Improving Risk Assessment Models in Insurance Using GAN-Generated Data

Authors

Adarsh Naidu

Abstract

Accurate risk assessment is essential for the insurance industry to make informed decisions and stay financially stable. Traditional approaches typically depend on historical data, which may be insufficient or skewed. This paper examines how Generative Adversarial Networks (GANs) can be used to create synthetic data to improve risk assessment models in insurance. We propose a framework that uses GAN-generated data to enhance existing datasets, helping build stronger and more accurate risk assessment models. The quality and variety of the generated data are measured using different metrics, and its impact on model performance is tested through detailed experiments. The results show that adding GAN-generated data significantly boosts the accuracy and reliability of risk models, especially in cases where historical data is limited or unbalanced. This study demonstrates the potential of GANs to solve data shortages and bias in insurance risk assessment, leading to better decisions and improved financial outcomes for insurance companies [1][2][3].

Keywords

Artificial intelligence, Generative adversarial networks, Insurance, Machine learning, Predictive models, Risk analysis, Risk assessment, Synthetic data generation.

Improving Risk Assessment Models in Insurance Using GAN-Generated Data. Adarsh Naidu. 2020. IJIRCT, Volume 6, Issue 1. Pages 1-5. https://www.ijirct.org/viewPaper.php?paperId=2503094