GAN-Based Synthetic Insurance Claim Data Generation for Fraud Detection  Systems

Adarsh Naidu

GAN-Based Synthetic Insurance Claim Data Generation for Fraud Detection Systems

Authors

Adarsh Naidu

Keywords:

Insurance Fraud Detection; Generative Adversarial Networks; Synthetic Data Generation; Class Imbalance; Data Augmentation; Machine Learning

Abstract

The rapid adoption of data-driven decision-making in the insurance industry has intensified theneed for effective fraud detection systems. However, the development of reliable machinelearning–based fraud detection models is severely constrained by the limited availability of high quality insurance claim data, extreme class imbalance

References

R. J. Bolton and D. J. Hand, “Statistical fraud detection: A review,” Statistical Science, vol. 17, no. 3, pp. 235–255, 2002.

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Published

2022-02-15

How to Cite

Adarsh Naidu. (2022). GAN-Based Synthetic Insurance Claim Data Generation for Fraud Detection Systems . Journal of Computational Analysis and Applications (JoCAAA), 30(2), 1094–1107. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5181