Specialty Insurance Carrier
Our client, a specialty insurance carrier, wanted to tap into its vast data to capture competitive advantage and improve financial performance. We used our proprietary data monetization approach to deliver three new data science models with the potential for adding $20 million to EBITDA annually.
Profitability increase by decreasing average claim severity through ML-recommended inspections
annual risk avoidance through improved dealer onboarding
in new revenue by identifying and targeting new markets
To capture advantage in the competitive middle-market insurance sector, companies need to leverage every tool at their disposal. But there’s one that many still struggle to exploit: data.
Our client wasn’t taking full advantage of its considerable volume of data. Executives knew there were opportunities to use data to run more efficiently and compete more effectively—resulting in direct EBITDA impact. That’s where we came in, with data at the center of our ability to create financial value.
After previously implementing Databricks and Azure Container Apps, we had the foundation for a data science and MLOps program to achieve value in future use cases. Our multidisciplinary team consisted of insurance, product, commercial analytics, and data engineering/visualization experts who employed our proprietary data monetization framework.
We began with the big picture—generating hypotheses about data products for two business units. Our team used value identification analytics principles to test hypotheses with the client’s data and shared the potential financial impact with them. With the client’s confidence in the value creation story, we then built and implemented the models, integrating them seamlessly into the client’s technologies and processes.
Our client now has three customized predictive models that put insight in the hands of decision-makers at critical points—enabling them to drive performance, profitability, and advantage:
After implementation, we measured impact to ensure the models were creating the value expected. We set our client up for long term success by automating dashboard for ongoing monitoring and standing up a data organization to maintain them and create new models.
Once live, the three models delivered results translating to $20 million in potential annual EBITDA gains—through a combination of cost reduction, risk avoidance, and increased revenue. Looking closer:
This project didn’t just accelerate our client’s analytics and data science program; the initial results opened eyes in the organization about the possibilities for creating financial value.
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