This bachelor thesis in the field of mathematical statistics was carried out in collaboration with an upcoming insurance start-up, Hedvig, and had the objective of investigating the predictive capabilities of different types of insurance claims. This was done through regression analysis, and more specifically the area in regression analysis called generalized linear models. Logistic regression was employed as the modeling technique, and data points were modeled in various ways to then be used to fit models in order to determine the most optimal one based on some pre-determined statistical evaluation metrics. The final model had an accuracy of above 96%, and the results showed that certain types of claims had a bigger contribution to the probability of a claim occurring the next period. This study contributes to the understanding of the predictive capabilities of insurance claim data and provides insights that could aid in the development of more accurate and efficient insurance pricing models. / Denna kandidatuppsats inom området matematisk statistik utfördes i samarbete med ett nyetablerat försäkringsföretag, Hedvig, och hade som syfte att undersöka den prediktiva förmågan hos olika typer av ersättningsanspråk. Detta gjordes genom regressionsanalys, och mer specifikt det område inom regressionanalys som kallas generaliserade linjära modeller. Logistisk regression användes som modelleringsteknik, och datapunkterna modellerades på olika sätt för att sedan skapa modeller, med syfte att fastställa den mest optimala modellen utifrån vissa förutbestämda statistiska utvärderingsmått. Den slutliga modellen hade en exakthet på över 96%, och resultaten visade att vissa typer av anspråk bidrog i större utsträckning till sannolikheten för att ett anspråk skulle inträffa under nästa period. Den här studien bidrar till förståelsen av den prediktiva förmågan hos data på ersättningsanspråk och ger insikter som kan bidra till utvecklingen av mer exakta och effektiva modeller för prissättning av försäkringar.

Of concern in this research were the ecological parameters associated with the establishment of a model invasive plant species, Imperata cylindrica, across a scale of ecological organization. Specifically, the study addressed the species’ ability to: differentially respond to abiotic and biotic constraints during seedling establishment, exhibit a novel underground competitive interference mechanism, and alter the decomposition dynamics in newly invaded ecosystems. Finally, the last portion of the research was centered around creating a predictive habitat model that will provide information on the most important variables responsible for creating habitat for this species. The population level seedling study indicated that soil characteristics and light availability play a significant role in seedling establishment. There were large trends in biomass allocation attributable to soil type with seedlings performing best in high nutrient soils representative of the Mississippi Alluvial Valley physiographic region. I. cylindrica seedlings also showed a positive response to increased seedling density during the initial stages of seedling establishment. The community level research examining a hypothesized novel interference mechanism deployed by I. cylindrica showed a significant and robust pattern of I. cylindrica damaging its own belowground tissue more often than that of its surrounding neighbors. Therefore, it is highly unlikely that I. cylindrica gains a competitive advantage by exposing the native plant assemblage to pathogen invasion (via ruptured tissue) as the plant would expose itself to these pathogens (to which it is evolutionarily naive) at much higher volumes. The ecosystem level examination of this globally important invasive species indicated that I. cylindrica invasion into native systems will significantly accelerate ambient rates of decomposition. Furthermore, fungal community composition in invaded areas was drastically altered as well as bacterial community functional activity in relation to several key enzymes responsible for the decomposition of plant tissue which were produced more abundantly in invaded areas.The landscape-scale analyses and modeling work validated decades of anecdotal evidence and indicated that anthropogenic disturbance factors associated with road maintenance and construction (soil disturbance and vegetation removal) are the principal factors responsible for creating habitat suitable for invasion by this species.

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Churn refers to the discontinuation of a contract; consequently, customer churn occurs when existing customers stop being customers. Predicting customer churn is a challenging task in customer retention, but with the advancements made in the field of artificial intelligence and machine learning, the feasibility to predict customer churn has increased. Prior studies have demonstrated that machine learning can be utilized to forecast customer churn. The aim of this thesis was to develop and implement a machine learning model to predict customer churn and identify the customer features that have a significant impact on churn. This Study has been conducted in cooperation with the Swedish insurance company Bliwa, who expressed interest in gaining an increased understanding of why customers choose to leave.  Three models, Logistic Regression, Random Forest, and Gradient Boosting, were used and evaluated. Bayesian optimization was used to optimize the models. After obtaining an indication of their predictive performance during evaluation using Cross-Validation, it was concluded that LightGBM provided the best result in terms of PR-AUC, making it the most effective approach for the problem at hand. Subsequently, a SHAP-analysis was carried out to gain insights into which customer features that have an impact on whether or not a customer churn. The outcome of the SHAP-analysis revealed specific customer features that had a significant influence on churn. This knowledge can be utilized to proactively implement measures aimed at reducing the probability of churn. / Att förutsäga kundbortfall är en utmanande uppgift inom kundbehållning, men med de framsteg som gjorts inom artificiell intelligens och maskininlärning har möjligheten att förutsäga kundbortfall ökat. Tidigare studier har visat att maskinlärning kan användas för att prognostisera kundbortfall. Syftet med denna studie var att utveckla och implementera en maskininlärningsmodell för att förutsäga kundbortfall och identifiera kundegenskaper som har en betydande inverkan på varför en kund väljer att lämna eller inte. Denna studie har genomförts i samarbete med det svenska försäkringsbolaget Bliwa, som uttryckte sitt intresse över att få en ökad förståelse för varför kunder väljer att lämna. Tre modeller, Logistisk Regression, Random Forest och Gradient Boosting användes och utvärderades. Bayesiansk optimering användes för att optimera dessa modeller. Efter att ha utvärderat prediktiv noggrannhet i samband med krossvalidering drogs slutsatsen att LightGBM gav det bästa resultatet i termer av PR-AUC och ansågs därför vara den mest effektiva metoden för det aktuella problemet. Därefter genomfördes en SHAP-analys för att ge insikter om vilka kundegenskaper som påverkar varför en kund riskerar, eller inte riskerar att lämna. Resultatet av SHAP-analysen visade att vissa kundegenskaper stack ut och verkade ha en betydande påverkan på kundbortfall. Denna kunskap kan användas för att vidta proaktiva åtgärder för att minska sannolikheten för kundbortfall.

