With strict data privacy laws in place, a rise in data breaches, and growing cybersecurity concerns, banks, and financial institutions face serious challenges when accessing and using real-world data. Synthetic data reduces compliance costs and overcomes usage limitations by providing secure, anonymized datasets used for modeling and testing without exposing sensitive customer information.

In addition to compliance and privacy concerns, banks and financial institutions struggle with accurately predicting risk, fraud, and money laundering. As these events are rare, it can be difficult to build accurate models based on real-world data. Synthetic data helps banks and financial institutions overcome this challenge by providing a more robust dataset for testing and refining predictive models, identifying and analyzing trends, assessing risk, and reducing the likelihood of money laundering and fraud.

Banks and financial institutions depend on accurate data to make critical business decisions. Silos and compliance barriers often prevent them from accessing the data they need to gain valuable insights, resulting in missed opportunities and revenue loss. Synthetic data provides a cost-effective and secure solution for accessing critical data, enabling banks and financial institutions to increase their competitive edge and generate new revenue streams.

Digital products with personalized services, such as mobile banking apps or customer relationship management software, require data sharing with third parties. Financial institutions need reliable and secure methods for sharing sensitive data with third-party vendors without risking a breach of customer privacy or compliance violations. Synthetic data is the perfect replacement for original data, enabling banks and financial institutions to safely share data needed for software development and testing without compromising security or risking compliance issues.