Structured synthetic data has the potential to revolutionise the way financial institutions use data securely.
Financial data, such as consumer transaction records, account payments, or trading data, is sensitive personal data subject to data protection obligations and is often commercially sensitive. Revolutionising data privacyĪccording to Gartner, synthetic data will enable organisations to avoid 70% of privacy violation sanctions. But in 2023, I believe the sector will open its eyes to the notion of synthetic data and how it can fuel growth, support overcoming longstanding obstacles, and totally rejuvenate the way financial services institutions meet and exceed the ever-evolving requirements of customers and regulators. This is already happening as the first banks start to roll out synthetic data across various use cases, from testing to AI model training to cloud migration projects. Whether the aim is to make data available across an organisation or accessible to third-party partners it drives speed to innovation within financial services. It is ‘artificial’ data that maintains the same statistical properties as ‘real’ data, generated using algorithms. This is not ‘real’ data created naturally through real-world events. This means that data is not driving the valuable innovation it can do to improve automation, decision-making and risk management. Much of this is due to the legacy infrastructure in place, the regulatory landscape in which they operate, and security and governance protocols they have been hamstrung by. However, these institutions have long been dubbed laggards when it comes to technology, innovation, and the speed at which they can digitally transform.
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