The common issues in the financial services industry include legacy systems that are difficult to maintain, stringent laws, increased security risks, and the inability to transfer, share and scale data to promote data-centricity and innovation.
Using synthetic data is the only available option to tackle all these issues. In this article, we will discuss how financial organizations benefit from using synthetic data.
The accuracy of deep machine learning models is heavily influenced by data size. To increase data size, synthetic data can be utilized. Labeled data, in addition to larger data size, is another perk of synthetic data for model accuracy.
Data labeling is a time-consuming procedure, and manual labeling is vulnerable to mistakes that might result in model inaccuracies. Synthetic data have proper labels for observations, removing the need for data labeling efforts and paving the way for extremely accurate ML models.
Financial organizations might wish to test methods under severe situations, such as market collapses or app outages, from time to time.
Instead of having an unbalanced dataset of such occurrences, they may lack the data resulting from these scenarios. They can use synthetic data to fill these gaps and assist them in developing measures to counteract these types of disasters.
Regulations such as GDPR and CCPA restrict financial data from being shared with third-party fintech corporations. As a result, it is difficult for a financial institution to analyze possible partners prior to launching new products.
The synthetic dataset comprises statistical information and correlations from bank transaction data but no data about the real customers. Instead of the real dataset, financial institutions can provide synthetic data that retains the key elements of the original dataset. Therefore, synthetic data has the potential to reduce the dangers associated with sharing.
The synthetic data platform enables banks to create a realistic test data environment by using synthetic replicas of their customer data. It matches the appearance, size, and shape of the production environment.
The realistic data also aids in the delivery of new data-driven product features, accelerates UX design decisions, increases development efficiency, and results in a more user-friendly and robust product/app.
A large library of credit card data, including transaction amounts, timings, and locations, would be highly helpful to the retail industry. Banks cannot sell the raw data for a plethora of reasons, including the violation of the privacy of their customers.
The collection and sale of customer data to other parties are strictly monitored. Therefore, banks can sell a synthetic dataset of accumulated values that is statistically identical to the original one. Potential purchasers would be willing to pay a high price for it.
The benefits that the financial sector derives from synthetic data will only increase in the coming years. It will benefit model maturity across the financial services sector by enhancing data-driven decision-making and encouraging innovation.