Case Study
Synthetic data for software development and testing with a leading Dutch Bank
About the client
Our customer, a leading bank, is a Dutch multinational banking and financial services company. This bank is one of the 5 largest banks in the Netherlands with over 5 million customers. This bank was ranked high in the list of “the world’s safest banks” by Global Finance and it aims to maintain and improve its position in this list.
The situation
This bank has a strong data-driven strategy, that aims to enable the bank to stay competitive in a dynamic and strong-competitive financial landscape. In this ambition, the bank heavily relies on data in the development of its core banking functions (CRM system, payment system, etc.) and innovative solutions (mobile banking app, virtual environment, etc.). The massive data amount complicates the creation of proper test data. Additionally, the data is stored in different databases and needs to be ingested from different sources.
Personal data from production is not an option for this bank from a privacy perspective. In an attempt to solve this issue, the bank tried existing dummy-data and mock-data generation tools in the past. However, those tools did not satisfy expectations, because they did not provide a universal and standardized data generation approach, did not maintain good data quality that did not look like production data and required a lot of manual work.
The solution
Syntho’s platform provides an opportunity to generate production-like data, that allowed this bank to now benefit from accelerated testing without sacrificing original data structures or relationships. By using the power of AI generation, Personally Identifiable Information scanners, and subsetting, this bank has now the solution to easily generate and maintain test data and accelerate software development lifecycles.
After the successful implementation of synthetic data for software development and testing, the bank is considering starting to use the platform for data analytics within the business intelligence department.
The benefits
Production-like test data
Allowing the fast simulation of production-like data, which keeps the original structure, replicates relationships, and is easy to maintain. This not only ensures proper testing of systems and applications, but also accelerates development cycles while maintaining robust data privacy.
Privacy-by-design
By using synthetic data, banks can adhere to strict data privacy regulations while still achieving accurate results and innovative advancements. By ensuring that sensitive customer information remains protected throughout testing and development processes and that personal data from production is not simply used as test data.
Faster software development cycles
Synthetic data usage accelerates software development, allowing rapid iteration and testing. The synthetic test data is of higher quality and similar in comparison to production data, thereby enhancing the quality of its tests to spot bugs earlier and to release faster. This expedites the launch of new financial products and services, enhancing the bank’s competitive edge in the market.
Data subsetting
Provide the opportunity to create a smaller representative subset of a database with preserved referential integrity. This allowed the bank to create a smaller synthetic version of the production data to reduce hardware consumption.
Organization: Leading Dutch Bank
Location: The Netherlands
Industry: Finance
Size: 43,000+ employees
Use case: Test Data
Target data: Core-banking data, transaction data
Website: On request
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