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Publicação
IBNR techniques in health insurance: a machine learning approach
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 1.68 MB | Adobe PDF |
Resumo(s)
Loss reserves are typically one of the largest liabilities on an insurer’s balance sheet
since they can have a significant impact on profits as well as the insurer’s solvency.
The Chain Ladder model is an outstanding actuarial reserving technique that has been
applied over the years to estimate Incurred But Not Reported claims.
This project aims to provide the most accurate estimates possible for the calculation
and prediction of reserve claim amounts in the context of corporate health insurance.
For this, the Chain Ladder approach is compared with machine learning algorithms
such as the Support Vector Machine (SVM), the Random Forest (RF), the Extreme
Gradient Boosting (XGBoost) and Neural Networks (NN).
Descrição
Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
Palavras-chave
IBNR Health Insurance Chain Ladder Machine Learning Predicting Claims