Top 10 Open Source .NET Projects and Libraries for InsurTech in 2025

Discover the top 10 open-source .NET projects and libraries revolutionizing InsurTech in 2025. Boost efficiency with AI, streamline claims processing, and enhance customer experience.

The insurance industry is undergoing a profounddigital transformation, driven by evolving customer expectations, increasing data volumes, and the disruptive potential of Artificial Intelligence (AI). In this high-stakes environment, a 2025 survey of 120 insurance industry leaders revealed that78% of organizations plan to increase their tech budgets, with AI and big data analytics being the top priorities. For insurers, leveraging technology is no longer a competitive advantage but a fundamental requirement for survival and growth. However, building custom software from the ground up can be prohibitively expensive and slow.

This is where the power ofopen-source software becomes a strategic asset. Open-source .NET projects and libraries provide a robust foundation upon which insurers can build scalable, secure, and innovative solutions without reinventing the wheel. These community-driven tools offer transparency, flexibility, and cost-efficiency, allowing organizations to adapt quickly to market changes. The key to success lies in expertly integrating these building blocks into compliant, enterprise-grade systems—a core competency of a specialized  .NET FinTech and Insurance software development company.

This guide curates the most impactful open-source .NET projects and libraries that are empowering insurers to streamline operations, enhance customer experience, and harness the power of AI in 2025.

The InsurTech Landscape in 2025: Why Open Source .NET?

The insurance technology (InsurTech) sector is characterized by its unique challenges: stringent regulatory requirements, complex legacy systems, and processes that involve vast amounts of data and documentation. Open-source .NET solutions are particularly well-suited to address these challenges for several reasons:

• Security and Compliance: The .NET ecosystem provides built-in features for building secure applications, which is paramount for handling sensitive customer data in compliance with regulations like GDPR and various insurance industry standards.
• Performance and Scalability: .NET Core and the latest .NET 8 are designed for high-performance and scalable cloud-native applications, essential for handling peak loads during claims events or policy renewals.
• Enterprise-Ready: .NET is a mature, enterprise-grade framework with strong support for long-term stability, making it a safe choice for mission-critical insurance applications.
• AI Integration: The growing .NET AI ecosystem, including  ML.NET, allows developers to build and integrate intelligent features directly into insurance workflows.

According to industry analysis, insurers that are leaders in AI adoption have significantly outperformed their peers, creating6.1 times the Total Shareholder Return (TSR) of AI laggards over the past five years  . Open-source tools are the accelerants that can help more companies achieve this leader status.

Top 10 Open Source .NET Projects and Libraries for InsurTech

The following table provides a high-level overview of the top projects, categorized by their primary function within the insurance value chain.

1. nopCommerce: For Digital Distribution and Customer Portals

While primarily an e-commerce platform,nopCommerce is a powerful, open-source example of a scalable .NET application that can be adapted for insurance distribution. Its pluggable architecture allows for the creation of custom modules to sell insurance policies online, manage customer accounts, and provide self-service portals for claims tracking and document uploads  .

• InsurTech Application: Build a B2C or B2B insurance marketplace. Its support for multi-factor authentication and robust order management system can be tailored to handle policy purchases, renewals, and customer data securely.
• Why it's a Good Fit: Its proven, high-performance architecture handles high traffic and complex transactions, which is essential for marketing campaigns or during natural disasters when claim submissions spike.

2. Dotnet-WebAPI-Boilerplate: For Secure and Scalable Policy Administration

This boilerplate template provides a fantastic starting point for building the backend of any modern InsurTech application. It comes with built-inmulti-tenancy support, which is ideal for software vendors building solutions that serve multiple insurance carriers or agencies from a single instance.

• InsurTech Application: Developing a modern Policy Administration System (PAS) or a claims management platform. The built-in best practices for security and API design ensure a solid and maintainable foundation.
• Why it's a Good Fit: It incorporates essential enterprise features out-of-the-box, saving hundreds of hours of development time and ensuring adherence to security and architectural standards.

3. CleanArchitecture Template: For Complex Underwriting Engines

Based onClean Architecture principles and  Domain-Driven Design (DDD), this project template is ideal for building the complex business logic found in insurance underwriting and pricing engines. It enforces a separation of concerns, making the system easier to test, maintain, and evolve over time.

• InsurTech Application: Creating a sophisticated underwriting engine that incorporates risk rules from various sources, including AI models. The architecture keeps the core business logic independent of external dependencies like databases or UIs.
• Why it's a Good Fit: Underwriting rules are a core asset for an insurer. This architecture protects that domain logic, making it easier to modify rules without impacting the entire system.

