EXPLORING THE ROLE OF SYNTHETIC DATA GENERATION IN TRAINING ROBUST INSURANCE MODELS AND MITIGATING DATA PRIVACY CONCERNS
Keywords:
Synthetic Data, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Data Privacy, Insurance Models, Anonymization, De-identification, Differential Privacy, Model RobustnessAbstract
The increasing reliance on data-driven models in the insurance industry underscores the need for effective solutions to address data privacy concerns and enhance model robustness. This research investigates the role of synthetic data generation in training insurance models, focusing on methods such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). The study evaluates the performance of models trained with synthetic data compared to those trained with real data, finding that synthetic data offers comparable effectiveness while addressing privacy issues. By employing techniques such as anonymization, de-identification, and differential privacy, synthetic data helps mitigate risks associated with handling sensitive information. The results suggest that synthetic data can serve as a practical tool for enhancing data privacy and improving model accuracy in the insurance sector. The findings highlight the potential of synthetic data to balance data utility with privacy, promoting more secure and efficient data management practices.
References
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.
Choi, E., Schuetz, A., Stewart, W. F., & Facius, C. (2019). Using deep learning for healthcare predictive modeling. Journal of Biomedical Informatics, 92, 103106.
Cohen, I. (2019). Data privacy: The role of synthetic data. Journal of Data Protection & Privacy, 2(1), 21-29.
Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3–4), 211-407.
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2014). Calibrating noise to sensitivity in private data analysis. Theory of Cryptography Conference, 265-284.
Fröhlich, H., Engelbrecht, A., & Adams, R. (2020). Generating synthetic electronic health records with variational autoencoders. IEEE Access, 8, 96378-96387.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., & Ozair, S. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27.
Hochreiter, S., & Schmidhuber, J. (2018). Long short-term memory. Neural Computation, 9(8), 1735-1780.
Johnson, A. E., Pollard, T. J., Shen, L., & Lehman, L. W. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035.
Kingma, D. P., & Welling, M. (2013). Auto-encoding variational Bayes. International Conference on Learning Representations.
Li, X., Xu, Z., & Zhang, M. (2020). Enhancing risk models with synthetic claims data: A case study in insurance. Journal of Risk and Insurance, 87(4), 1025-1048.
Li, Y., Wang, Y., & Chen, X. (2021). Privacy-preserving synthetic data generation using differential privacy. IEEE Transactions on Information Forensics and Security, 16, 823-834.
Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359.
Wang, Y., Liu, T., & Zhang, L. (2021). Synthetic data for insurance claim prediction and risk assessment. Insurance: Mathematics and Economics, 101, 193-206.
Zhang, X., & Wang, L. (2020). Addressing the generalization gap in models trained with synthetic data. Artificial Intelligence Review, 53(3), 1895-1912.
California Legislative Information. (2018). California Consumer Privacy Act of 2018. Retrieved from https://leginfo.legislature.ca.gov
Dwork, C., & Roth, A. (2014). The Algorithmic Foundations of Differential Privacy. Foundations and Trends® in Theoretical Computer Science, 9(3–4), 211-407.
El Emam, K., Dankar, F. K., & Jonker, E. (2011). A Systematic Review of De-identification and Anonymization of Health Data. Journal of the American Medical Informatics Association, 18(1), 1-6.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … & Bengio, Y. (2014). Generative Adversarial Nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems (pp. 2672-2680).
Kingma, D. P., & Welling, M. (2013). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR).
Sweeney, L. (2002). k-Anonymity: A Model for Protecting Privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557-570.
Voigt, P., & Von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR). Springer.
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Copyright (c) 2023 Devidas Kanchetti (Author)
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