Demand Model for Motor Third Party Liability Sales Data
Name
Kelli Kukk
Abstract
The aim of this Master thesis is to construct a demand model for motor third party liability sales data. The aim is to predict probability that a client buys insurance. The logistic regression model is the used statistical model. As a result, the insurance company gets information about certain types of policies (segments) which sale is better and which sale is worse. This information can be taken into account when pricing motor third party liability insurance.
Data is collected and processed in QlikSense software and demand model is created in AKUR8 software.
The work consists of four chapters, in the first chapter we describe the backround of insurance. In the second chapter, we give an overview of the methods used in the Master thesis. In the third chapter, we describe the data and provide an overview of the data collection process. In the fourth chapter, we present the compiled model and interpret the obtained results. Finally, some conclusions are made.
Data is collected and processed in QlikSense software and demand model is created in AKUR8 software.
The work consists of four chapters, in the first chapter we describe the backround of insurance. In the second chapter, we give an overview of the methods used in the Master thesis. In the third chapter, we describe the data and provide an overview of the data collection process. In the fourth chapter, we present the compiled model and interpret the obtained results. Finally, some conclusions are made.
Graduation Thesis language
Estonian
Graduation Thesis type
Master - Data Science
Supervisor(s)
Tõnu Kollo, Anu Hoop
Defence year
2022