International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Understanding Moral Hazard in Auto Insurance Claims with AI-Powered Predictive Models

Author(s) Rajesh Goyal
Country United States
Abstract Fraudulent auto insurance is a serious problem, as monetary losses happen to insurance companies and the costs increase for policyholders. The goal of this project is to develop a prediction algorithm that is able to correctly guess potential auto insurance fraud claims. Moral hazard issues always challenge the design and regulation of insurance policies. Yet most of these worries are based on theoretical expectations of how rational economic agents will respond to financial incentives. In this study, ML and DL approaches are used in data-driven ways to identify false statements and evaluate behavioral patterns of moral hazard. It involves the preprocessing of the unbalanced Auto Insurance Claims Fraud Detection dataset, training the classification models like XGBoost, Random Forest (RF), Recurrent Neural Networks (RNN), and Multi-Layer Perceptron (MLP) and performing feature engineering with the help of min max normalization as well as one hot encoding. Accuracy, precision, recall, F1 score and AUC ROC are used to evaluate the models. Experimental results show that XGBoost performs better than deep learning models in fraud classification, with the highest accuracy (82.5%) and a balanced trade-off between recall (80.39%) and precision (80.80%). The findings highlight the effectiveness of ML-based ensemble techniques in mitigating moral hazard and enhancing fraud detection strategies.
Field Engineering
Published In Volume 5, Issue 5, September-October 2023
Published On 2023-10-05
DOI https://doi.org/10.36948/ijfmr.2023.v05i05.43942
Short DOI https://doi.org/
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E-ISSN 2582-2160
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