Insurance Claims Optimization with Intelligent Automation
An end-to-end view of the claims lifecycle identified where automation and process redesign could reduce friction.
InsuranceAI & automation
industry contextOperating challenge → Primus delivery → Client value
01 / Business challenge
The operating context
An established insurance company encountered several challenges in processing home and industrial insurance policies, primarily due to the complexities of navigating intricate clauses and verbiage. The diverse types of documents involved in each policy required thorough validation before claim processing, leading to extended processing times. The manual review process was error-prone, often resulting in delays and an increased workload for the adjusters. Moreover, ensuring consistency and compliance across different policy types while adhering to regulatory standards added another layer of complexity. To address these challenges, the company sought to streamline the process, aiming to reduce errors, speed up processing, and ensure compliance.
02 / What Primus delivered
A solution shaped around the client’s operating need.
Primus successfully developed and implemented a comprehensive solution that included LLM-agnostic information retrieval, enabling precise extraction of relevant data from policy documents in response to user queries. We implemented a citation mechanism to ensure that all information is traceable and reliable. The process also enhanced the system's adaptability to handle diverse policy structures and document types, ensuring that our solution could meet the client's diverse needs.
03 / Project detail
The work, methods and business value.
The sections below preserve the substantive detail from the original Primus case study in the new presentation.
01
Business Benefits
Efficiency Gains: Streamlining the navigation and processing of insurance policies results in faster and more efficient claim resolutions.
Increased Accuracy: Improved accuracy in retrieving policy details, making the claim processing process more reliable and consistent.
Error Reduction: Enhanced efficiency in handling complex policy documents, reducing the likelihood of errors, and ensuring compliance with regulatory standards.
Scalable Solution: Provides a scalable solution that can adapt to various policy types, ensuring long-term value and flexibility for the client.
02
Technologies Used
-.NET: For server-side processing and integration.
Python: For backend processing and integration tasks.
Open AI: To power language models for information retrieval.
React: For building interactive user interfaces.
Azure Bus Service: To handle messaging and service bus operations.
Adobe Extract API: For document extraction and data processing.
AI and machine learning algorithms for automated claims processing and policy navigation.
Natural Language Processing (NLP) to interpret and present complex policy information in an understandable format.
Cloud-based infrastructure for scalable and secure data management.
04 / Business value
How the work improved.
01Efficiency Gains: Streamlining the navigation and processing of insurance policies results in faster and more efficient claim resolutions.
02Increased Accuracy: Improved accuracy in retrieving policy details, making the claim processing process more reliable and consistent.
03Error Reduction: Enhanced efficiency in handling complex policy documents, reducing the likelihood of errors, and ensuring compliance with regulatory standards.
04Scalable Solution: Provides a scalable solution that can adapt to various policy types, ensuring long-term value and flexibility for the client.
05 / Technology & methods
Technology and delivery methods used in the work.
PythonOpen AIReactAzure Bus ServiceAdobe Extract APICloud-based infrastructure for scalable and secure data management..NET
About this caseEnterprise delivery context
The story covers the operating problem, Primus responsibility, implementation approach and resulting capability.
