Kris Dickinson
Director, Financial Services
In today’s world, data truly makes the world go around. The fintech world is no exception. Fintech startups working on cutting-edge innovative use-cases need access to various kinds of data in order to build, test, and refine their products for market readiness.
Owing to the highly regulated nature of the financial services industry and the complex maze of cumbersome legacy systems, access to data is a big obstacle for fintech startups in their innovation process. While the regulators across various regions are attempting to bring Open Banking legislation to improve data sharing mechanisms between banks and FinTechs, the adoption is yet to become mainstream. Moreover, financial institutions have to increasingly comply with developing data protection laws such as Europe-wide General Data Protection Regulation (GDPR), Payment Card Industry Data Security Standard (PCI-DSS), California Consumer Privacy Act (CCPA), Data Protection Act (DPA, UK), Health Insurance Portability and Accountability Act (HIPAA, US), etc. Along with this, the datasets in financial institution data servers are spread across several systems and business units which involves managing different data formats and data fragmentation scenarios in order to prepare a single source of truth to aid the data analysis use-cases for fintech innovation.
Several other datasets regarding consumer demographics and behavior may be available for purchase but come with hefty price tags. Moreover, real-life datasets do not provide flexibility in running specific scenarios that may require tweaking datasets to meet the requirements of a specific use case that needs to test extreme conditions such as market crashes or app failures.
Modern Approach to Data
Synthetic data generators essentially create new datasets that have all the same statistical characteristics and patterns of the real data but are secure in a manner that it is completely impossible to trace or recreate the original datasets by using either synthetic datasets or synthetic generators. Hence, the Digital Twin datasets have the same utility and relevance for innovative use-cases as the original datasets, but none of the security and privacy concerns that are associated with using real-life data sets.
Digital Twin to Accelerate FinTech Innovation
Digital Twin unlocks a new archetype for creating or sharing data quickly and securely to accelerate the experimentation and validation of commercial innovations in the financial services industry.
It allows fintechs to access several kinds of data such as consumer data, enterprise data, and industry data to test and validate their innovative solutions. The speed and scalability of generating synthetic datasets are additional value-adds that help fintechs reduce the time to market and allow them to experiment with various alternate scenarios cost-effectively. Digital Twin also allows the flexibility to inject multiple scenarios to generate different dynamic datasets to test out a gamut of alternate scenarios during development and quality assurance stages to ensure innovation use-cases perform well in various real-life scenarios and business events.
Digital Twin for Open and Collaborative Innovation
As the fintech industry matures both in terms of customer adoption and regulatory acceptance, it has given rise to fintech infrastructure startups that are enabling both incumbents and insurgents to launch new products and services. These fintech infrastructure startups are innovating across several use-cases such as digital onboarding, credit underwriting, claims management, KYC, robo-advisors, card processing, digital compliance, etc.
NayaOne’s Digital Twin offering promises to be an excellent solution to serve this growing segment of FinTech infrastructure capability providers. It allows FinTech startups to test their solutions quickly and securely while unlocking the doors to collaborate with financial institutions, which may find it difficult to share their data with the fintechs, owing to their compliance imperatives or legacy technology constraints. The availability of ready-to-use synthetic datasets also helps FinTechs control the number of people required to prepare data for experimentation and enables them to innovate freely without worrying about data leakage risks.
