University of Twente Student Theses
Disability insurance : predicting accurate inflow probabilities in an imbalanced data setting
Speldekamp, T.J. (2018) Disability insurance : predicting accurate inflow probabilities in an imbalanced data setting.
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| Abstract: | A core business of insurance companies is to set a premium for the policy holders. To set an accurate premium, one needs to model the probability of the insured to produce a claim. In the disability insurance branch for high income employees in the Netherlands, this poses a problem due to the imbalance regarding claims and non-claims. The current pricing model applies two features and is a relatively simple model. In this research we use more advanced machine learning techniques to predict the probability of a disability with more accuracy. We add external and internal data to improve the predictions. We conclude that using a gradient boosting model improves the ability to seperate claim from non-claim data while retaining the accuracy of the probabilities. Furthermore, the addition of features such as geographical data can help increase the performance of machine learning models. |
| Item Type: | Essay (Master) |
| Faculty: | BMS: Behavioural, Management and Social Sciences |
| Subject: | 30 exact sciences in general, 31 mathematics, 54 computer science |
| Programme: | Industrial Engineering and Management MSc (60029) |
| Link to this item: | https://purl.utwente.nl/essays/76768 |
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