The explosive growth in data availability and increasing market competition are challenging insurance providers to provide better pricing to their customers. With 100s of millions of insurance records to analyze for downstream ML, Nationwide realized their legacy batch analysis process was slow and inaccurate, providing limited insight to predict the frequency and severity of claims. With Databricks, they have been able to employ deep learning models at scale to provide more accurate pricing predictions, resulting in more revenue from claims.
The key to providing accurate insurance pricing lies in leveraging information from insurance claims. However, data challenges were difficult as they had to analyze insurance records that were volatile as claims were infrequent and unpredictable — resulting in inaccurate pricing.
Nationwide leverages the Databricks Unified Analytics Platform to manage the entire analytics process from data ingestion to the deployment of deep learning models. The fully managed platform has simplified IT operations and unlocked new data-driven opportunities for their data science teams.
With the use of Databricks across data engineering and data science, Nationwide has seen significant improvements around data processing speeds and the ability to quickly train accurate models for their use cases.
With Databricks, we are able to train models against all our data more quickly, resulting in more accurate pricing predictions that have had a material impact on revenue.”
– Bryn Clark, Data Scientist, Nationwide
Technical Talk at Spark + AI Summit NA 2019