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Optimizing Motor Insurance Premiums in the UAE Using Predictive Analytics

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Title: Optimizing Motor Insurance Premiums in the UAE Using Predictive Analytics
Authors: Abdelsalam, Samar
Committee Members: Ioannis Karamitsos
Summary: The car insurance industry in the UAE and GCC faces growing challenges including premium inflation, fraudulent claims, inconsistent pricing models, and lagging innovation in underwriting. This study investigates how predictive analytics and machine learning can enhance motor insurance premium optimization, with a focus on incorporating socio-demographic, behavioral, environmental, and vehicle-specific risk factors. Using a publicly available dataset of over 125,000 insurance policy records, this research applies a CRISP-DM framework to develop and evaluate supervised learning models including Linear Regression, Decision Trees, and Random Forests. Results highlight Linear Regression as the most effective model (RMSE = 193.62, R² = 0.9016), enabling accurate premium estimation and improved pricing fairness. Complementary qualitative analysis contextualizes findings with industry-specific challenges such as regulatory constraints and customer demand for personalization. The project delivers a predictive modeling tool, interactive dashboards for risk profiling, and strategic recommendations for insurers aiming to implement data-driven premium structures. Ultimately, this research supports a transition toward more transparent, efficient, and equitable insurance practices in the UAE and GCC.
URL: https://repository.rit.edu/theses/12523
Database: OpenDissertations
FullText Text:
  Availability: 0
Header DbId: ddu
DbLabel: OpenDissertations
An: ddu.oai.repository.rit.edu.theses.13656
AccessLevel: 6
PubType: Dissertation/ Thesis
PubTypeId: dissertation
PreciseRelevancyScore: 0
IllustrationInfo
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  Label: Title
  Group: Ti
  Data: Optimizing Motor Insurance Premiums in the UAE Using Predictive Analytics
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  Data: <searchLink fieldCode="AR" term="%22Abdelsalam%2C+Samar%22">Abdelsalam, Samar</searchLink>
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  Data: <searchLink fieldCode="CO" term="%22Ioannis+Karamitsos%22">Ioannis Karamitsos</searchLink>
– Name: Abstract
  Label: Summary
  Group: Ab
  Data: The car insurance industry in the UAE and GCC faces growing challenges including premium inflation, fraudulent claims, inconsistent pricing models, and lagging innovation in underwriting. This study investigates how predictive analytics and machine learning can enhance motor insurance premium optimization, with a focus on incorporating socio-demographic, behavioral, environmental, and vehicle-specific risk factors. Using a publicly available dataset of over 125,000 insurance policy records, this research applies a CRISP-DM framework to develop and evaluate supervised learning models including Linear Regression, Decision Trees, and Random Forests. Results highlight Linear Regression as the most effective model (RMSE = 193.62, R² = 0.9016), enabling accurate premium estimation and improved pricing fairness. Complementary qualitative analysis contextualizes findings with industry-specific challenges such as regulatory constraints and customer demand for personalization. The project delivers a predictive modeling tool, interactive dashboards for risk profiling, and strategic recommendations for insurers aiming to implement data-driven premium structures. Ultimately, this research supports a transition toward more transparent, efficient, and equitable insurance practices in the UAE and GCC.
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RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Car Insurance
        Type: general
      – SubjectFull: Predictive Analytics
        Type: general
      – SubjectFull: Premium Optimization
        Type: general
      – SubjectFull: Fraud Detection
        Type: general
      – SubjectFull: Risk Assessment
        Type: general
      – SubjectFull: Data-Driven Pricing
        Type: general
      – SubjectFull: Insurance Innovation
        Type: general
      – SubjectFull: Supervised Learning
        Type: general
    Titles:
      – TitleFull: Optimizing Motor Insurance Premiums in the UAE Using Predictive Analytics
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Abdelsalam, Samar
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Type: published
              Y: 2026
ResultId 1