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Claims Management

Streamlining the Claims Process with AI


For a consumer who has just been in an auto accident or experienced damage to their home, the processes of filing an insurance claim is often a make or break moment for the relationship with their insurance provider. The assessment and payment of the claim should be fast and accurate to ensure customer satisfaction and prevent issues like fraud. Traditional claims management processes are manual with analysts and rules-based systems making choices in claims processing which can slow down the process and make it opaque to consumers and result in a poor customer experience.


AI is ideal for automating repetitive processes and finding anomalous behavior that may indicate fraud or other issues. AI can streamline processing by scoring claims for issues like fraud and allowing claims with low probability of an issue to be processed automatically while higher probability claims are routed to investigators for review. AI models can also provide reason codes for claims denials, which streamlines the review process by allowing the analyst to quickly resolve those issues, so the claim can go through, or by showing the investigator the key issues to focus on. Reason codes are also helpful for customers because they can inform them of issues with their claim which can help them to fix the claim for reprocessing, approval and payment.


The mission at is to democratize AI for all so that more people across industries can use the power of AI to solve business and social challenges. The insurance industry is a key focus for the company with leading insurance companies including Progressive, TransAmerica, Aegon, Zurich Insurance helping to drive significant product innovation. H2O Driverless AI is an award-winning platform for automatic machine learning that empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models. Driverless AI includes innovative features of particular interest to insurance companies including machine learning interpretability (MLI), reason codes for individual predictions, and automatic time series modeling.

Related Case Studies

Conner Jensen
Analytics Program Director, Zurich Insurance

"The ability to do advanced analytics and do more work across the data is going to be the differentiator for insurance companies going forward. "

Pawan Divakarla
Data and Analytics Business Leader, Progressive

"With H2O we are able to build many models in a much shorter period of time."