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Synthetic data in banking

Synthetic financial data fuels AI/ML model building, software testing and data sharing. Banks and financial institutions need to bridge their innovation gap with AI-generated, privacy compliant synthetic transaction data. 
synthetic data for enterprise data sharing

Why do banks need synthetic data?

“For global banking, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year. ” – Building the AI Bank of the Future, McKinsey
Synthetic data plays an important role in the future of banking. Access to meaningful customer and transaction data is getting more restricted. Growing cybersecurity concerns and increasing legislative pressure are only some of the reasons. Business lines work in siloed ways, where data owners and data consumers are separate entities. Legacy systems represent a mounting challenge to data architectures. Customers demand digital personalization and privacy simultaneously. Cybersecurity concerns and full digital transformation have grown critical over the pandemic years. Synthetic data can solve all of these issues and more. 
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How can synthetic data help?

High quality synthetic data is 100% GDPR compliant, statistically representative and flexible. Generate as much or as little as you need, fix embedded biases and train models with high accuracy. MOSTLY AI's state of the art synthetic data generator handles complex data structures really well. Behavioral data, time-series data, transactions data and synthetic text are the highlights. Highly realistic synthetic test data can be generated directly from databases.

Data-driven product development

Shorter sprints, hyperrealistic banking app demos, data-driven features, such as balance prediction. Third-party product development and testing.

Privacy-preserving AI at scale

Synthetic data provides a privacy-safe, readily available building block for AI/ML exploration. Ethical AI is built on rebalanced synthetic data.


Data augmentation capabilities, such as simulations, rebalancing and smart imputation for a granular, 360-degree customer view.

Safer, cheaper and faster POCs

Banks evaluate hundreds of vendors annually. Replacing sensitive data with synthetic versions saves time, reduces costs and privacy risks.

  • “SWIFT's goal is to deliver privacy-preserving AI at scale for the Payments and Securities industry. Therefore, synthetic data will play a crucial role in machine learning exploration. Deploying and training AI models on MOSTLY AI's synthetic data generation capability is a foundational step towards our goal.”
    Deepak Janardhanan
    SWIFT’s AI Platform Lead
  • "Partnering with MOSTLY AI allowed us to experiment with Synthetic Data. We have recognized the potential values of this approach very early on, and found out the best partner in this field. We believe Synthetic Data is one of the best ways  to build powerful data-driven banking experiences, without compromising on customer privacy and being fully compliant with GDPR."
    Erste Group Research and Digital Development
    George Labs GmbH
  • “We see synthetic data as the foundation for all future data-driven development, as it provides the only GDPR-compliant method for unlocking advanced analytics and insights based on customer data."
    Dietmar Böckmann
    Managing Director, s IT Solutions, ERSTE Group
  • “Working with synthetic data, we can develop and test our services in a much more sophisticated manner than before, while still ensuring complete privacy protection for our customers.”
    Maurizio Poletto
    Chief Platform Officer, ERSTE Group
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What's your financial synthetic data use case? 
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