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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 synthetic data.
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Why do banks need synthetic data?

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. 

How can synthetic data help?

High quality synthetic data is 100% GDPR compliant, 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.

What's your synthetic data use case? 

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