Reimagining Insurance with Generative AI
The ultimate guide for business leaders — from $50B-$70B in projected industry value to the use cases, models, and adoption journey that unlock it.
Download the full guideGenerative AI in insurance: from research to revenue
Generative AI has evolved from research concept to significant business driver, creating unique outputs through human commands. Organizations across banking, healthcare, and manufacturing are investing in GenAI for enhanced customer interactions, operational efficiency, and innovation. Insurance industry leaders like Nationwide and HDI Global are already implementing GenAI solutions for customer service and claims processing.
McKinsey Global Institute estimates the GenAI opportunity for insurance:
Adoption enables complementary solutions including hyperpersonalized customer service, intelligent document processing, and custom knowledge agents. GenAI represents a strategic opportunity to remain competitive beyond traditional AI capabilities.
General considerations for generative AI in insurance
Insurance organizations possess diverse, high-quality customer and business data for developing competitive GenAI solutions.
GenAI complements rather than replaces traditional AI for risk assessment, fraud detection, and claims processing.
Adoption progresses from individual text creation to operational augmentation to core process transformation.
While development costs remain relevant, operational expenses align with traditional AI systems; some use cases prove more cost-efficient.
Prioritize data security, privacy, system availability, operational continuity, interoperability, auditability, and regulatory compliance. Cloud provider enforcement of security measures ensures default safety.
Requires diverse training data, ethical guidelines, transparency, continuous monitoring, and mechanisms like Reinforcement Learning with Human Feedback (RLHF), Explainable AI (XAI), and Human-in-the-Loop (HITL).
Overview of generative AI use cases in insurance
Short-term impact (horizontal use cases)
Organized by broad business functions, horizontal use cases enable dialogue generation for virtual assistants, automated code generation, and personalized messaging.
Long-term impact (vertical use cases)
Sector-specific applications create sustainable, measurable value through domain knowledge and fine-tuned foundation models.
Both horizontal and vertical use cases realize scale value when seamlessly integrated into systems, processes, and teams. Organizations must balance GenAI value extraction against managed risks including hallucinations, biases, phishing, and prompt injections.
Choosing the right foundation model for insurance
Foundation model selection requires balancing scale and specificity. Multiple providers exist (AWS, Anthropic, Cohere, Meta, Stability AI, AI21 Labs), and effectiveness depends on fine-tuning to specific tasks rather than inherent insurance focus. Infrastructure capabilities, cost, security, and performance are critical factors.
- 1. Assess
Assess data and infrastructure readiness for generative AI workloads.
- 2. Discover
Discover use cases with the highest ROI for your business.
- 3. Build
Build a prototype to determine production business value.
Factors for selection
GenAI actions to complete:
- Text generation
- Text summarization
- Text translation
- Text classification
- Information retrieval
- Question answering
- Sentiment analysis
- Vector search
Large models provide robust performance and superior reasoning at increased cost; smaller, specialized models are more affordable but challenging to train, maintain, and scale.
- Supporting architecture
- Training data (volume, variety, quality)
- Optimization technique (e.g., quantization)
- Transformer efficiencies
- Choice of learning frameworks
- Model compression techniques
- Cost of inference (run-time)
- Environmental impact
Organizations should evaluate immediate tasks and organization-wide scalability potential.
- Data residency requirements
- Estimated traffic volume
- Talent: upskilling or recruitment
- Costs: minimal up-front vs. significant future spend
- Operations: in-house vs. managed AI services
Streamlining insurance underwriting with generative AI
Client profile. An international specialty insurance company combining legacy-free operations with digital risk management solutions.
Challenge. Subject matter experts previously spent days reviewing 100-page technical survey reports. Manual underwriting was labor-intensive with limited processing capacity.
Solution. Provectus developed a GenAI solution using Cohere's Command Large Language Model hosted on Amazon Bedrock. In-context learning was adjusted for summarization and extraction tasks specific to engineering reports in PDF format.
Generative AI adoption journey with Provectus and Cohere
A two-phase approach designed for cross-functional collaboration:
Data readiness and use case prioritization workshop uniting IT and business teams for highest-return identification.
Prototype development in your organization's AWS account to test use case value.
What you receive at every stage
- Data and infrastructure discovery
- Prototype of selected use case on AWS
- GenAI use case prioritization and adoption roadmap
- Solution architecture documentation
- Expected business value and success criteria research
- Detailed production deployment plan
- Use case complexity and risk estimation
- TCO estimates for AWS and FM services
Why Provectus
GenAI transforms insurance rapidly with $50B-$70B in annual adoption value potential. Adoption requires comprehensive organizational change beginning with the first use case. Provectus offers deep AI expertise, end-to-end transformation implementation, and pre-built solutions enabling risk-free prototypes, ready to go live within weeks.
AWS provides funding support for banking, finance, and insurance organizations. Provectus launched "The AI Landing Zone" accelerator for enterprise GenAI adoption while managing risks.
- AWS Premier Tier Services Partner
- AWS Machine Learning Competency
- AWS Data & Analytics Competency
- AWS DevOps Competency
- AWS Migration Competency
- AWS Generative AI Competency