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.
CHALLENGE
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.
SOLUTION
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.
RESULTS & ADVANTAGES
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.
