Unlocking the power of mainframes: data integration use cases

Mainframes have long been the backbone of enterprise IT. For decades, they’ve powered the most critical systems in banking, insurance, healthcare, government, and retail handling massive transaction volumes with unmatched reliability, security, and consistency. Even today, many of the world’s largest organizations depend on mainframes to run their core operations.

But while mainframes excel at processing transactions, they weren’t built for today’s data landscape. Modern businesses need real-time insights, unified customer views, and seamless data flows across cloud applications, analytics platforms, and machine learning systems. This creates a gap: some of the most valuable data in the enterprise is locked inside systems that are difficult to access and integrate.

That’s why mainframe data integration has become such a critical priority. By connecting mainframe data with modern systems, organizations can unlock new use cases, from real-time fraud detection to personalized customer experiences, without replacing the systems they rely on. The result is a powerful combination: the stability of mainframes paired with the agility of modern data infrastructure.

Below, we explore how different industries are unlocking value from their mainframe data through integration.

Banking and financial services

Banks have relied on mainframes for decades to run core systems like deposits, loans, and payments. These systems are highly structured and transactionally consistent—but often siloed.

The most important integration use case in this sector is creating a unified customer view. By combining mainframe core banking transactions with CRM data, digital banking telemetry, and external market data feeds, institutions can finally see the full picture of each customer. This enables better personalization, smarter product recommendations, and improved customer retention.

Another critical use case is fraud detection in near real-time. Historically, fraud systems relied on batch data from mainframes, which introduced delays. By integrating mainframe transaction streams with modern analytics platforms and machine learning models, banks can detect suspicious activity as it happens—reducing losses and improving trust.

Regulatory compliance is also a major driver. Frameworks like CCAR (Comprehensive Capital Analysis and Review) and DFAST (Dodd-Frank Act Stress Tests) require complete, accurate, and timely data across the organization. Integrating mainframe data with risk systems, financial planning tools, and reporting platforms ensures institutions can meet these requirements without manual reconciliation or delays.

In short, integration allows banks to move from reactive, siloed operations to proactive, data-driven decision-making.

Insurance

Insurance companies depend heavily on mainframes for policy administration, billing, and claims processing. These systems contain decades of valuable data—but are often disconnected from newer systems like agent portals and customer engagement platforms.

One of the biggest opportunities is improving underwriting accuracy. By blending mainframe policy and claims data with third-party risk data (such as credit scores, geospatial risk, or weather data) and agent management systems, insurers can build more precise risk models. This leads to better pricing and reduced loss ratios.

Another key use case is accelerating claims processing. Claims data stored on mainframes can be integrated with digital intake systems, customer communication tools, and fraud detection models. This enables faster adjudication, automated workflows, and a better customer experience—especially in moments that matter most to policyholders.

Integration also unlocks cross-sell and upsell opportunities. By combining customer interaction records with policy data, insurers can identify gaps in coverage and proactively recommend relevant products. For example, a homeowner’s policyholder without flood insurance can be flagged based on geographic risk data.

Ultimately, mainframe integration helps insurers become more agile, customer-centric, and analytically sophisticated—without disrupting their core systems.

Healthcare

In healthcare, mainframes often power critical systems like claims processing and member enrollment. These systems are essential for operations but are rarely integrated with clinical or external datasets.

A major use case here is population health management. By integrating mainframe claims data with electronic health records (EHRs), provider network information, and social determinants of health datasets, organizations can identify at-risk populations and intervene earlier. This leads to better outcomes and lower costs.

Another important application is reducing hospital readmissions. Claims data can reveal patterns in patient utilization, while EHR data provides clinical context. When these datasets are combined, providers and payers can identify patients who are likely to be readmitted and implement targeted care plans.

Integration also supports improving HEDIS (Healthcare Effectiveness Data and Information Set) quality scores, which are critical for payer performance and reimbursement. By unifying data across systems, organizations can ensure accurate reporting and identify gaps in care delivery.

Beyond analytics, integration enables better care coordination. When data flows seamlessly between systems, providers, payers, and care teams can make more informed decisions, improving both efficiency and patient experience.

Government and public sector

Government agencies are among the largest users of mainframes, particularly for systems like tax processing, benefits administration, and citizen records. These systems are often decades old but remain mission-critical.

One of the most impactful integration use cases is improving service delivery. By connecting mainframe data with modern case management systems and digital service platforms, agencies can provide faster, more accessible services to citizens. For example, applicants for benefits can track their status online instead of relying on manual processes.

Another major priority is fraud detection and prevention. Government programs are frequent targets for fraud, and siloed systems make it difficult to detect anomalies. Integrating mainframe data with interagency datasets and advanced analytics tools allows agencies to identify suspicious patterns and act quickly.

Integration also enables cross-agency collaboration. Many government programs require coordination between departments, but data silos often stand in the way. By connecting systems, agencies can share information more effectively, reducing duplication and improving outcomes.

Finally, modern integration supports policy analysis and decision-making. With a unified view of data, policymakers can better understand program effectiveness and make data-driven decisions.

In a sector where efficiency and transparency are critical, integration transforms how governments serve their citizens.

Retail and supply chain

Retailers and supply chain organizations often rely on mainframes for inventory management and point-of-sale (POS) systems. While these systems are highly reliable, they are not inherently designed for real-time, omnichannel operations.

A key use case is inventory optimization. By merging mainframe inventory data with e-commerce platforms, logistics systems, and demand planning tools, retailers can gain real-time visibility into stock levels across all channels. This helps prevent stockouts and overstock situations.

Integration also plays a crucial role in reducing shrinkage and loss. By analyzing POS transactions alongside supply chain data, organizations can detect anomalies such as theft, fraud, or process inefficiencies.

Another important application is improving order fulfillment. Modern consumers expect fast, flexible delivery options. By connecting mainframe systems with warehouse management and last-mile logistics platforms, retailers can optimize fulfillment routes, reduce delivery times, and improve customer satisfaction.

Finally, integration supports demand forecasting and planning. By combining historical transaction data from mainframes with external signals like seasonality, promotions, and market trends, organizations can make more accurate predictions and align their supply chain accordingly.

In an increasingly competitive landscape, these capabilities are essential for maintaining margins and delivering a seamless customer experience.

How Fivetran supports mainframe integration

Modernizing access to mainframe data doesn’t require ripping and replacing legacy systems, it requires a reliable, scalable way to replicate that data into modern environments. This is where Fivetran plays a critical role.

Fivetran enables organizations to seamlessly replicate mainframe data into cloud data warehouses and lakes, making it available for analytics, machine learning, and operational use cases. With automated data pipelines, Fivetran handles schema changes, ensures data accuracy, and keeps data continuously updated with minimal maintenance.

For industries like banking and insurance, this means faster access to transaction and policy data for real-time decision-making. In healthcare, it enables combining claims and clinical data for better outcomes. In the public sector, it supports cross-agency data sharing and fraud detection. And in retail, it unlocks real-time inventory and sales insights.

By abstracting away the complexity of mainframe connectivity and data movement, Fivetran allows data teams to focus on delivering value not managing pipelines.

Building out support for mainframes

Mainframes aren’t going anywhere and they shouldn’t. They remain some of the most powerful and reliable systems in enterprise IT. But to fully realize their value in a modern data-driven world, organizations must integrate them with the rest of their data ecosystem.

Across industries, the benefits are clear: better customer experiences, faster decision-making, improved operational efficiency, and stronger compliance. With the right integration strategy and the right tools, organizations can extend the life and value of their mainframe investments while unlocking entirely new capabilities.

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