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Ververica Platform Success Story:

Machine Learning-based models to quantify commercial fleet exposure at individual insured asset risk in real-time

  • Learn how moved to a Kubernetes-based, Apache Flink® infrastructure within weeks

  • Explore the architecture of the Machine Learning-based platform for dynamic risk assessment.

  • Discover how upgraded Apache Flink® jobs to production with Ververica Platform

About the Case Study uses Ververica Platform and Apache Flink to build a Machine Learning-based platform producing dynamic risk assessment models and real-time pricing for tomorrow’s insurance industry. deployed Ververica Platform with Apache Flink to harden production Flink jobs seamlessly and quickly.

They developed a Machine Learning-based platform that is Kubernetes-native and implements ML-based algorithms for calculating the risk of vehicles in real-time. The risk scores for each insured asset is calculated dynamically and then updates a premium variable part of the insurance policy in real-time.

This case study includes: 

  • The challenges faced with their previous technology stack and how Apache Flink helped in resolving them 

  • The results  achieved by deploying Ververica Platform and Apache Flink to production

  • A detailed overview of's experience using Ververica Platform

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