[pt] O objetivo da dissertação é analisar e comparar a influência de diferentes tipos de parceiros em arranjos cooperativos para projetos de PD&I sobre a apropriação de valor pelas empresas inovadoras da indústria de transformação no Brasil, considerando-se condições ambientais distintas, como nível de intensidade tecnológica e força do regime de apropriabilidade dos setores em que atuam, bem como características internas, como tamanho da empresa e capacidade para cooperar em projetos de PDeI (focalizando inovações de produto e/ou processo). A fonte de dados é a Pesquisa Nacional de Inovação (Pintec 2014), realizada pelo Instituto Brasileiro de Geografia e Estatística (IBGE). A pesquisa pode ser considerada descritiva e aplicada. A metodologia adotada compreende pesquisa bibliográfica sobre inovação; mecanismos de apropriação de valor; cooperação interorganizacional para inovação e classificações tecnológicas, destacando-se a classificação de intensidade tecnológica proposta pela Organização para a Cooperação e Desenvolvimento Econômico (OCDE); pesquisa documental referente à Classificação CNAE e ao Manual da Pintec 2014, ambas divulgadas pelo IBGE; análise de conteúdo para classificar as atividades econômicas das empresas inovadoras da indústria de transformação (respondentes da Pintec 2014), segundo quatro níveis de intensidade tecnológica e regime de apropriabilidade dos setores em que atuam e três faixas de pessoal alocado; solicitação ao IBGE de acesso aos microdados não desidentificados da Pintec 2014; e desenvolvimento de modelos econométricos logit para os doze agrupamentos de empresas, classificadas por intensidade tecnológica/força do regime de apropriabilidade do setor e por faixa de pessoal ocupado. A utilização dos microdados da Pintec 2014 para analisar a influência da cooperação interorganizacional sobre a apropriação de valor pelas empresas inovadoras da indústria de transformação no Brasil em diferentes condições ambientais conferem à pesquisa um caráter original, uma vez que os estudos anteriores baseados em Pesquisas Nacionais de Inovação não exploraram essa abordagem metodológica. / [en] This dissertation aims to analyze and compare the influence of different types of partners in cooperative arrangements for RDandI projects on the appropriation of value by innovative companies of the transformation industry in Brazil, considering different environmental conditions, such as the level of technological intensity and strength of the appropriability regime of the sectors in which they operate, as well as internal characteristics such as company size and its capacity to cooperate in RDandI projects (focusing on product and/or process innovations). The primary data source is the National Innovation Survey (Pintec), conducted by the Brazilian Institute of Geography and Statistics (IBGE). The research can be considered descriptive and applied. The methodology adopted includes bibliographic research on innovation; value creation and value appropriation mechanisms; and technological classifications, highlighting the classification of sectoral technological intensity proposed by the Organization for Economic Cooperation and Development (OECD); documentary analysis concerning the Brazilian Classification of Economic Activities (CNAE) and Pintec 2014, both published by IBGE; content analysis to classify the economic activities of the respondent companies, according to sectoral technological intensity, appropriability regime of the sectors in which they operate and number of employees; request to IBGE for access to microdata of Pintec 2014; development of logit econometric models for the companies classified by sector technological intensity/strength of the appropriability regime, and by number of employees (micro and small, medium and large companies). The use of microdata from Pintec 2014 to analyze and compare the influence of different types of partners in cooperative arrangements for RDandI projects on the appropriation of value by innovative companies under different environmental conditions give the research an original character, since previous studies based on National Innovation Surveys have not explored this methodological approach.