Why Data Curation is the Missing Link in Insurance AI (And How to Fix It)
In the race to implement artificial intelligence across the insurance industry, carriers have invested billions in sophisticated AI models, talented data science teams, and cutting-edge technology infrastructures. Yet many continue to see underwhelming results from their AI initiatives. The missing piece of this puzzle isn't more complex algorithms or bigger datasets - it's thoughtful, systematic data curation.
The Current State of Insurance AI
Insurance companies are drowning in data.
From policy applications and claims documents to customer interactions and third-party information sources, the modern insurer collects more information than ever before.
This data abundance should theoretically power transformative AI applications, but in practice, many insurers struggle to translate their data assets into business value.
Common AI implementation challenges include:
Models that perform well in development environments but fail in production
Difficulty scaling successful AI pilots across the organization
Inconsistent results between similar models in different business units
AI systems that require constant maintenance and retraining
These symptoms all point to a fundamental issue: the data feeding these AI systems hasn't been properly curated.
What is Data Curation and Why Does It Matter?
Data curation is the organised process of collecting, structuring, enriching, and managing data assets to maximize their value for analytical purposes.
It goes beyond basic data cleaning to create consistent, contextually rich, and properly governed information resources.
For insurance AI applications, proper curation addresses several critical requirements:
1. Data Quality: Ensuring information is accurate, complete, and consistently formatted
2. Contextual Richness: Preserving business context & domain-specific knowledge with the data
3. Appropriate Representation: Structuring data in ways that highlight patterns relevant to insurance processes
4. Bias Mitigation: Identifying and addressing potential sources of unfair bias in historical data
5. Compliance Readiness: Maintaining clear lineage and documentation for regulatory purposes
Where Insurance Companies Go Wrong
Most insurers already invest in data management, so why is curation specifically such a challenge? Several factors are at play:
Fragmented Responsibility
Data ownership is often scattered across IT, business units, and analytics teams, creating disconnects between those who understand the business context and those managing the technical infrastructure.
Skewed Incentives
AI projects are typically measured by speed-to-deployment rather than sustainable value creation, pushing teams to cut corners on data preparation.
Legacy Systems Complexity
Insurance data often resides in decades-old systems with inconsistent formats, undocumented business rules, and complex interdependencies.
Undervalued Expertise
Data curation requires a blend of technical skills and insurance domain knowledge - a combination that is both rare and frequently underappreciated.
How to Fix the Curation Gap
Addressing the data curation challenge requires a strategic approach:
1. Establish a Dedicated Curation Function
Create a specialized team responsible for transforming raw data into AI-ready assets. This group should combine:
Data engineers who understand technical requirements
Domain experts who can provide business context
Data scientists who understand model needs
Compliance specialists who ensure regulatory alignment
2. Implement Curation-Focused Processes
Formalize workflows for data assessment, enrichment, and validation:
Develop standards for documenting data lineage and transformations
Create quality scoring mechanisms to prioritize curation efforts
Establish feedback loops between AI users and curation teams
Build review processes that involve both technical and business stakeholders
3. Invest in Supporting Tools
Leverage technology specifically designed for insurance data curation:
Automated quality assessment and monitoring tools
Insurance-specific data dictionaries and ontologies e..g ACORD enabled
Metadata management systems
Version control for datasets and feature stores
Collaborative annotation platforms for domain experts
4. Shift the Measurement Focus
Update success metrics to emphasize sustainable value over short-term deployment:
Track model stability over time, not just initial accuracy
Measure reduction in model maintenance needs
Monitor consistency of results across similar use cases
Assess regulatory compliance readiness
Real-World Success Stories
Organizations that prioritise data curation are seeing tangible benefits:
A global property insurer reduced their claims processing AI models' error rates by 32% after implementing a structured curation process that standardized how property characteristics were represented across different geographical markets.
A regional health insurer cut model retraining frequency from monthly to quarterly after establishing a dedicated data curation team, saving over 2,000 data scientist hours annually.
A commercial specialty carrier successfully scaled their underwriting AI from one line of business to seven in just eight months by building a centralized, well-curated feature store representing common risk factors.
Getting Started
Begin your data curation journey with these practical steps:
1. Audit current state: Assess existing data assets, ownership structures, and quality issues
2. Start small: Choose one high-impact AI use case and implement proper curation practices
3. Document the benefits: Quantify improvements in model performance, maintenance requirements, and business outcomes
4. Expand gradually: Apply lessons learned to additional data domains and AI applications
5. Build institutional knowledge: Create training programs & career paths for curation specialists
Competitive Advantage in the Insurance Sector
As AI becomes increasingly central to insurance operations, the competitive advantage will shift from who has the most sophisticated algorithms to who has the most thoughtfully curated data assets.
By investing in proper data curation now, insurers can build a foundation for sustainable AI success that delivers real business value while minimizing regulatory and operational risks.
The future of insurance AI isn't just about bigger data or smarter algorithms - it's about better-curated information that truly represents the complex realities of insurance risks, customers, and operations.
