The Industry Landscape
The Complexity of Underwriting Research
Insurance underwriters typically have longer turnaround times for building research reports due to manual workflows and multitude of data sources. Extended validation cycles and outdated records not only delay report delivery but can also skew risk assessments.
Increasingly, advanced analytics and AI-driven interventions, including the growing adoption of AI in underwriting, are re-shaping this process. They speed up information retrieval, verify data as it arrives and surface insights that were once hidden in disparate sources. By embracing these technologies within a smart framework combining real-time intelligence with human judgment, insurers can move from uncertainty and delay to swift, confident underwriting decisions.
The Client Challenge
Long Hours, Limited Coverage and Bias
The client recognized that its traditional report-building processes inhibited its ability to lead in a fast-moving market.
Operational inefficiency
Hours-long, manual research sessions sapped employee capacity and delayed critical risk insights, leaving the business a step behind emerging trends.
Cumbersome data management
The team struggled to unify data from numerous, often complex sources, creating gaps that undermined proactive risk management. Furthermore, the process relied on ad-hoc approaches that introduced unintended biases.
To sustain competitive advantage and strengthen capital efficiency, the client needed to transform its underwriting research into a streamlined function capable of supporting scalable underwriting automation while delivering accurate results at speed. This required more than technology; it called for a partner who could thoughtfully combine Al capabilities with domain expertise to craft a smarter, future-ready operating model.
The Solution
The Multi-agent, AI-powered SKENSE Research Assistant
Stepping in as an insurance analytics partner, WNS Analytics deployed a future-ready solution powered by our intelligent solution engineering capabilities and deep domain expertise. Leveraging SKENSE – our AI-driven data extraction and contextualization product – we implemented a sophisticated multi-agent architecture on Azure to transform the underwriting research process.
This award winning, cloud agnostic solution combined a planner agent with specialized execution agents to orchestrate goal-driven research tasks – practical application of agentic AI in insurance. A human-led oversight layer was integrated to guide, validate and refine AI outputs, ensuring each report met high standards of accuracy, relevance and domain-specific rigor.
SKENSE is an agentic AI-powered product that empowers organizations to tap into the potential of their unstructured data through accurate extraction and contextualization within domain-specific workflows.
Key elements of the solution:
Planner Agent for Structured Research
- The agent breaks complex underwriting queries into sequenced sub questions.
- It ensures end-to-end coverage and citation tracking.
Execution Agents for Data Extraction and Analysis
- Web-scraping agent retrieves real-time data from multiple online sources.
- Document analysis agent processes and validates PDF, HTML and text files.
- Research query agent creates research queries based on the topic of research for efficient web results.
- Summarization agent condenses findings into clear, unbiased narratives.
Citation and Validation Module
- This module embeds source references in every report.
- It applies Retrieval Augmented Generation (RAG) architecture with vector database lookup to improve factual accuracy.
User Interface and Feedback Loops
- This intuitive portal allows underwriters to submit queries and review draft reports.
- It allows continuous feedback integration for model refinement.
Key solution features
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Intelligent data extraction and contextualization
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AI agents capable of processing multiple document types
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Gen AI-powered report generation
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Cloud-agnostic and multi-hyperscaler deployment
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RAG architecture with vector database
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Secure, multi-agent orchestration
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Seamless integration with enterprise applications like Guidewire, Salesforce and Dropbox
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Adherence to organizational data policies
The Outcome
Accelerated Underwriting and Operational Efficiency
The deployment of this solution reduced cognitive overload for the underwriting teams and significantly accelerated decision making. By combining a step-wise, multi-agent AI architecture with Azure’s scalable infrastructure, our client has established a robust foundation for underwriting automation, delivering continuous gains in predictive precision, operational agility and sustainable growth.
Key benefits included:
percent reduction in report generation time, with:
percent reduction in research costs, owing to elimination of manual data collection
percent data relevance and accuracy
FAQs
1. How does AI improve underwriting research in insurance?
AI transforms underwriting research by automating data collection, analysis, and report generation. Tools like SKENSE use multi-agent architectures to extract insights from diverse sources, verify facts, and summarize findings, allowing underwriters to make faster, more accurate decisions. This reduces manual effort, minimizes bias, and improves operational efficiency.
