Behind the Tech: How AI Powers Insurwave's Intelligent Data Ingestion

Our data ingestion product, Insurwave AI, tackles a fundamental challenge: transforming unlimited variability in unstructured data into predictable, structured documents that insurers can actually use. 

The key insight? AI should be strategic. Routine or low-complexity tasks can often be handled more efficiently with conventional logic. When using AI for these tasks, the resulting product could be found to be lacking or containing inaccuracies. Instead, we've designed an intelligent automation system that strategically deploys AI capabilities at specific stages where they add the most value, alongside an invaluable human-in-the-loop system that ensures a human can intervene. Checking the structured data the AI produces to review low confidence predictions before outputs are generated provides invaluable system feedback for the ongoing learning of models. 

This hybrid approach combines the speed and scalability of automation with AI's adaptability and sophistication, creating a system that's both powerful and practical, turning unfiltered data into actionable insights without the complexity of end-to-end AI processing.

Every AI model in our platform follows a rigorous development process: 

Problem Definition: We start by clearly identifying our prediction target and our problem statement. 

Framework Design: Target variables are identified, and a unique model framework is tailored to the specific challenge. 

Data Assessment: Client data is received and thoroughly evaluated for quality and relevance to desired outputs. 

Continuous Improvement: Models are actively monitored and updated to capture evolving patterns and maintain accuracy. 

Schedule of Values (SOV) documents exemplify the complexity our AI handles daily. These critical spreadsheets detail risk assets but come with significant processing challenges: 

Our AI-powered SOV extraction process tackles this systematically by identifying the relevant tabs, finding the headers, and dynamically mapping the important information to standardised codified outputs.

One of our most sophisticated AI challenges involves building occupancy classification. The objective: automatically categorise buildings into standard industry codes based on occupancy descriptions. 

The Challenge: Unlimited string variability (up to 100,000 different descriptions) combined with ambiguous language, contradictions, and inconsistent terminology. 

Why It Matters: Certain occupancies increase exposure, making accurate classification critical for pricing and risk decisions. Misinterpretations can lead to significant underwriting errors. 

Our Solution: We train AI models using large-scale data sets to enhance pattern recognition and semantic understanding. These models represent abstract concepts and contextual relationships in a structured form, enabling them to differentiate between nuanced inputs and improve classification performance as they mature, even in cases of ambiguity or inconsistent terminology.   

Once deployed, our AI models serve predictions in real-time through client-facing APIs. But deployment is just the beginning – maintaining high-quality outputs requires active monitoring to prevent performance degradation and model "staleness." 

Our production models are continuously retrained on the latest data to capture evolving patterns in real time — ensuring clients receive intelligent, forward-looking insights that reflect current market dynamics and emerging trends. 

What sets Insurwave apart in the specialty underwriting space? Service architecture that prioritises interpretability, control and precision. 

To effectively scale AI in Insurance, the industry must stop viewing models as a ‘silver bullet’ and rethink workflows with human input in mind. AI models do not flourish in isolation; instead, they require sufficient operational integration, human control and total interpretability. 

In specialty insurance where risk submissions are highly variable and rarely standardised, models are found to frequently underperform on edge cases, which requires human correction. Without investing in a workflow that accounts for underwriter oversight, correction and decision making, AI becomes a hindrance instead of generating progress.  

The balance between automation and human input must be integrated into the system from the start to provide teams with the ability to route cases for human review, correction or intelligent automation. The brilliance of our solution is found in the harmony of machine intelligence and human decision-making. Our human-centric design delivers true automation that is built for out-of-the-box readiness, promoting a smooth interaction between man and machine that optimises the underwriting workflow. 

Whilst early adopters have uncovered additional inefficiencies in producing standalone tools that ultimately create more work, Insurwave’s automation tool innovates the underwriting workflow through a seamless end-to-end service architecture. Insurwave’s suite of tools leverages HITL capabilities and solves automation complexities in-flight. This empowers our clients with an intelligent workflow, that seamlessly integrates into their software and leverages artificial intelligence performing in combination with human expertise.

As the insurance industry continues to embrace digital transformation, the ability to efficiently process and understand risk data is essential to gaining a competitive advantage. Our AI infrastructure doesn't just handle today's challenges – it's built to evolve with emerging patterns and new data sources. 

The future of specialty insurance lies in intelligent automation that enhances human expertise rather than replacing it. By handling the complex data processing behind the scenes, our AI empowers underwriters to focus on what they do best: making informed risk decisions.