Project Overview


Our client required a Proof of Concept (PoC) model to be developed using available data. The aim was to create a predictive model that could estimate the probability of a breach for specific companies.

Additionally, the model needed to calculate potential losses and incorporate implied risk densities for companies within a given subgroup.


The challenge was to create a sophisticated predictive model that could accurately forecast the likelihood of a breach and potential financial losses. Furthermore, the inclusion of the Cramer-Lundberg actuarial model added complexity to the project.


DataObrii utilized a combination of advanced statistical and machine-learning techniques to address the client's requirements. We employed R and h2o for data analysis, while randomforest and xgBoost algorithms were utilized for predictive modelling. To estimate implied distributions and implement the Cramer-Lundberg actuarial model, specialized libraries were integrated into the solution.

The final deliverables included predictive models to estimate breach probability and potential losses, along with implied risk densities for the specified subgroup of companies.


Our comprehensive solution empowered the customer with the ability to assess breach probabilities and potential financial impacts accurately.

By providing detailed insights into implied risk densities and implementing the Cramer-Lundberg actuarial model, Data Breach Model improved their risk management processes.


R, h2o, randomforest, actuarial, libraries for estimating implied distributions, xgBoost, regressions.


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