2. What is an agentic AI research assistant and how does it work?
An agentic AI
research assistant, like SKENSE, uses multiple AI agents to perform structured tasks—breaking complex queries into sub-questions, retrieving real-time data, analyzing documents, and generating concise reports. Human oversight ensures accuracy and domain relevance, making the research process faster, smarter, and reliable for insurance underwriting.
3. What are the benefits of using AI-powered underwriting in real-world insurance workflows?
Real-world deployment of AI in underwriting can significantly reduce report generation time (by up to 85%), lower research costs, and integrate data from multiple sources with high accuracy. Insurers gain faster decision-making, better risk assessments, and improved operational agility while reducing manual workload for underwriters.
4. How does SKENSE ensure accuracy and minimize bias in underwriting research?
SKENSE uses a combination of Retrieval Augmented Generation (RAG), vector databases, and a citation module to ensure factual accuracy. Human-led oversight and feedback loops continuously refine AI outputs, minimizing errors and unintended bias, while providing clear, domain-specific insights in every research report.
5. How does WNS help insurers implement AI-powered underwriting solutions?
WNS combines deep insurance domain expertise with intelligent solution engineering to deploy AI-driven platforms like SKENSE. We design multi-agent architectures, integrate diverse data sources, and provide human-led oversight to ensure underwriters get fast, accurate, and actionable insights while maintaining compliance and operational efficiency.
6. What makes WNS a trusted partner for AI-driven insurance research?
WNS brings decades of experience in insurance analytics, along with advanced AI capabilities and cloud-agnostic deployment options. Our solutions are tailored to client workflows, combining agentic AI, predictive analytics, and secure data integration, helping insurers accelerate decision-making, reduce costs, and strengthen risk management.
7. How is Agentic AI transforming underwriting decision-making in insurance?
Agentic AI is transforming underwriting by automating complex research workflows, contextualizing data from multiple sources and generating intelligent insights in real time. Unlike traditional automation, Agentic AI can independently orchestrate tasks such as document analysis, risk assessment and report generation while maintaining human oversight. WNS enables insurers to accelerate underwriting decisions through AI-powered research assistants that improve speed, accuracy and operational efficiency.
8. Why are insurers adopting AI-powered underwriting research assistants?
Insurance underwriters often spend significant time manually gathering and validating information across fragmented systems and external data sources. AI-powered underwriting research assistants streamline this process by extracting, verifying and summarizing relevant insights automatically. WNS’ SKENSE platform helped reduce report generation time by up to 85% while improving research accuracy and lowering operational costs.
9. How does Agentic AI improve underwriting accuracy and risk assessment?
Agentic AI improves underwriting accuracy by integrating structured and unstructured data sources, validating insights through Retrieval Augmented Generation (RAG) frameworks and continuously refining outputs using feedback loops. Multi-agent AI architectures also reduce cognitive overload for underwriters and surface hidden risk indicators that may otherwise be overlooked. WNS combines AI, analytics and insurance domain expertise to help insurers strengthen underwriting precision and decision intelligence.
10. What operational challenges can AI-led underwriting automation solve for insurers?
AI-led underwriting automation helps insurers address challenges such as slow turnaround times, manual research workflows, fragmented data environments and inconsistent risk analysis. Intelligent orchestration frameworks accelerate submission review, improve operational scalability and enable underwriters to focus on strategic decision-making instead of repetitive administrative tasks. WNS enables insurers to modernize underwriting operations through scalable AI-powered ecosystems integrated with enterprise platforms such as Guidewire and Salesforce.
11. Why should insurers partner with WNS for Agentic AI-powered underwriting transformation?
WNS combines deep insurance domain expertise with advanced AI, analytics and intelligent automation capabilities to help insurers operationalize Agentic AI responsibly and at scale. Through platforms such as SKENSE, WNS delivers cloud-agnostic AI solutions that integrate real-time intelligence, multi-agent orchestration and human-in-the-loop governance to improve underwriting agility, reduce operational costs and drive smarter risk decisions across the insurance lifecycle.