Optimizing FNOL workflows for a property and casualty insurance provider
- AWS
- Lambda
- S3
- DynamoDB
- Java
- Node.js
SERVICES
EXPERTISES
Project Context
Solution
Outcome
About Client
Our client is an insurance provider specializing in Property and Casualty Insurance for both businesses and individuals. Headquartered in the US, the company operates globally and offers comprehensive risk management solutions tailored to various industries.
Employing around 50,000 people and generating an annual turnover of $45-50 billion, the company leverages digital claims processing and automation technologies to enhance operational efficiency and customer service. Its First Notice of Loss (FNOL) system is critical in streamlining claims management, ensuring quick and accurate processing of loss data from various input channels.
As part of its ongoing commitment to innovation and scalability, the company sought to expand its development team with experts to accelerate enhancements to its FNOL and post-claim processing systems.
Business challenge
Our client faced growing challenges in managing Loss Data Capturing channels and their integration with the Claim Management System (CMS). Without seamless data flow:
- Claims processing slowed down, impacting response times and customer satisfaction.
- Operational challenges led to higher costs and hindered workflow effectiveness.
Additionally, service stability was a major concern:
- Any downtime in core claims services could result in financial and reputational risks.
- Ensuring scalability was essential to keep up with evolving industry demands.
The lack of dedicated resources led to:
- Delays in resolving production issues, creating operational bottlenecks.
- Limited ability to develop new services, preventing innovation and growth.
Recognizing the urgency, the company decided to expand its development team to maintain core system availability, minimize downtime, improve the integration between Loss Data Capturing channels and the CMS, reduce operational costs through system optimization, and develop new services based on business requirements.
Approach
Binariks assembled a dedicated team of 5 developers, 1 AQA, 1 BA, and 1 Architect, working alongside the client's Product Owner (PO) and 4 developers. Within a short timeframe, the team gained a deep understanding of the FNOL system architecture, ensuring alignment with the client’s core development team.
Process & execution:
- Identified key stages of the project and structured the approach accordingly
- Conducted additional clarification meetings to refine requirements and ensure alignment
- Established a problem framework, created a Work Breakdown Structure (WBS), and set up an efficient team workflow
By adapting to the project's scope and working closely with the client’s internal team, Binariks ensured a structured and efficient approach to system enhancement and ongoing development.
Implementation
The solution was implemented within strict client requirements, ensuring compatibility with existing services and secure data handling:
- Security: Encryption in-transit (HTTPS) and at rest (AWS KMS).
- Technology stack: Java (backend services) and Node.js (AWS-CDK for infrastructure).
- Cloud & DevOps: Deployed on AWS with Open Cloud Foundry (OCF), using OAuth2 authentication and IAM policies.
- CI/CD: Bamboo Pipelines, later migrated to GitHub Actions.
System Architecture & Key Components
A set of developed backend services must act as a middleware between Loss Data Capturing systems and CMS, ensuring structured claim registration and updates.
- FNOL system – Stores Claim Loss Data before registering claims.
- Claim registration service – Creates new claims in CMS.
- Claim search service – Allows searching claims by predefined parameters.
- Post-FNOL service – Updates claims after registration.
Infrastructure & Security Enhancements
- AWS-CDK (IaC): Enabled scalable and repeatable infrastructure provisioning.
- Network security: Private VPC with Security Groups, RBAC/ABAC IAM Policies.
- Authentication and secret management: OAuth2, Vault for credentials.
Key Improvements
- Extended CMS Web Services for new claim fields.
- Migrated CI/CD pipelines to GitHub Actions.
- Configured automated QA workflows.
- Integrated monitoring and logging: AWS CloudWatch, Datadog, Splunk.
The implementation ensured seamless claim processing, improved system stability, and compliance with industry standards.
Value Delivered
The implemented solution is now fully operational in production, seamlessly integrating Loss Data Capturing systems with the Claim Management System (CMS). It successfully addressed all key challenges while delivering additional business and technical benefits.
- Seamless system integration: The middleware solution eliminated integration barriers, ensuring smooth claim registration and updates.
- Operational efficiency: The client's core team can now focus on critical tasks, while automation has reduced manual claim handling costs.
- System stability: Core services remain available with minimal downtime, supporting uninterrupted claims processing.
- Scalability & future-proofing: The system is built on modern technologies, making it easier to maintain and scale, with a wider talent pool for future hiring.
Additional business benefits:
- Process improvement – The FNOL process for Auto Claims was enhanced by introducing a fuel identification step. This addition allows the system to route damaged vehicles to the correct type of repair service based on fuel type, ensuring faster and more accurate service allocation.
- Code quality & maintainability – Implementation of CI/CD quality gates, regular code reviews, and detailed documentation improved deployment reliability and accelerated onboarding for new team members.
- Expanded expertise – The project strengthened Binariks' expertise in private insurance, fostering further cooperation with the client.
By streamlining claims processing, optimizing costs, and ensuring system reliability, the solution not only met project objectives but also set the foundation for future enhancements and business growth.
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