Far too many companies in the financial sector find themselves in an incredibly frustrating position.
On the one hand, they’re sitting on mountains of valuable, insight-rich data. Data that should be driving their analytics and data science projects. That should be helping to improve their processes. To streamline KYC and customer onboarding processes, and vastly reduce lending risk. That could be helping them to boost efficiency, save money and grow their business.
They know that data-driven, machine learning models would facilitate the development of new market-leading products, or open up other brand new revenue streams. They know that they are tantalizingly close to using their data assets to predict customer churn and LTV. To build personalized marketing campaigns that will really work.
… But on the other hand, they don’t dare use the data in the ways they’d actually need to, in order to achieve all those goals.
Formidable compliance requirements and privacy obligations make many financial services companies wary of doing anything that could lead to a cybersecurity breach or data leak. Even with the best anonymization and encryption tools available to them, these companies can’t be absolutely sure that a crafty hacker won’t figure out a way to re-identify real people whose sensitive data they’ve collected. Faced with the prospect of exposing their customers to danger, many decide against taking the risk.
But others have caught onto a third way that, essentially, lets them have their cake and eat it. By using their raw datasets to generate synthetic data, they replicate all the value and insight of the original data, without the associated risk. The synthetic dataset replicates the statistical properties of the source production data, meaning companies can use it in exactly the same ways with the same level of confidence, including to validate models and test new products, services, and software performances. They can, in fact, use this data for all the business-boosting purposes they want, without running the risk of undermining anyone’s privacy.
The implications of this are huge for organizations of all stripes and sizes, all across the financial sector.
Suddenly, they are free to share and collaborate on data, both with internal teams and external partners. They can start monetizing this data, stripping out waste and inefficiency inside the business while creating exciting new products for their customers. They can even repackage and sell synthetic datasets of aggregate values, extrapolated from real customer data and statistically indistinguishable from this, to third parties, without falling on the wrong side of regulation.
The Use of Synthetic Datasets for Financial Services
What’s more, by artificially generating these synthetic datasets, financial services companies can even boost DevOps by shortening their time to production. Since they don’t have to acquire or collect as much “real” data before they get started, they can jump right in without delay. Testing times reduce, projects become more flexible and development becomes more agile.
Put simply, industry leaders in the financial sector that embrace synthetic data are using this to drive forward innovation, collaboration and profit-making ventures. It opens all the doors that privacy fears have kept firmly shut. At least, until now.
Find out more about how the financial sector is benefiting from synthetic data in this in-depth article >