4. Orchard Core: For Dynamic Content and Broker Portals

Orchard Core is a modular, open-source Content Management System (CMS) built on  ASP.NET Core. It is perfectly suited for creating dynamic websites, broker portals, and knowledge bases that require frequent content updates without developer intervention  .

• InsurTech Application: Powering a broker portal where agents can access marketing materials, product documentation, and sales tools. It can also be used for the public-facing website to manage blog content about insurance tips and company news.
• Why it's a Good Fit: Its modularity allows developers to create custom "content types" for insurance products or claims procedures, providing great flexibility.

5.ML.NET: For AI-Powered Risk and Claims Automation

ML.NET is a free, open-source, and cross-platform machine learning framework for the .NET ecosystem. It enables .NET developers to integrate custom machine learning into their applications without needing expertise in Python or R.

• InsurTech Applications:
  • Fraud Detection: Train models to identify suspicious claim patterns based on historical data.
  • Claims Triage: Automatically categorize and prioritize incoming claims based on complexity and estimated cost.
  • Risk Prediction: Enhance underwriting accuracy by analyzing a wider set of data points to predict risk more precisely.
• Why it's a Good Fit: It allows insurers to build and operationalize AI models directly within their existing .NET applications, simplifying the IT landscape and leveraging in-house .NET talent.

6. Microsoft Semantic Kernel: For Generative AI Workflows

Semantic Kernel (SK) is an open-source SDK that simplifies the integration of Large Language Models (LLMs) with conventional programming languages like C#. It is the key to building "agentic" AI systems that can automate complex, multi-step workflows  .

• InsurTech Applications:
  • Document Analysis: An AI agent that can read and summarize lengthy medical reports or auto repair estimates for claims adjusters.
  • Customer Service: Powering advanced chatbots that can handle complex queries about policy details or guide a customer through the first notice of loss (FNOL) process.
  • Process Orchestration: Automating the entire customer onboarding process by coordinating multiple AI agents for data intake, risk profiling, and compliance checks  .
• Why it's a Good Fit: It provides the scaffolding to build the next generation of AI-driven insurance applications, moving beyond simple chatbots to fully automated, intelligent systems.

7. QuestPDF: For High-Fidelity Document Generation

QuestPDF is a modern, open-source library for generating PDF documents in .NET. It uses a declarative approach inspired by web layout engines, making it easier to create complex, data-driven documents like insurance policies, claims forms, and benefit statements.

• InsurTech Application: Generating pixel-perfect policy documents and correspondence letters. Its code-driven approach is more reliable and easier to version-control than traditional template-based tools.
• Why it's a Good Fit: It offers high performance and flexibility, which is crucial for generating legally binding documents that must be 100% accurate.

8. OAuth 2.0 / OpenID Connect inASP.NET Core: For Secure Identity and Access Management

The built-in support for OAuth 2.0 and OpenID Connect inASP.NET Core is a critical "library" of functionality for any InsurTech application. It provides a standardized, secure way to handle authentication for customers, agents, and employees.

• InsurTech Application: Securing customer portals, broker applications, and internal admin tools. It allows for secure single-sign-on (SSO) and integration with enterprise identity providers.
• Why it's a Good Fit: Security is non-negotiable. Using these well-established, built-in standards is the most secure and efficient way to manage identity in a .NET application.

9. Polly: For Building Resilient Integrations

Polly is a .NET resilience and transient-fault-handling library. It allows developers to express policies such as Retry, Circuit Breaker, Timeout, and Fallback in a fluent and thread-safe manner.

• InsurTech Application: Making reliable calls to external services, such as credit scoring agencies, vehicle databases, or weather data APIs. If an external service fails, Polly can retry the call or provide a fallback mechanism, preventing the entire insurance process from failing.
• Why it's a Good Fit: Insurance applications rely on many external data sources. Polly ensures that the application remains stable and responsive even when these external dependencies are experiencing issues.

10. Serilog: For Comprehensive Logging and Audit Trails

Serilog is a popular diagnostic logging library for .NET. It is designed to be easy to set up and use, while providing powerful structured logging capabilities that are essential for debugging, monitoring, and compliance auditing.

