The Impact of Artificial Intelligence on Data-Driven Insurance Systems: An Applied Study using Machine Learning Models
DOI:
https://doi.org/10.59992/IJFAES.2025.v4n12p17Keywords:
Artificial Intelligence, Data-Driven Insurance, Pricing Fairness, Algorithmic Bias, Insurance FraudAbstract
This study investigates the impact of artificial intelligence (AI) applications in the insurance sector, focusing on measuring risk prediction accuracy, Identifying the factors affecting installment pricing, and reducing bias in underwriting decisions, as well as evaluating AI’s effectiveness in mitigating insurance fraud. A quantitative methodology was employed, including linear regression models, analysis of variance (ANOVA), and t-tests, based on a dataset comprising insurance customers and incorporating both traditional and AI-driven variables.
The findings indicate a statistically significant effect of AI-based models on improving claim prediction accuracy and achieving fairer premium pricing. AI-related variables enhanced the explanatory power of the models and reduced reliance on traditional variables prone to bias. However, the results did not provide conclusive evidence of AI’s impact on reducing insurance fraud, likely due to data limitations and the characteristics of the fraud variable.
The study concludes that transitioning toward data-driven insurance is a promising approach, provided that governance and regulatory frameworks are implemented to ensure transparency and fairness in algorithmic decision-making.
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