Success Stories / GenAI-Empowered Underwriting & Claim Processing

GenAI-Empowered Underwriting & Claim Processing

A premier financial-protection provider was hampered by manual document handling, underwriting, and claims review - processes that slowed policy issuance, introduced errors, and inflated operating costs.
AWSGenerative AIProcess Automation
10 min read
Calendar2025

Executive Summary

A premier financial-protection provider was hampered by manual document handling, underwriting, and claims review – processes that slowed policy issuance, introduced errors, and inflated operating costs. Partnering with Matoffo, the company adopted an AWS–based Generative AI platform that automatically ingests, classifies, and summarizes policy documents and claims, then delivers AI-driven risk scores and decision recommendations directly to underwriters. The rollout – completed on time and within budget – cut claims-review cycles from days to minutes, reduced manual touchpoints by more than half, and boosted data-entry accuracy into the high-ninety-percent range. As a result, the client established a new operational baseline that scales effortlessly with growth while delivering faster, more reliable service to policyholders worldwide.

Client Background

Serving diversified clients across the United States and the United Kingdom, our client is a leading financial protection provider with over 20 years of experience in the industry. The company delivers a vast selection of Disability, Life, Accident, and Critical Illness insurance products. Specializing in disability insurance, life insurance, accident insurance, critical illness insurance, and other supplemental health and employee benefits, the team has earned the trust within the insurance domain. While their vision revolves around the combination of cutting-edge technology and a forward-thinking approach, the company sets new standards in the industry, providing efficient claims processes to ensure timely support when it matters most.

Client's Feedback

5.0
Review verified

""Working with Matoffo has transformed our business. From day one, their team demonstrated a deep understanding of our challenges and delivered a feature-rich solution that has exceeded our expectations. We highly recommend Matoffo to any organization looking to modernize their workflow with AI-driven solutions. We look forward to continuing our partnership with them as we grow and evolve in the insurance industry.""

Head of Operations,

Customer Challenge

As the provider’s book of business grew, so did the operational stress on its document-heavy underwriting and claims processes. Manual extraction, validation, and re-keying created a domino effect of delays, costs, and customer dissatisfaction.

 

Key Business Challenges:

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Prolonged Quote-to-Bind Cycle:

Application turnaround stretched from 5 – 15 days, delaying premium recognition and giving competitors a window to win the deal.
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Escalating Operating Costs:

Manual data entry and reconciliation consumed 50 %+ of underwriting staff hours, driving up acquisition cost per policy.
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Error-Driven Revenue Leakage:

Mis-keyed figures led to premium misquotes and overpaid claims, eroding margins and triggering costly policy endorsements.
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Customer Experience Risk:

Slow, error-prone workflows diminished broker confidence and jeopardized renewal rates among high-value clients.

These business pressures threatened the company’s ability to deliver fast, accurate coverage while protecting profitability in an increasingly competitive market.

Goals and Requirements

In response to slow underwriting cycles, costly manual effort, and growing document volume, the client set clear, results-oriented targets. The aim was to create a faster, more accurate, and highly scalable operation – while maintaining strict compliance and auditability.

Performance Targets

  • Cut Turnaround Time:

    Reduce quote-to-bind and claims-review cycles from 5-15 days to < 4 hours through AI-driven document ingestion and decision support.

  • Boost Data Accuracy:

    Achieve > 95 % field-level extraction accuracy across all supported document types, minimizing rework and misquotes.

  • Accelerate Decisions:

    Deliver real-time risk scores and AI recommendations so underwriters can approve standard cases in minutes.

Financial Targets

  • Lower Operating Cost:

    Trim manual data-entry and reconciliation effort by at least 40 %, targeting a six-figure annual saving in staffing and error remediation.

  • Improve Loss Ratio:

    Reduce payout leakage from misclassified claims by automating rules and anomaly detection – contributing to a projected 2-point improvement in combined ratio.

Scalability & Reliability

  • Handle Volume Spikes:

     Design a pipeline that can process 10 000+ documents per day without performance degradation or new headcount.

  • Ensure Continuous Compliance:

    Embed regulatory rules (e.g., SOC 2, HIPAA) and audit trails directly in the workflow, maintaining 100 % traceability of data and model decisions.

  • Enable Cloud-Native Resilience:

    Achieve ≥ 99.9 % platform uptime with automated fail-over, backup, and recovery across AWS availability zones.

By meeting these objectives, the client expects to unlock rapid growth, elevate customer satisfaction, and future-proof operations against evolving compliance and volume demands.

The Solution

To resolve the client’s document-heavy underwriting and claims challenges, Matoffo implemented a structured, multi-phase approach – building an AI-powered automation platform on AWS that ensures scalability, operational resilience, and near real-time decision-making. The solution was designed to integrate with existing systems, streamline workflows, and significantly reduce manual overhead.

