Project Overview

The client requested the advanced statistical analysis of their portfolio, to find out patterns, similarities and insights that may assist them in reducing the attritional loss ratio.
DataObrii thoroughly analyzed the portfolio of the company, retrieved data insights and gained a comprehensive understanding of the current situation. The team also utilized a deep autoencoder neural network to detect anomalies and provided a distinct analysis of outliers.

Our recommendations for reducing the attritional loss ratio were based on the results of tree ensemble models and the loss-cost modelling approach. In addition, the portfolio was divided into clusters, allowing the customer to realize all the patterns, similarities inside, and adjust the baseline strategy with new knowledge.
The customer received recommendations, supported by statistical significance tests and sensitivity analysis including different ways of treating large losses. The implemented solution helped him to get insights and knowledge on identifying weak profiles, leading to the creation of opportunities for reducing the attritional loss ratio by 3%.


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