurance is a strategic advantage

The insurance industry has some difficult choices to make.

Loss modeling needs to update daily, not quarterly. The climate crisis is rewriting actuarial tables in real time as catastrophic events become more frequent. A regional carrier in the US would have processed hurricane claims in manageable waves before. Now, they’re likely to face simultaneous floods, wildfires and severe storms in multiple states.

At the same time, regulators have higher expectations for data governance, traceability and reporting, even as competition in the market — including from digital-native insurers — squeezes margins.

And customers? They expect the same streamlined, real-time experience from their insurer that they get from a food delivery app.

However, many core insurance systems were built decades ago, in a different era. They were never designed for these demands, as we set out in a new NTT DATA perspective, Mainframe modernization: Why insurers can’t afford to stand still.

The mainframe challenge: Reliable but rigid

Mainframes are some of the most reliable systems ever built. They process millions of transactions with extraordinary consistency. For decades, they’ve been the backbone of policy administration, billing and claims. But reliability isn’t the same as agility.

Most mainframe environments were designed for monthly report runs, batch-processing claim settlements, and underwriting decisions that followed predefined rules which were embedded deep in code. The problem now is that they don’t support the demand for real-time data, instant decision-making and integration through application programming interfaces (APIs).

Furthermore, mainframe talent is becoming scarce. Many of the engineers who built and maintained these systems are nearing retirement, and replacing them isn’t easy.

In this environment, modernization needs to preserve stability while enabling agility, resilience and a better customer experience.

Why mainframe modernization makes strategic sense

AI-driven underwriting models, fraud detection and claims automation rely on accessible, well-structured data, real-time analytics and event-driven architectures. These capabilities aren’t usually found inside a monolithic, tightly coupled legacy stack.

When catastrophic events hit, systems must scale instantly. For instance, during a major storm, claim volumes can spike massively within hours. Cloud-ready architectures can absorb that surge where traditional fixed-capacity environments will struggle.

And then there’s customer experience. Digital-first servicing — including mobile policy updates and chatbot-driven inquiries — depends on APIs and modern integration patterns. If your core system can’t enable services cleanly, innovation slows to a crawl.

All of this makes modernization an absolute requirement for competitiveness.

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Understanding modernization pathways

Insurers have several options, each aligned with different business objectives and risk appetites. The right choice will depend on an insurer’s business priorities, risk tolerance and time constraints:

• Rehost (lift and shift): The quickest route to cost optimization. Applications move to cloud infrastructure with minimal code changes. There’s less operational complexity, hardware costs decline and you gain breathing room without disrupting the core logic embedded in your systems.
• Replatform: Here, applications shift to more modern runtime environments to improve flexibility and reduce overhead. Some components may be optimized, but large-scale code rewrites aren’t required. It’s a pragmatic middle ground.
• Refactor or re-architect: This is where deeper transformation happens, with legacy code converted into modern languages and frameworks to enable cloud-native operating models. Tools such as AWS Transform help automate parts of this journey more quickly. The result: Modular systems ready for AI, analytics and rapid innovation.
• Rewrite or replace: A clean-slate approach. Custom-built solutions or package-driven platforms replace legacy cores. This requires greater investment but delivers future-fit capabilities. AI-powered tools like Amazon Q Developer can speed up code development and modernization efforts.

The opportunity that lies ahead

For insurers, modernization paves the way for real-time analytics that improve underwriting precision and risk assessment, modular architectures that enable faster product launches, and swift digital claims-processing powered by automation and agentic AI.

Regulatory compliance also improves when data is transparent, traceable and auditable. And most importantly, modernization creates an AI-ready foundation for the next wave insurance innovation.

Every insurer’s journey is different, but the future of insurance won’t wait. Are your core systems ready to meet it?

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