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Data Engineering Case Study

The life insurance industry has experienced significant growth in accelerated underwriting (AUW) programs over the last few years. As the underwriting process continues to provide less invasive accelerated pathways, insurers increasingly rely on self-disclosed information for key risk factors instead of observed measures under traditional underwriting, such as smoking status and build. Munich Re's North American Data Engineering team (DE) continues to explore ways to utilize machine learning models that leverage data science and data engineering to build cutting-edge tools and deliver transformative solutions. 

Recently, our team delivered a web-based, client-facing application that is now used in the daily underwriting workflow – allowing the client to receive case data and provide model scores through API requests. 

In this case study, we explore the many design decisions and technical expertise that made it possible to build and integrate a non-trivial underwriting solution within a short timeframe. This process is the software development and deployment cycle in action, customized to align with the evolving insurance industry and our client's specific needs.

Read the full Data Engineering Case Study on the Munich Re Life US website.

Munich Re’s North American Data Engineering team creates and maintains the infrastructure that our business partners use to integrate data science insights into pre-existing or novel workflows.

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Raghav Batra
Data Engineer
Integrated Analytics
Pragnya Srinivasan
Data Engineer
Integrated Analytics