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.
Why
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.
How
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.
