Sökning: "machine learning insurance"

Visar resultat 1 - 5 av 31 uppsatser innehållade orden machine learning insurance.

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. Detection of insurance fraud using NLP and ML

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Rasmus Bäcklund; Hampus Öhman; [2023]
   Nyckelord :Technology and Engineering;

   Sammanfattning : Machine-Learning can sometimes see things we as humans can not. In this thesis we evaluated three different Natural Language Procces-techniques: BERT, word2vec and linguistic analysis (UDPipe), on their performance in detecting insurance fraud based on transcribed audio from phone calls (referred to as audio data) and written text (referred to as text-form data), related to insurance claims. LÄS MER

4. 3. A Predictive Analysis of Customer Churn

   Kandidat-uppsats, KTH/Matematisk statistik

   Författare :Olivia Eskils; Anna Backman; [2023]
   Nyckelord :Churn prediction; CRM; optimization; applied mathematics; machine learning; gradient boosting; random forest; logistic regression; insurance industry; Kundbortfall; CRM; optimering; tillämpad matematik; maskininlärning; gradient boosting; random forest; logistisk regression; försäkringsbranschen;

   Sammanfattning : 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. LÄS MER

5. 4. Predicting Risk Level in Life Insurance Application : Comparing Accuracy of Logistic Regression, DecisionTree, Random Forest and Linear Support VectorClassifiers

   Kandidat-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskap

   Författare :Pulagam Karthik Reddy; Sutapalli Veerababu; [2023]
   Nyckelord :Decision Tree Classifier; Logistic Regression; Machine Learning; Random Forest Classifier; Linear Support Vector Classifier;

   Sammanfattning : Background: Over the last decade, there has been a significant rise in the life insurance industry. Every life insurance application is associated with some level ofrisk, which determines the premium they charge. The process of evaluating this levelof risk for a life insurance application is time-consuming. LÄS MER

7. 5. A Causal Analysis of Cat Bond Markets

   D-uppsats, Handelshögskolan i Stockholm/Institutionen för finansiell ekonomi

   Författare :Tim Matheis; [2023]
   Nyckelord :Cat bonds; Insurance-linked securities; Causal machine learning; Random forests;

   Sammanfattning : This work is a contribution to the causal analysis of the catastrophe bond market, which has generated high excess returns over the last two decades. Since these excess returns remain partially unexplainable and the interest in catastrophe bonds is increasing, the causal study of the factors affecting their premiums is of high relevance. LÄS MER