In today's data-driven world, companies and organisations across industries constantly collect, analyse, and utilise data to inform their decisions and strategies. However, using real-world data can present various challenges, particularly in data privacy and security. This is where synthetic data comes in as an innovative solution that balances data privacy and data utility. In this blog post, we'll explore the importance of synthetic data and why it should be considered a best practice in various industries, focusing on personal identifiable data and GDPR. What is Synthetic Data? Synthetic data refers to artificially generated data that mimics the statistical properties of real-world data. It can be created using various techniques such as generative adversarial networks (GANs), agent-based simulations (ABM), variational autoencoders (VAEs), and other machine learning algorithms. The resulting synthetic data can be used in place of real-world data in various applications, su

 Financial crime continues to pose a significant threat to the global financial system, and traditional methods of detecting and preventing these crimes are often inadequate. In recent years, the use of artificial intelligence (AI) has emerged as a promising solution to this problem, but it requires high-quality data to be effective. This is where financial crime vaccines come into play. Financial crime vaccines use synthetic data to train AI systems, creating a more robust and effective defence against financial crime. Using synthetic data, financial institutions can train AI models to identify and prevent financial crime without exposing sensitive customer information. This makes financial crime vaccines a safe and secure way to fight financial crime. But the creation of a financial crime vaccine is a complex task requiring multiple financial institutions' collaboration. This is why a consortium is necessary to develop and deploy financial crime vaccines successfully. Financial c