This service provides a Proof of Concept (PoC) for the GenAI Underwriting Assistant tailored to (Re)Insurers, with metrics measured on top- and bottom-line improvement targets.
Insurers face significant challenges in processing complex claims and underwriting requests, arising from highly manual operations, inconsistent data formats, and prolonged response times. Overcoming these challenges opens up exciting opportunities to reduce operational costs, eliminate processing backlogs, enhance client and broker satisfaction, and, ultimately, enhance retention and revenue.
EPAM's GenAI Underwriting Assistant (GUA) leverages Microsoft Azure Open AI, Azure AI Foundry, and other Azure services to automate and standardize data extraction and analysis from diverse submission inputs, including comprehensive contract wording analysis, summarization of lengthy submission documents, and intelligent digital ingestion to prepare cases for quotation. GUA also classifies client industrial activity using industry codes, empowering underwriters to focus on strategic tasks like risk conversion and enhanced client engagement.
- Halve submission processing time (request for quotation). Reduce submission input processing time to achieve significant FTE savings and faster client responses. This can translate to $400k in benefits per year for a mid-cap company.
- Centralize and standardize submissions data. Provide a single pane of glass view of extracted and calculated data from various documents and tables, standardizing outputs and offering clear alerts for missing information. This enables quicker case opening and reduces manual errors.
- Accelerate resolution of complex claims with improved underwriting processes. Empower underwriters to generate policies that clarify and mitigate risk more accurately, removing ambiguities that complicate claims processing.
- Boost underwriter satisfaction and retention. Automate repetitive data extraction and administrative tasks, enabling underwriters to focus on higher-value activities like risk analysis and improving response quality. This leads to increased job satisfaction and talent retention within your organization.
- Rapidly add and scale additional lines of business. Adapt to new product lines in less than a month with GUA, which is built for scalability and extensibility, ensuring continuous improvement of data extraction precision through a feedback loop.
- Reduce submission processing costs through intelligent data retrieval. Achieve low average processing costs of approximately thirty cents per submission using targeted Retrieval-Augmented Generation (RAG), which efficiently classifies documents and extracts data only from relevant sections.
Deliverables
- Running GUA in client’s cloud environment, configured for extracting key submission data on one line of business
- Measurement of accuracy in submission document processing, identification of tables (schedules of value, claims experience/loss runs), and on simple triage rules
- User (underwriter) adoption testing within specified line of business
- Automatically generated data extraction summary based on submission inputs
Activities
Project kickoff and product vision definition.
Usage of existing client infrastructure in Microsoft Azure PoC design.
GenAI workflows configuring and prompt engineering refinement.
Ground truth creation with client business area and precision testing.
Simple front-end changes.
Timing
3-6 weeks, customizable to your organization’s specific needs and targeted outcomes.
Pricing
We offer customized pricing based on your organization’s specific scope and requirements. Please contact us for a detailed proposal and quote.