SYNTHETIC DATA FOR FINANCIAL SECURITY: BALANCING PRIVACY AND MODEL ACCURACY
Keywords:
Generative Adversarial Networks,Synthetic Data, Insurance Models,Variational Autoencoders, Data Privacy , Anonymization, Deidentification, Differential Privacy and Model Robustness.Abstract
This increased attention and reliance on data-driven models in the insurance business signify that there is a requirement for workable solutions to effect the privacy-related concerns and ensure model robustness. The study investigates into synthetic-data-generation techniques in training insurance models with special attention to GANs and VAEs. It further compares the functioning of models trained using synthetic data to those using the real data and concludes that synthetic data provides an equal level of functionality while mitigating privacy-related issues. With such implementations, synthetic data provides a way to avoid risks of handling sensitive information by means of anonymization, deidentification, and differential privacy. The study concludes that synthetic data may act as a viable means to ensure data privacy and improve model accuracy vis-à-vis the traditional data-gathering approach commonly adopted by the insurance industry. The findings indicate that synthetic data can strike a balance between utility and privacy when it comes to data, thereby offering possibilities of introducing safe and efficient data management practices.
