| dc.description.abstract | Motor vehicle damage insurance is the most common type of insurance in the world and one that generates the largest
amount of loss for most insurance companies. In Kenya especially, the challenge faced by insurers is to balance the
growth of the motor vehicle insurance business by increasing the customer base while also maintaining the profitability
of this sector. It is crucial to identify the main causes of motor vehicle damage, its impact on revenue for insurers and
factors that contribute to high motor claims to enable more accurate estimates of risk versus premium paid. In recent
years the interest has increased in the use of information technology (IT) and statistical machine learning methods,
supported by increasing computing capabilities, data availability and the trend towards automation. Statistical regression
models have numerous applications in this regard. This paper explores applicability of new machine learning techniques
such as tree-boosted models to optimize the proposed premium of prospective policy holders. It proposes two machine
learning models for pricing motor vehicle damage insurance (decision trees and regression). The aim is to identify
sources of risks in motor vehicles and the variables for motor vehicle premium determination. Data from insurance
companies has been used, which is made up of the premium rates and compensations, and other variables such as age,
driver's experience, etc. Results will be used to advise the insurance companies on how to charge premiums dynamically. | en_US |
