Leading Insurer Cuts Claims Processing Time with Neo4j on AWS
According to the Coalition Against Insurance Fraud, an estimated $308.6 billion is lost to insurance fraud annually in the United States alone.
A major U.S. insurance provider, with over a century of experience protecting customers from risks, knew it needed to keep pace as fraudulent activities became more sophisticated. Claims adjusters within the firm lacked a single view of claim histories, related policies, and indicators of fraud required to make accurate claim decisions at scale. This fragmentation slowed claims processing and increased the risk of fraudulent payouts slipping through the cracks.
Traditional relational databases proved inadequate for handling the complex relationships between claims, policies, customers, and external data sources. The company needed a solution that could give claim agents a holistic view of each claim in real-time, identify fraud patterns, and scale to handle millions of claims within existing AWS infrastructure.
The stakes were high: failure to modernize the claims process would result in slower customer service and potential revenue loss due to undetected fraud. The firm recognized that overcoming these challenges was essential to maintaining its market position and fulfilling its promise to customers.
Building a Scalable Claims Graph with Neo4j on AWS
After evaluating various options, the insurance company chose Neo4j Enterprise Edition, deployed on Amazon Web Services (AWS), as the foundation for its next-generation claims processing system. This decision was driven by Neo4j’s ability to store complex relationships natively, perform real-time traversal of deep connections, scale horizontally on AWS EC2 instances, and integrate seamlessly with the AWS ecosystem.
The insurance company partnered with Neo4j and AWS to design and implement a cloud-native claims graph solution. At the core of the architecture are Neo4j clusters deployed across multiple AWS EC2 instances for high memory and compute power. Data ingestion pipelines use AWS Glue for ETL processes and AWS Lambda functions for real-time updates. Amazon SQS enables real-time data streaming into the graph, while integration with AWS SageMaker opens up possibilities for advanced machine learning and predictive analytics.
Above: Fortune 500 Insurance Company’s Neo4j AWS Architecture
The graph model was carefully designed to represent the full spectrum of claims-related entities and relationships. It encompasses claims and their various states, policies and coverage details, claimants and their relationships, service providers such as auto repair shops, and external data sources like weather data and traffic reports. As the project progressed, the team discovered additional benefits of the graph approach, including the ability to easily incorporate ISO claims data, which enriched their fraud detection capabilities with industry-wide patterns.
A key innovation in the implementation was the use of Neo4j Fabric. This feature makes it easy to query the data in the same DBMS or multiple DBMS using a single Cypher query. This allowed the company to create a unified view across multiple graph databases, including their customer graph and unstructured document storage. This approach eliminated the need for data duplication and enabled cross-domain analytics without compromising data governance.
Beyond Fraud Detection: Transforming Customer Experience and Operational Efficiency
The business’s Neo4j-based claims graph has dramatically streamlined its claims operations. Claims processing time has decreased significantly, as adjusters now have instant access to a complete claim context. The company can now identify complex fraud patterns that were previously undetectable with graph algorithms and machine learning.
The unified data model has eliminated the need for multiple system lookups, saving thousands of work hours and reducing errors. Real-time risk assessment capabilities have enabled dynamic policy pricing and more accurate risk evaluations, positioning the company at the forefront of data-driven insurance practices.
The solution’s scalability has been proven in production, currently handling nearly a terabyte of data. It is poised to scale further as the company expands its data sources and use cases, providing a future-proof foundation for the insurer’s data strategy.
Looking ahead, the insurance company is exploring new applications of Neo4j across its operations. Plans include integrating IoT data from connected vehicles and smart home devices to enable predictive maintenance and risk mitigation. The company is also expanding the use of graph-based machine learning models in SageMaker to improve underwriting accuracy and automate more aspects of the claims process.
By embracing graph technology on AWS, this insurer has positioned itself at the forefront of digital innovation in the insurance industry. The claims graph project has not only solved immediate operational challenges but has also opened up new possibilities for data-driven insights and customer services.
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