• InsurTech Application: Creating detailed audit trails for policy changes, claim adjustments, and user logins. The structured logs can be easily exported to analysis tools like Seq or Elasticsearch for monitoring and security analysis.
• Why it's a Good Fit: For regulatory compliance, insurers must be able to trace every action taken on a policy or claim. Serilog's structured logging makes this traceability robust and searchable.

Implementation Strategy and Best Practices

Adopting these tools requires a thoughtful approach to avoid common pitfalls.

• Start with a Pilot Project: Begin with a well-defined, high-value use case. For example, use  ML.NET to build a pilot for automating the triage of low-complexity auto glass claims . This limits risk and allows you to demonstrate value quickly.
• Prioritize Data Security and Compliance: From day one, design your architecture with security in mind. Utilize the security features of  ASP.NET Core and ensure that any AI model, especially in claims determination, is transparent and fair to avoid potential ethical issues and legal challenges .
• Focus on Integration: The real power comes from combining these projects. For instance, you could use:
  • Orchard Core for a broker portal.
  • Dotnet-WebAPI-Boilerplate to build secure APIs for policy management.
  • ML.NET to power risk recommendation engines within those APIs.
  • QuestPDF to generate the final policy documents.
• Plan for Change Management: As McKinsey highlights,  for every dollar spent on developing AI solutions, plan to spend at least another dollar on user adoption and change management. This is critical for ensuring that new tools are actually used effectively by underwriters, claims adjusters, and agents.

Conclusion: Building the Future of Insurance with Open Source .NET

The open-source .NET ecosystem offers InsurTech companies an unparalleled toolkit for innovation. The projects and libraries listed here provide a foundation for building more efficient, intelligent, and customer-centric insurance applications. From distributing policies via adaptable e-commerce platforms to automating complex claims processes with AI, these tools are helping to redefine the industry.

The journey involves more than just technology; it requires a strategic partnership with experts who understand both the technical landscape and the unique demands of the insurance sector. By leveraging these open-source solutions and adhering to best practices, insurers can not only reduce costs and improve efficiency but also unlock new opportunities for growth and create a significant competitive advantage in the dynamic market of 2025 and beyond.

Ready to leverage open-source .NET for your InsurTech initiatives? Contact our FinTech and Insurance development team for a complimentary consultation on selecting and implementing the right solutions for your challenges.

Frequently Asked Questions (FAQ)

1. Why should an InsurTech company choose .NET over other technologies?

The .NET platform, particularlyASP.NET Core, is engineered for building secure, high-performance, and scalable enterprise applications. Its strong typing, extensive built-in security features, and long-term support from Microsoft make it an ideal choice for the highly regulated and data-sensitive insurance industry. The growing integration of AI through ML.NET further strengthens its position.

2. Are these open-source projects secure enough for handling sensitive insurance data?

Yes, when implemented correctly. The open-source nature allows for community scrutiny, which often leads to faster identification and patching of vulnerabilities compared to proprietary software. However, security is a shared responsibility. It is crucial to follow security best practices, keep dependencies updated, and conduct regular security audits. Professional implementation is highly recommended to ensure the system is hardened against threats.

3. How can AI, specificallyML.NET, be used in claims processing?

ML.NET can be applied to several areas of claims processing:

• Automated Triage: Classifying incoming claims by complexity and routing them to the appropriate handler.

• Fraud Detection: Identifying patterns indicative of fraudulent activity by analyzing claims data against historical models.
• Estimates: Predicting the likely cost of a claim based on factors like the type of incident, involved parties, and historical similar claims, helping with reserve setting.

4. What is the biggest challenge when implementing AI in insurance?

A key challenge isdata quality and availability. AI models are only as good as the data they are trained on. Many insurers have data siloed in legacy systems. Other challenges include ensuringmodel transparency and fairness to avoid biased outcomes, and managing  cultural resistance within the organization to AI-driven decision-making  .

5. Can these tools be integrated with existing legacy systems in an insurance company?

Absolutely. A common strategy is to use a gradual modernization approach. APIs built withDotnet-WebAPI-Boilerplate can act as a bridge between new .NET microservices and legacy systems. This "strangler fig" pattern allows you to incrementally replace functionality without a risky, big-bang migration.

6. Is there a risk of AI replacing jobs in the insurance industry?

AI is more likely toaugment jobs than replace them entirely in the near term. It will automate repetitive, data-intensive tasks (like data entry from documents), freeing up human experts—such as underwriters and claims adjusters—to focus on complex cases, customer interaction, and strategic decision-making. However, the workforce will need to adapt and develop new skills to work alongside AI tools