Deutsche Bank has invested in Synthesized’s technology innovation, as the data generation platform offers access to synthetic data for testing purposes.
Deutsche Bank is already partnering with Synthesized to leverage data and accelerate the adoption of client insights driven by artificial intelligence (AI)/machine learning (ML), while at the same time protecting data privacy and security. Via Synthesized, the bank’s engineering teams have access to the synthetic test data they need – speeding up testing, driving more accurate outcomes, and shortening time to market. Through synthetic, non-traceable versions of original datasets, the platform will enable the bank to experiment with large data sets for AI/ML use cases and additional new technologies.
Deutsche Bank provides commercial and investment banking, retail banking, transaction banking, and asset and wealth management products and services to corporations, governments, institutional investors, small and medium-sized businesses (SMBs), and private individuals.
The investment in Synthesized further accelerates Deutsche Bank’s application migration, data analysis, experimentation, and testing in the cloud – enabling the bank to increase its data productivity and innovation velocity.
Key benefits delivered by Synthesized
Synthesized aims to make the creation and access of high-quality data fast and easy. As mentioned in the official press release, Synthesized created an API-driven data generation platform that creates synthetic data in minutes.
Synthesized is enabling users to produce synthetic data that has an overall statistical resemblance to original data but consists of entirely new data points. This helps financial institutions to ensure full data privacy and security for their cloud transformation initiatives.
Some of Synthesized key benefits include:
Improve ML model performance and resilience – creating more balanced training data for model testing;
Increase ML model Time to Value – shortening data collection cycles;
Open innovation, experimentation, and partnering – reducing vendor onboarding and data approvals for POCs;
Drive compliant data monetization strategies – using irreversible datasets;
Facilitate cloud adoption strategies – providing compliant datasets for migration to GCP;
Enable faster development cycles – increasing time to market;
Improve application quality – shifting left to catch defects before production rollouts.
The use of synthetic data in financial services
The future of banking is all about becoming AI-first and creating digital services coupled with tight cybersecurity. AI has a diverse set of applications in financial services from process automation to chatbots and fraud detection. The estimates show that aggregate potential cost savings for banks from AI applications would be USD 447 billion by 2023.
However, some of these applications have their limitations because financial data is one of the most sensitive and personally identifiable data types. To solve this, financial institutions can leverage synthetic data, or data that is generated artificially based on real data, to overcome privacy challenges and provide innovative products and services to their customers.
Synthetic data can eliminate the risks of sharing. Instead of the original dataset, financial institutions can share synthetic data that preserves the important characteristics of the original dataset. Synthetic data generation techniques can be applied to a wide range of data types, from tabular to time series and artificial images.