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For insurers, inaccurate claim reserves can lead to staggering loss ratios and insolvency concerns. With funds being allocated to these reserves before there is any report of a potential loss, the margin for error can be catastrophic. According to McKinsey, there was approximately a $30 billion increase in loss costs in 2021.

The traditional approach to estimating these reserves is based on life tables and survival analysis–using data accumulated over the years among various insurers. Using an A.I. predictive analytic and machine learning approach, survival analysis can be incorporated and improved over time–using automated data collection and an intelligent rule base to outperform traditional approaches with a significant margin.


Claim managers are empowered by NEMESIS to analyze the optimal claims reserve for the customers. With the drag-and-drop feature of NEMESIS, no coding is required in the process. Claim adjusters can easily segment the customers and set the claim reserve based on customer data. With NEMESIS Insight and Case Management System, the managers are able to overview the effectiveness of the current setting, assigning claim adjusters to take immediate action without leaving the platform.


This case requires the use of claim historical data and customer data, including demographic data and behavioral data, such as credit history, claim type, insurer description, policy information, insured person description, claim amount, etc.

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