“Labelbox has become the foundation of our training data infrastructure. Our data science teams create high quality labeled training data with our internal domain experts as well as external labeling services, all inside Labelbox.”
Insurance companies collect an abundance of data: geospatial imagery, cell-phone photos of vehicle damage, documents of medical records. Labelbox partners with insurers from every industry to turn this data into state-of-the-art AI and usher in a new era of increased efficiency, customer satisfaction, and growth.
Computer vision models trained on geospatial data enable insurance companies to assess risks and value for any area or property without human inspection. Labelbox provides native support for geospatial data across all products, empowering teams to visualize raw data, annotate data, update and curate location data for spatial analysis, and more.Learn more
Building AI-powered claims processing and adjustment systems for insurance companies presents big challenges: creating the MLOps infrastructure, the time and costs of labeling data, and slow iteration cycles. Labelbox offers full support for all modalities used in claims adjustment AI projects for insurers, as well a suite of solutions to improve model performance and accelerate time to market.
Train document processing algorithms that can get through thousands of of insurance documents in minutes with fast and easy PDF labeling tools. Labelbox also makes it easy to create human-in-the-loop workflows to ensure that AI-driven results are accurate and appropriate decisions are made in real time.Learn more
Large language models are increasingly used by insurers to transform recommendations, chatbots, risk assessments, and more. Labelbox provides an intuitive, state-of-the-art text labeling editor to accelerate and optimize the development of NLP-based AI.Learn more
Whether your AI team needs data labelers experienced with insurance AI projects, operations setup, training, or just extra support, the Labelbox team is ready to help.Learn more
How Cape Analytics uses active learning to get to production AI faster
In edge case geographic areas, models can be confused by natural features and incorrectly tag objects (such as yard debris, trees, etc) which leads to inccurate predictions and low performant models. Collaborating on fixing these edge cases is a complex challenge which requires countless hours of coordination and communication between many stakeholders.
The Cape team ran an iterative active learning cycle within their model training pipeline, determining areas of low model confidence and then prioritizing those areas for additional labeling via Labelbox. This iterative cycle increased overall model accuracy in a targeted manner. From their experience, specifying where a model may be uncertain allowed for more rapid fixes which led to more performant model outcomes.
The aggregate time saved from utilizing Labelbox’s active learning workflows represented an estimated 30%+ increase in total time savings, as well as months of custom development work in engineering hours.
How a Fortune 500 creative software company improved the speed of their AI development by 50%
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