The 3 banking challenges synthetic data is ready to meet
Banks and financial institutions face increased regulatory pressure, cybersecurity threats and declining rates of customer consent. The consequences are massive data shortages, stalled digital transformation and AI adoption. New, data-rich big tech players like Apple and new incumbents like Walmart are eyeing the market, andbanks face a change or die moment.
All that change hinges on one prerequisite: access to high-quality customer data in an increasingly zero access environment. Privacy-enhancing technologies, or PETs, promise to deliver precisely that. These new technologies can unlock the intelligence contained within the customer data without posing a privacy risk. In contrast, with legacy data anonymization technologies, PETs like homomorphic encryption and synthetic data don’t destroy the intelligence of the data via the anonymization process.
The so-called privacy-utility trade-off has long been a compromise data scientists and analysts had to live with. Legacy anonymization tools like data masking and pseudonymization obscure valuable parts of the data. A large European bank was testing its flagship digital banking app with one cent transactions, having had to go live with a less than robust product. Even more worryingly, masking parts of the data does not make it anonymous. Increasingly sophisticated linkage attacks can reidentify subjects in masked datasets, endangering the privacy of customers and the safety of institutions. The consensus is clear: ditch legacy anonymizations and find the right privacy-enhancing technology for all use cases.
One of the easiest and most versatile PETs is AI-generatedsynthetic data for banking. Synthetic data offers the best of both worlds: statistically identical datasets based on production data that contain none of the original data points. Although there is no such a thing as a zero privacy risk solution, a good quality synthetic data generator provides full privacy and high accuracy. When selecting vendors, it’s important to find one with experience in banking and finance. There are three main areas for leveraging synthetic data assets: AI/ML development, software testing and data sharing.
AI and machine learning development is a crucial area of concern for banks and financial institutions, and synthetic data helps these efforts in more than one way. In fact, synthetic data is better than real data when it comes to training models.According to Gartner: “The fact is you won’t be able to build high-quality, high-value AI models without synthetic data.”
As much as 60% of the data used in AI and analytics is expected to be synthetic by 2024. What’s the appeal? You could think of synthetic data as an insight engineering tool. The process of synthetization is itself AI-driven and, as such, can be used not only for data anonymization but also for data augmentation. Not enough fraud cases in a dataset? Upsample. Too much data? Subset. Are predictions not accurate enough? Inject domain knowledge from public databases to increase accuracy. Historically biased datasets? Introduce a fairness constraint via synthesization. Even the elusive concept of explainable AI becomes realistic when the training data is synthetic.
