Python

Recently Data Sense published an article discussing how synthetic financial data is reshaping risk management in financial services. We detailed how financial regulators have begun to experiment and publish guidelines for implementing and assessing synthetic data for analytical fidelity and privacy preservation. But how can this actually be achieved? Extending our previous research, we have provided a framework below for economists, supervisors and financial data scientists to implement and assess synthetic data use cases. The objective of this tutorial is to help economists, supervisors, and financial data scientists gain practical experience in generating, validating, and assessing synthetic financial data using…