Creating and testing competitive insurance pricing strategies has never been more significant as the customer behavior and experience is shifting towards a digital environment due to the COVID-19 pandemic. Insurance organizations need to constantly update their rating models in order to stay ahead of competition and maintain portfolio profitability. Any disruptive rating changes can negatively impact retention and cause a significant loss of revenue.

In this paper, we outline how TAZI’s Profitability and Rate Monitoring solution works. This solution is based on TAZI’s Continuous and Explainable, No-Code, Automated Machine Learning (AutoML) platform. We describe how continuous learning and dynamic customer segmentation helps discover profitable and loss-making micro-segments. We also describe how business units, such as state product managers, can monitor their rating models and take rating actions in order to maintain their loss ratio.

Please see the resources at the end of the paper to see how much you could save with a tool like this and how you could create a customized insurance profitability solution.

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