Infosys’ AI and policy migration solutions target insurance technical and data debt

The insurance industry is undergoing significant changes due to  AI-driven automation and digital transformation, which are reshaping underwriting, claims, and policy management. However, many insurers continue to struggle with legacy system complexities, data fragmentation, and slow migration processes, preventing them from realizing the full benefits of AI.

Infosys is addressing these challenges with two distinct AI-powered solutions—XtractEdge, which automates data extraction and processing across insurance workflows, and the Policy Migration Solution (IPMS), which accelerates policy migration from legacy systems to modern Policy Administration Systems (PAS) platforms such as Majesco, McCamish, Vitech, and DXC. While these solutions can work together, they also function independently, each delivering specific business outcomes.

AI-powered data processing and policy migration: Infosys’ dual approach

Insurers need to address their technical and data debt if they’re going to make progress on AI. A typical policy migration experience is slow, expensive, and often dependent on SMEs for documentation and rule validation. Similarly, insurers find their core data locked up in documents and unavailable to use with AI. Infosys is tackling these persisting industry challenges with the following solutions:

1. XtractEdge: AI-powered document processing that extracts, validates, and organizes structured and unstructured data for underwriting, claims, and policy servicing.
2. Infosys Policy Migration Solution (IPMS): An AI-enabled framework that automates business rule extraction, legacy data transformation, and policy conversion, reducing reliance on manual validation.

By integrating these two solutions or using them independently, Infosys enables insurers to extract critical policy details from legacy documents. It maps the details to target PAS and automates rule-based configurations, significantly reducing migration timelines and operational risks.

Evaluating impact through real-world enterprise case studies

Enhancing underwriting with XtractEdge

A global insurer faced delays in underwriting due to fragmented broker submissions arriving in multiple formats such as emails, PDFs, and spreadsheets. By implementing XtractEdge, the insurer automated data intake, prioritized high-value risks, and reduced manual effort by 40%, leading to faster quote-to-bind cycles. However,  downstream automation remained limited without structured policy data, highlighting the need for a policy migration strategy alongside AI-powered document processing.

Accelerating policy migration with AI

A leading insurer struggling with multi-year policy migrations across 16 different PAS  adopted Infosys’ Policy Migration Solution (IPMS) to accelerate business rule extraction and data conversion. The insurer reduced SME dependency and migration errors by automating policy document interpretation and mapping legacy code to target PAS configurations, allowing them to consolidate their policy portfolio 40% faster than traditional approaches.

Why Infosys’ combined approach is critical for AI-driven insurance transformation

While many AI solutions focus solely on document automation or underwriting decision support, Infosys takes a broader approach by tackling legacy system migration alongside AI-driven processing. However, achieving sustained transformation requires more than just integrating AI—it demands a clear strategy to address enterprise-wide challenges that often hinder modernization efforts.

• Data extraction and business rules automation: Infosys leverages AI to extract and interpret legacy policy data from documents, databases, and code, reducing SME dependency. Yet, data inconsistencies, outdated formats, and missing policy records can create roadblocks, requiring insurers to invest in standardization efforts before AI implementation.
• Smoother migration to target PAS: Infosys’ AI-powered transformation minimizes conversion risks and accelerates onboarding to new PAS platforms. However, legacy system complexities and multi-decade-old policy structures often require customized AI training and extensive human validation, making migration timelines longer than expected.
• Seamless ecosystem integration: Infosys embeds AI across underwriting, claims, and policy management through XtractEdge, while IPMS independently enhances policy migration by automating business rule extraction and data conversion. Yet, insurers must ensure regulatory compliance, maintain explainability in AI-driven decisions, and align AI adoption with overall business strategy, all of which remain key hurdles for enterprise-wide AI scalability.

Enterprise challenges: Bridging AI ambitions with practical implementation

While AI and policy migration solutions provide a structured path to modernization, successful execution requires insurers to tackle key challenges for long-term success. Clean, standardized data is critical for AI efficiency, yet many insurers face fragmented legacy records. Reducing SME dependency is valuable, but enterprises must retain business-critical knowledge to prevent operational gaps. AI adoption should align with broader transformation goals, ensuring seamless integration across underwriting, claims, and policy servicing. Moreover, regulatory compliance must remain a priority, as faster AI-driven policy migrations still require validation to meet market-specific rules and obligations. Without addressing these foundational issues, even the most advanced AI tools may struggle to deliver their full potential.

The Bottom Line: Infosys’ XtractEdge and IPMS offer insurers a pragmatic way to access their core policy data and speed up migration.

AI in insurance goes beyond automation. It requires strategic alignment with enterprise goals—whether that’s driving customer centricity, new product innovation, or winning in embedded insurance. While XtractEdge and Infosys’ Policy Migration Solution provide a structured path for modernization, success depends on how well insurers can chart a path for continued AI infusion after paying their technical and data debts.