Case Study · Healthcare & Insurance
AI-Powered Medical Underwriting: Automating Insurance Risk Assessment
Business outcomes
automated verification
Exceeding the client's target of 80%.
from kickoff to MVP
The core platform delivered against a demanding deadline.
in production and evolving
The solution is live and continuously expanding with new capabilities.
01 · Client
Meet Our Client
- Client
- Confidential
- Industry
- Healthcare, Insurance
- Market
- Europe
- Technology
- LLM, AI-native
- Delivery
- PoC – 1 week · MVP – 2 months · Live in production
02 · Challenge
Client's Challenge
Medical underwriting required the client to review health questionnaires, medical records, and product-specific insurance rules. Handling this work manually was time-consuming, made it difficult to apply risk assessment criteria consistently, and limited the company’s ability to scale.
The client needed custom medical underwriting software that could automate repetitive tasks and use AI to support medical document analysis. At the same time, the solution had to safeguard sensitive health data, integrate with existing workflows, and keep experienced underwriters in control of cases that required individual judgment.
03 · Solution
Our Solution
We designed and built a custom platform for medical underwriting automation. The system supports the entire insurance risk assessment workflow, from collecting health questionnaire data and medical records to analyzing the information, recommending an outcome, and enabling underwriter review.
The platform uses OCR and large language models to extract and organize information from medical documents. A configurable rules engine then evaluates the data against the criteria for each insurance product. Cases that require individual interpretation are automatically routed to an expert with a structured summary of the most relevant information.
The solution was designed around health data security, transparent decision-making, and integration with the client’s existing workflows. Its modular architecture makes it possible to expand the platform, introduce additional insurance products, and update underwriting rules without rebuilding the entire system.
04 · AI-native development
AI-native software development in practice
The project was delivered using an AI-native approach, with artificial intelligence supporting the entire software development process, from requirements analysis and code generation through testing, error detection, and change verification.
The project repository served as the central knowledge base for developers and AI tools. It contained information about the system architecture, coding standards, security, integrations, and application testing practices. This enabled AI to work with up-to-date business and technical context.
05 · Project delivery
How did AI help deliver the project?
- Accelerated requirements analysis and feature implementation.
- Supported code generation, refactoring, and verification.
- Analyzed test results and helped resolve defects.
- Enabled work in short, easy-to-review cycles.
- Used knowledge stored directly in the repository.
- Supported automated unit, integration, and end-to-end tests.
- Helped detect regressions, security issues, and integration errors.
- Streamlined the review process and preparation of changes for deployment.
Combining AI-assisted software development, automated testing, and quality controls allowed us to deliver the core version of the platform in around two months. Despite the rapid pace of development, the number of defects reaching production remained close to zero.
AI supported the technical team, but key decisions about architecture, security, business logic, and deployments remained in human hands.
06 · Benefits
Client's Benefits
- Faster application reviews through automated analysis of health questionnaires and medical records.
- More consistent underwriting decisions based on transparent, configurable business rules.
- Less time spent on repetitive tasks, allowing underwriters to focus on complex cases that require expert judgment.
- Faster launches of new insurance products through configurable questionnaires, eligibility criteria, and decision pathways.
- Full expert oversight of the process. AI supports risk assessment without replacing human judgment when interpretation is required.
- A secure, scalable foundation for continued digital transformation across healthcare and insurance workflows.
07 · Result measurement
How do we measure the 92% automated verification rate?
The result was measured in production and covers every application submitted by the first 200 customers. In 92% of cases, the system made the decision independently, without human involvement.
The remaining 8% were routed for additional review by an underwriter, doctor, or another expert. We continue to develop the solution to increase the automated verification rate while maintaining expert oversight.
OptFor.AI
AI solutions for medical and insurance organizations
OptFor.AI designs and builds custom AI solutions for insurers, healthcare organizations, and companies processing sensitive medical information. We combine intelligent document processing, configurable business rules, and human-in-the-loop workflows to automate complex decisions without turning them into an unexplainable black box.
Whether you are modernizing medical underwriting, automating insurance risk assessment, or introducing AI into a regulated healthcare process, we can help you move from concept and proof of concept to a secure, production-ready solution.
Case study · Last updated:
OptFor.AI
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