One of the largest retail banks in Europe developed a mobile banking app that aims to be a true alternative to in-person banking. To provide a high-quality user experience, extensive testing of the app with clients’ transaction data was crucial. However, the bank’s IT department had to heavily mask transaction data due to privacy policies, and the resulting test data failed to provide realism in transaction amounts, dates, and so on. Dummy data could never match the smart synthetic dataset’s granularity and realism, failing to provide the complexity necessary for testing a product so important to work flawlessly. Imagine you could create realistic customers at the push of a button.
How can you tell that your test data is exceptionally high quality? We decided to test the testers and presented a mix of synthetic and real customers, using the MOSTLY team’s real banking data. Product developers couldn’t spot synthetic customers in the batch - that is how realistic and high-dimensional the smart test data was. Generating smart test data took significantly less time than anonymizing the initially used and discarded dummy data. Synthetic transaction data was safe to share with third parties, enabling the development of further customer-centric products and services, such as a bill-splitting app, serving a real customer need.