AI HUB
From pioneering AI to practical use cases with measurable impact.
of customers prefer AI-powered, real-time services
higher customer retention when claims are AI-enabled
fewer false positives with AI-enabled fraud detection
The positive impacts of AI on turnaround times, operational costs, fraud prevention, customer and employee satisfaction have proven to be game-changing for our customers.
Instantly classify, sort, and index 500+ document types at claim intake, dramatically reducing the need for manual review, while improving data accuracy and throughput.
Extract structured data and key insights from unstructured documents (e.g., medical records, legal submissions, receipts), then use them to inform claim assessments and reduce manual data entry.
Analyze data patterns, compare submitted evidence, and flag claims suspected of fraud, waste, or abuse (FWA) with exceptional precision.
LLMs (Large Language Models) extract key data from large, unstructured policy documents (e.g., coverage limits, clauses, exclusions) and transform it into structured formats for automated, GenAI-enabled decision-making or further analysis.
AI-enabled workflows apply logic to trigger auto-approval or rejection, or flag complex cases for human review. Policy clause references explain real-time recommendations or decisions. Processing times are cut from weeks to minutes, and staff gain time for work that needs their care and attention.
Scan claims data for subrogation opportunities that would be undetectable with manual processes, thereby increasing recovery rates and lowering loss ratios.
Our models were trained on 7.5 million real insurance claims and policy documents, not synthetic data.
Our models, integration, and UI capabilities are built to deliver the scale and complexity you need.
We’ve pushed the limits of cutting-edge AI modeling from the outset. We are continuously evolving, so we can future-proof your business to be AI-enabled and customer-centric.
The vision and insurance experience of our leaders, meshed with the best and brightest AI engineers we hire, guarantees outstanding outcomes for our customers.
2019
AI research lab established with data science talent & insurance industry experts.
2021
First AI document understanding models proven in customer pilots in Japan.
Computer vision & synthetic data innovation leveraged to process 3 million documents, with 94% accuracy.
2022
Full-scale customer deployments underway.
Step change in capabilities, including policy assessment models leveraging NLI (Natural Language Interpretation) and NLU (Natural Language Understanding).
2023
Expansion into the UK, Europe & Latin America, with enterprise insurance customers.
Advanced LLMs (Large Language Models) integrated with existing models to drive automation in document processing and policy checking.
2024
North America market entry.
Generalized, end-to-end AI decisioning models added to auto-generate recommendations and next best steps.
2025
Growth in global deployments for insurers, service providers & MGAs (Managing General Agents).
Live Agent flows developed and deployed.
What is AI in insurance?
AI in insurance refers to the use of artificial intelligence to automate processes, improve decision-making and enhance customer experience across claims, underwriting and fraud detection.
It enables insurers to analyse large volumes of data more efficiently, reduce manual work and deliver faster, more consistent outcomes. These capabilities are increasingly delivered through modern insurance automation solutions designed specifically for insurance workflows.
How is AI used in claims handling?
AI is used in insurance across multiple functions, including claims processing, underwriting, fraud detection and customer service.
It helps automate repetitive tasks, analyse complex data and support decision-making, enabling insurers to operate more efficiently and at scale. Many of these capabilities are brought together within an insurance automation platform.
What are the main use cases of AI in insurance?
AI is commonly used in insurance for:
• claims processing automation
• fraud detection
• underwriting and risk assessment
• customer communication and support
These use cases help insurers improve operational efficiency and deliver better customer experiences across the insurance lifecycle.
What are the benefits of AI in insurance?
AI helps insurers reduce processing times, lower operational costs and improve decision accuracy.
It also enables better fraud detection, more consistent underwriting and improved customer experience through faster and more reliable service. These benefits are often achieved through integrated insurance automation platforms.
What are the challenges of implementing AI in insurance?
Key challenges include integrating AI with legacy systems, ensuring high-quality data, maintaining regulatory compliance and managing organisational change.
Insurers also need to balance automation with human oversight to ensure accuracy, transparency and trust in decision-making. Working with specialised insurance automation solutions can help reduce implementation complexity.
What is generative AI in insurance?
Generative AI in insurance refers to the use of large language models to generate content, summarise information and support decision-making.
It can be used to automate customer communications, assist claims handlers and analyse complex documentation more efficiently as part of broader AI adoption strategies.
Should insurers build or buy AI solutions?
AI is transforming insurance claims processing by automating document analysis, extracting data and identifying inconsistencies.
This allows insurers to process claims faster, improve accuracy and reduce manual workload, while enhancing the overall customer experience. These advancements are often implemented through modern insurance automation solutions.
