Insurance claims processing has long required a lot of tedious and expensive human labor, but artificial intelligence (AI) can help. From onboarding new customers to analyzing pictures or videos of damages for evaluation, machine learning (ML) and AI offer exciting possibilities for optimization and cost-saving across the insurance industry.
Traditional claims processing is manual, labor-intensive, and prone to human error. Claims adjusters pour hours into reviewing claims documents, verifying information, coordinating with customers, and making decisions about payments. Human agents can take weeks or months to process claims, undermining customer satisfaction and costing insurance companies wasted resources and potential attrition.
AI can expedite tasks like data entry, document review, trend forecasting, and fraud detection. This can increase throughput, reduce repetitive manual tasks, enhance customer satisfaction, improve volume forecasting, and significantly reduce time and labor costs.
AI reduces time and labor in claims processing
Insurance enterprises can leverage AI to solve a number of problems in claims processing, including document review, fraud detection, damage assessment, and customer support.
AI can power:
- Chatbots that can handle certain customer interactions and learn how to provide more accurate and useful responses over time.
- Quick analysis of relevant documents and incident images or videos to accelerate claim verification and compensation.
- Pattern detection of potentially fraudulent behavior, taking aim at an estimated $308.6 billion in annual loss. Models can also help predict claim volume, allowing insurers to make better decisions regarding premiums, payouts, and staffing requirements.
AI requires sufficient training data to accurately address domain-specific problems. Claims data is often noisy, unstructured, and multi-modal. Manually aligning and labeling this data is laborious and expensive, but—without high-quality representative training data—models are likely to make errors and produce inaccurate results.
Insurance enters the foundation model era
These massive models, with millions or billions of parameters, can:
- Quickly sort and label unstructured data through prompt-based labeling approaches.
- Accelerate development by providing the basis for custom AI applications.
- Distill generative power into efficient, deployable models.
Insurance companies can face a number of challenges when adopting FMs. FMs require immense computational resources and can be expensive either to maintain or to use via an open-access third party like OpenAI. FMs can also have low explainability, making them hard to understand, adjust, or improve.
The Snorkel advantage for claims processing
Snorkel offers a data-centric AI framework that insurance providers can use to generate high-quality training data for ML models and create custom models to streamline claims processing.
- Programmatic labeling to quickly generate high-quality training sets from unstructured data.
- Integrations with FMs and LLMs to address data challenges and springboard development for domain-specific models. Models like GPT-3, Stable Diffusion, BERT, and CLIP can be pre-trained and fine-tuned to address problems specific to claims processing.
- Enhanced explainability so users can understand why models make certain predictions, optimize those models, and update schemas to changing priorities and conditions.
Insurance companies can distill customized FMs into deployable mini-models to automate workflows, process multi-format evidence, make predictions, and create new efficiencies in claims processing. They can deploy these lightweight custom AI applications on-premises or in the cloud, enjoying enterprise-grade security in Snorkel’s SOC2-certified secure cloud or with leading cloud providers like AWS, Microsoft Azure, and Google Cloud.
Accessible AI for claims processing with Snorkel AI
AI can significantly lessen the time and labor costs involved in insurance claims processing. By automating many of the tasks involved in claims processing, AI can free up human employees to focus on more value-added tasks and reduce costs, sharpen efficiency, and improve customer service.
If you'd like to learn how the Snorkel AI team can help you develop high-quality LLMs or deliver value to your organization from generative AI, contact us to get started. See what Snorkel can do to accelerate your data science and machine learning teams. Book a demo today.