Topics in Non-Life Insurance (Skade2)

Knowledge:

• Thorough understanding of distributions in non-life insurance.
• Thorough understand of maximum likelihood methods in non-life insurance
• Basic understanding of the specificities of non-life regression models, and their practical ramifications.
• Thorough understanding mod model selection methods (Prediction based and goodness-of-fit based).
• Familiarity with insurance data analysis and machine learning techniques, including implementation in R.

Skills: 

Proficiency in estimating and predicting risks, even when dealing with heavy-tailed data, censoring, truncation, covariates; utilizing both parametric and non-parametric methodologies.

Competences:

• Capacity to discern the appropriate technique for specific scenarios, as well as having an understanding of the strengths and limitations inherent in different modeling approaches.
• Effective communication of model outputs and diagnostic findings, both in technical and practical terms.