There are two themes in this dissertation, that is, the effect of universal demand law change on corporate risk-taking in the U.S. property & casualty insurance industry, and the application of hidden Markov model to auto telematics data. The first chapter presents my study in the first theme and the rest two chapters present the other theme. In Chapter 1, "Does Shareholder Litigation Affect Corporate Risk-Taking? Evidence from the Property-Casualty Insurance Industry", I explore whether shareholder litigation affects corporate risk-taking differently depending on distinct organizational structures. I use a law change, called Universal Demand (UD) Law, as an exogenous shock and develop three risk-taking measures that are unique in the U.S. property-casualty insurance industry: leverage risk, asset risk, and underwriting risk. The insurance industry provides an interesting opportunity for the study as shareholders in mutual insurers are an ambiguous concept in the legal world, as opposed to the common argument in the insurance literature. The results show that along with UD law adoption, insurers increase their risk-taking. After taking organizational structures into account, the impact of the law change differentiates. Stock insurers increase all three risk-taking measures while mutual insurers decrease their Leverage Risk and increase Asset Risk measures. For different time windows, stock insurers respond faster with respect to their Asset Risk compared to mutual insurers. In addition, I proceed to examine the main economic channel for the impact and find that the free cash flow argument is not the main channel. Chapters 2 and 3 present the study in auto telematics data using a proprietary data source. Both studies are based on the application of hidden Markov model (HMM). Specifically, Chapter 2, "Auto Insurance Pricing Using Telematics Data: Application of a Hidden Markov Model", develops an HMM-based clustering framework to predict auto insurance losses using driving characteristics extracted from telematics data. Through a simulation experiment based on a proprietary telematics data set, I show that HMM can effectively classify driving trips using model-implied hidden states, and HMM-based pricing methods provide better predictive power measured by both deviance statistics and mean squared error. Importantly, the proposed framework not only enables us to price usage-based insurances at a granular level, but it is also viable for estimating long-term insurance losses utilizing the limiting properties of HMM. Chapter 3, "Theoretical Framework of a 3-Layer Hidden Markov Model for Auto Insurance Pricing", is a theoretical extension of the second chapter to improve the framework at a more granular level. I develop a 3-layer HMM for risk classification, which links driving behavior characteristics with risk classes and loss estimation. The proposed model presents a direct structure among all variables and utilizes time series data without aggregation. Furthermore, this study provides a theoretical framework to estimate the 3-layer HMM using the Expectation-Maximization (EM) algorithm. The parameters of Bernoulli distributed loss count (per unit of time) and Gamma distributed loss severity can be solved at least numerically, and the negative definite Hessian matrix indicates that the solution of the first-order condition of the log-likelihood function achieves its local maximum. / Business Administration/Risk Management and Insurance

In an increasingly digitised world, the connectivity and data within machines is becoming more important, giving possibilities to analyse and improve the business of the actors involved. The digitalisation within the earthmoving industry has so far been lagging compared to other industries but is now starting to gain more traction within the industry. With the increased interest in digitalisation within the industry, questions arise as to how this might affect the involved actors within the ecosystem. The purpose of this thesis aims to investigate how data from the earthmoving contractors’ operations can be used within the earthmoving ecosystem. In order to do so, all actors involved in the capturing, sharing and usage of data have been mapped, as well as the offering back towards the earthmoving contractor. The study used a case study approach of embedded design in order to get an in-depth understanding of the specific business ecosystem while investigating the involved actors. The study used an explorative approach due to the novel nature of the phenomenon of ecosystems in terms of data in the earthmoving context. What the study has shown is that the positions and links between actors within the ecosystem has changed due to the increased data coming from the earthmoving contractors’ operations, and the offerings back towards the contractors have changed as well. Actors within the ecosystem are becoming increasingly dependent on each other to deliver their value propositions and issues of unalignment can negatively affect the value proposition to the end user, the earthmoving contractor. The end user, which is also the actor generating the data is therefore the one seeing least direct benefits of it. / I en allt mer digitaliserad värld ges nya möjligheter att analysera och utveckla företags affärsmodeller tack vare data från uppkopplade maskiner. Digitaliseringen inom maskinentreprenadbranschen har hittills legat efter jämfört med många andra branscher, men börjar nu att få mer intresse riktat mot sig. Till följd av detta ökade intresse följer frågor hur denna digitalisering kan komma att påverka de olika aktörerna inom ekosystemet. Syftet med detta arbete är att undersöka hur data som genereras av maskinentreprenörer kan användas av olika aktörer inom ekosystemet. För att göra detta har en kartläggning genomförts som tydliggör vilka aktörer som är inblandade i insamlandet, delandet och användandet av data, samt vad deras erbjudande till maskinentreprenörerna består av. Arbetet har utförts som en casestudie med flera analysenheter för att få en djup förståelse av affärsekosystemet, samtidigt som enskilda aktörer har undersökts. Studien har utförts med en explorativ ansats, till följd av det ännu ganska outforskade fenomenet kring ekosystem och data inom maskinentreprenadindustrin. Vad studien har visat är att det ökade dataflödet från maskinentreprenörerna har lett till förändringar inom ekosystemet, genom förändrade positioner och länkar mellan aktörer. Studien har även visat på förändringen av erbjudanden tillbaka till maskinentreprenören. Aktörer inom ekosystemet har blivit allt mer beroende av varandra för att leverera ett värdeerbjudande och brist på ömsesidig anpassning kan leda till ett ofullständigt värdeerbjudande som levereras till slutkunden, i detta fall maskinentreprenören. Detta har även visat sig leda till att aktören som genererar datan är den aktör som ser minst nytta med den.

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