Perficient’s Generative AI Lab, part of our larger Generative AI Innovation Group, is consistently exploring and implementing use cases for generative AI and helping clients operationalize it with policies, advocacy, controls, and enablement.

One use case organizations can consider is Synthetic Data and Document Generation, which allows companies to automate the creation of synthetic (or fake) data for testing purposes, therefore accelerating their digital transformation initiatives.

What Is Synthetic Data and Document Generation?

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Synthetic data and document generation leverages large language models (LLMs) and GenAI to generate synthetic test data and documents so organizations can quickly populate and generate PDFs of sample documents for use in testing. Synthetic data created can include personal identifiable information (PII) such as social security numbers (SSN), dates of birth (DOB), policy/account numbers, and more.

Why Should Organizations Consider Synthetic Data and Document Generation?

Many organizations, particularly those within highly regulated sectors such as financial services and healthcare, face limitations in using actual production data or documents for testing purposes. This restriction arises since customers’ PII is present in such data, and test environments usually lack the requisite security safeguards. Today, this challenge is overcome by team members manually creating sample documents with fictional names, SSNs, policy numbers, and other sensitive data – a cumbersome process that slows progress and innovation.

By utilizing synthetic data and document generation, companies can expect these benefits:

• Increase efficiency – By removing the tedious process of generating data and documents manually and replacing it with an automated approach, organizations can be more agile and efficient. This results in better testing and enabling of their technology teams to focus on development and engineering tasks, not data/document creation.
• Reduce risk of downstream PII use – By enabling easy access to synthetic data and documents, organizations reduce the risk of team members using sensitive data in their testing processes.
• Expand AI capabilities – With more efficient testing, organizations can better train their AI models and improve and increase their capabilities.

Who Can Benefit from Synthetic Data and Document Generation?

This can benefit any highly regulated organization that handles documents including customer data like SSNs, DOBs, etc. This includes those in industries like healthcare (both payers and providers), banking, insurance, and lending. Business and technology teams within those organizations – those focused on digital transformation initiatives – will see the most benefit from utilizing this solution.

What’s Next?

Interested in how synthetic data and document generation could benefit your company, or would you like to explore other use cases for your organization? Contact us today.