Sökning: "Insurance Tariff"

Visar resultat 1 - 5 av 6 uppsatser innehållade orden Insurance Tariff.

  1. 1. Double Machine Learning for Insurance Price Optimization

    Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)

    Författare :Jakob Kristiansson; [2023]
    Nyckelord :DML; Double Machine Learning; Price Optimization; Insurance Pricing; DML; Dubbel Maskininlärning; Prisoptimering; Försäkringsprissättning;

    Sammanfattning : This thesis examines how recent advances in debaised machine learning can be used for estimating price elasticities of demand within the automotive insurance field. Traditional methods such as generalized linear model (GLM) to estimate demand has no way of ensuring there are no biases in the underlying data selection, especially when the confounding variables are many. LÄS MER

  3. 2. Using Gradient Boosting to Identify Pricing Errors in GLM-Based Tariffs for Non-life Insurance

    Master-uppsats, KTH/Matematik (Avd.)

    Författare :Felix Greberg; Andreas Rylander; [2022]
    Nyckelord :GLM; Gradient Boosting; XGBoost; Non-life insurance; Property Casualty; Rate making; Insurance Tariff; MTPL insurance; Machine learning; Regression trees; Tweedie regression; Credit risk; GLM; Gradient Boosting; XGBoost; Skadeförsäkring; Prissättning; Försäkringstariff; Trafikförsäkring; Regressionsträd; Maskininlärning; Tweedie-regression; Kreditrisk;

    Sammanfattning : Most non-life insurers and many creditors use regressions, more specifically Generalized Linear Models (GLM), to price their liabilities. One limitation with GLMs is that interactions between predictors are handled manually, which makes finding interactions a tedious and time-consuming task. LÄS MER

  4. 3. Tree-based Machine Learning Models with Applications in Insurance Frequency Modelling

    Kandidat-uppsats, KTH/Skolan för teknikvetenskap (SCI)

    Författare :Samuel Tober; [2020]
    Nyckelord :;

    Sammanfattning : As the insurance industry is highly data driven it is no surprise that machine learning (ML) has made its way into the industry. While GLMs are still the comfort zone of most actuaries, we have in recent years seen a surge in machine learning algorithms. LÄS MER

  5. 4. Modeling risk and price of all risk insurances with General Linear Models

    Kandidat-uppsats, KTH/Matematisk statistik

    Författare :Ellinor Drakenward; Emelie Zhao; [2020]
    Nyckelord :Bachelor Thesis; Mathematical statistics; Generalized Linear Model; Multiplicative GLM; Regression analysis; Insurance Pricing; Claims; Tariff; Kandidatexamensarbete; Matematisk statistik; Generaliserad linjär modell; Försäkringsanspråk; Multiplikativ GLM; Regressionsanalys; Försäkring; Prissättning;

    Sammanfattning : Denna kandidatexamen ligger inom området matematisk statistik. I samarbete med försäkringsbolaget Hedvig syftar denna avhandling till att utforska en ny metod för hantering av Hedvigs försäkringsdata genom att bygga en prissättningsmodell för alla riskförsäkringar med generaliserade linjära modeller. LÄS MER

  7. 5. Bayesian insurance pricing using informative prior estimation techniques

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Alexandra Hotti; [2020]
    Nyckelord :;

    Sammanfattning : Large, well-established insurance companies build statistical pricing models based on customer claim data. Due to their long experience and large amounts of data, they can predict their future expected claim losses accurately. In contrast, small newly formed insurance start-ups do not have access to such data. LÄS MER