When thinking about the uses of synthetic data with financial institutions, banking applications and fraud detection are what commonly come to mind, but synthetic data can be used in junction with credit cards as well.
Credit card data is useful for predictive analytics to determine potential future purchases so that promotions and other marketing efforts for credit card users are more effective. Additionally, credit card data can be used to help track fraud due to purchase history outlining consumer tastes and behaviors, allowing banks to detect fraudulent purchases or false positives within fraudulent activity.
So how does synthetic data tie in to this? Artificially generated data can be used to speed up the training of the machine learning and algorithm testing of credit card fraud detection software and predictive analytics, not to mention giving the applications a larger pool of data to test with to ensure as little error as possible. Demographics and private financial information of consumers are also safe when using synthetic data while the software runs just the same as if their data was being used, making it a win-win for all parties involved!