  1. 1

    Data Ingestion and Centralization

    The first step involved establishing secure and automated channels for ingesting unstructured and structured data from brokers and partners. This included formats such as scanned PDFs, Excel sheets, Word files, and email attachments. Using AWS Transfer and AWS Glue, incoming data was funneled to Amazon S3 and hosted through a scalable AWS ECS-powered portal, ensuring centralized, high-throughput intake and flexible mapping through JSON configuration.
  2. 2

    Intelligent Document Processing

    Next, the system applied advanced document parsing using Amazon SageMaker and AWS Lambda to classify submissions, extract relevant entities (e.g., policyholder names, claim dates, damage summaries), and prepare the data for semantic search. AWS Bedrock Knowledge Base was used to store and manage extracted insights, while Labeling Studio supported high-quality annotation during model training and evaluation.
  3. 3

    Workflow Automation and Decision Logic

    With structured data in place, Matoffo automated core underwriting and claims workflows using AWS Step Functions and a custom LLM decision-making pipeline. Integration with AWS Bedrock enabled LLM-driven summarization and classification, while an LLM Abstraction Layer allowed flexibility in routing, validation, and exception handling. Simple cases were auto-cleared, while complex scenarios were flagged for manual review.
  4. 4

    Integration with Core Business Systems

    The platform was integrated with the client’s ERP, CRM, and internal systems via AWS Step Functions, ensuring bidirectional data exchange and traceability. Final outcomes, policy decisions, and audit logs were stored in Amazon RDS, enabling consistent reporting and downstream processing without data loss or duplication.
  5. 5

    Monitoring, Feedback, and Optimization

    To ensure long-term reliability and performance, Matoffo deployed AWS CloudWatch for monitoring and Lambda-driven feedback loops to capture operational KPIs. Model outputs were regularly evaluated and refined using Amazon SageMaker, while semantic context and retrieval capabilities were enhanced through Pinecone for vector storage and memory indexing.

Results and Impact

The implementation of the AI-powered underwriting and claims automation platform delivered immediate, measurable improvements to the client’s operational efficiency and service delivery. By automating document intake, data extraction, and decision-making processes, the organization achieved major gains in speed, accuracy, and scalability – while reducing dependency on manual workflows.

Quantitative Outcomes

  • 78% of submissions processed automatically, reducing turnaround time from up to 15 days to just a few hours.

     

     

  • Underwriting decisions accelerated by over 80%, with many standard cases handled in near real time.

Qualitative Outcomes

  • Improved employee productivity, as underwriters now focus on high-value cases rather than repetitive data entry.

  • Enhanced decision-making accuracy, with AI-driven analysis and consolidated data, reducing misquotes and claim errors.

  • Scalable operations, allowing the platform to handle fluctuating submission volumes without additional staffing or infrastructure.

These results positioned the client to serve brokers and policyholders faster, more reliably, and with higher confidence – paving the way for expansion into new lines and markets.

Key Learnings

The success of this project stemmed from a deliberate alignment between business needs and technical execution. Every architectural and process decision was rooted in addressing specific inefficiencies within the client’s underwriting and claims workflows. The following elements were critical to the platform’s success and offer replicable best practices for similar transformation efforts:

  • Modular, Cloud-Native Architecture

    We chose a modular, service-based architecture on AWS to allow for rapid iteration and scalability across multiple document types and underwriting scenarios. This approach minimized deployment friction, enabled region-based scaling, and allowed individual pipeline components (e.g., ingestion, extraction, validation) to be updated independently without disrupting operations.

  • Strategic Use of AI and Automation

    The use of AI was not experimental – it was purpose-driven. Generative AI and OCR were integrated directly into the pipeline to solve a precise business problem: the slow, error-prone nature of manual data entry and decision-making. Embedding LLMs for summarization, risk scoring, and exception handling ensured that automation extended beyond simple field extraction and into actual underwriting logic.

  • Business-Driven Rule Design

    Automation rules were designed in partnership with underwriters and claims experts. This ensured that automated decisions adhered to internal guidelines and external regulatory requirements. The result was an engine that replicated human judgment with consistency – reducing exceptions while maintaining trust in AI-driven outcomes.

Next Steps

Following the successful deployment of the AI-powered underwriting and claims platform, the client is well-positioned to expand its capabilities, drive additional value, and unlock new business opportunities. The next phase of this transformation focuses on extending functionality, deepening automation, and broadening impact across the organization.

  1. 1

    Expand to New Product Lines

    The current platform is optimized for core underwriting and standard claims processing. The client now plans to extend AI workflows to additional lines of business - such as specialty insurance, commercial risk, or high-net-worth portfolios - where document complexity and decision timelines are even more critical.
  2. 2

    Introduce Continuous Learning Loops

    To maintain high accuracy and adaptability, the team will integrate a continuous feedback mechanism using underwriting and claims outcomes to refine LLM decision models and retrain extraction pipelines in Amazon SageMaker. This will reduce edge-case errors over time and boost confidence in AI judgment.
  3. 3

    Broker and Partner Self-Service Tools

    A roadmap is in place to expose document submission, policy tracking, and claim status features through a secure broker portal, leveraging the existing ECS-hosted service interface. This will reduce inbound service requests, improve partner transparency, and enhance client satisfaction.

Conclusion

The successful deployment of a Generative AI–powered underwriting and claims automation platform marked a pivotal moment in the client’s digital transformation journey. What began as a response to operational bottlenecks evolved into a scalable, cloud-native solution that delivers faster, more accurate decisions – with fewer manual touchpoints and greater transparency.

By eliminating document chaos, embedding business-aligned automation, and enabling real-time decision-making, the platform has redefined how policies are issued and claims are processed. Beyond efficiency gains, the solution has elevated customer experience, improved compliance readiness, and created a replicable framework for innovation across other business areas.

This transformation not only positions the client as a forward-thinking player in the insurance space but also lays the foundation for new products, partners, and market expansion – proving that when AI meets execution, operational excellence becomes a competitive advantage.

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