Dynamic Pricing for Auto Rental Insurance
Date
2024-12
Authors
Kandanur, Pallavi
Major Professor
Mitra, Simanta
Advisor
Committee Member
Prabhu, Gurpur M
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Altmetrics
Abstract
This project focuses on developing a machine learning framework for dynamic insurance pricing in short-term rental insurance. The framework predicts accident severity and calculates personalized insurance costs by leveraging driver, location, and weather information. Using publicly available datasets and private sales and claims records, the model applies a probabilistic approach to assess accident likelihood and severity,integrating diverse feature sets through a multi-task neural network architecture and using XGBoost model. The methodology involves embedding-based feature transformations, data augmentation strategies, and advanced machine
learning techniques to address challenges like data sparsity and class imbalance. Offline evaluations compare the proposed model’s dynamic pricing capabilities against a static baseline, revealing significant improvements in net income, pricing accuracy, and profitability metrics. Despite data limitations, the results highlight the effectiveness of the approach in aligning insurance premiums with actual risks.
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Type
creative component
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Copyright
2024