If your team has been tasked with digitally transforming your organization’s finance operations, you’ve probably encountered strict data privacy requirements–especially if you work for a global company. Your CISO may insist that no financial data can leave your environment. Your AI governance council will likely demand complete transparency in AI training. And regulatory compliance requires strict data boundaries. Thankfully, there’s a way to satisfy those mandates and rigorous data protection standards: Federated Learning.

The AI data privacy challenge

You likely already know AI automation will save you a full day’s work each week, improve communication with managers and suppliers, and catch potential fraud no one else could find. But without a clear way to isolate your company’s sensitive data, you’re done before you begin.

You’re not alone–this is the reality for many enterprises, especially in highly regulated industries. In financial services, healthcare, and government sectors, the promise of AI comes with significant hurdles. Common hurdles include:

Strict data privacy regulations may require complete isolation of financial data
AI governance councils demand transparency in how AI models are trained
Centralized, shared data required by traditional AI creates potential security risks
Organizations need AI automation but can’t compromise on data security

Federated Learning helps organizations with strict AI governance policies enable automation while upholding the highest protections for sensitive data.

What is Federated Learning?

Federated Learning allows the training of AI models without sharing your data. This technique sends the model to the data, not the other way around. Only algorithm updates return to improve the central model, while raw information stays secure. The result is an AI model with continuously improving insights that maintains data isolation.

This approach is growing in popularity within organizations and industries that must meet AI Council mandates and compliance requirements. Hospitals, for example, can train models locally without centralizing patient records. Banks and finance teams can improve fraud detection without sharing transactions. Manufacturers can enhance equipment monitoring without exposing operational data. By keeping information in place, organizations are able to maintain regulatory compliance and build customer trust.

Federated Learning solves the key problem of organizations with strict AI governance policies: how to enable automation while keeping sensitive data within a learning environment specific to that company. 

AppZen’s Private ZenLM uses Federated Learning

AppZen’s Private ZenLM with Federated Learning lets enterprises adopt AI tools with enhanced data privacy. AppZen’s solution brings the model to your data, ensuring sensitive information never leaves your isolated instance.

Our innovative “base model plus customer adapters” approach means:

Your data stays isolated within your environment
AI models train exclusively on your financial data and business patterns
No data mixing or sharing across organizational boundaries
You maintain complete control over your sensitive information

By reimagining how AI models learn and adapt, AppZen has created a solution that aligns with the strictest enterprise security requirements while delivering powerful AI automation capabilities.

Let’s look at how this revolutionary approach can work within accounts payable operations.

AP automation with Private ZenLM

The Private ZenLM approach to Federated Learning uses a unique architecture in which each customer operates within a dedicated learning space. AppZen’s Public ZenLM language model provides proven AI capabilities and security, while customer-specific adapters train exclusively on the proprietary data within the isolated partition. Training data never flows back to the base model and is never shared across organizational boundaries.

Real value for enterprise organizations

While data privacy is a critical benefit, the real value of Private ZenLM’s Federated Learning extends far beyond security. Organizations taking this approach discover transformative benefits across their entire finance operation.

Enhanced security with the isolation of sensitive financial data, including vendor contracts, pricing, and payment terms
Regulatory compliance is made easier for companies with the strictest data privacy requirements
Accuracy improvements with models that learn from your specific business patterns, for more accurate fraud detection and improved compliance
Reduced risk in enabling digital transformation with the ability to maintain the isolation of sensitive data

As organizations continue their digital transformation journeys, AppZen’s Private ZenLM sets a new standard for secure AI adoption in enterprise finance. It enables companies to use the power of AI automation while helping them satisfy both AI Council mandates and strict data privacy and compliance requirements.

Our commitment to data security

It’s important to note that AppZen’s standard AI training, or Public ZenLM, already operates at the highest level of enterprise security. Our core platform maintains rigorous data privacy standards, with complete anonymization and secure handling in our controlled environment. This approach to handling sensitive financial data has earned the trust of the world’s largest companies.

Federated Learning builds upon this foundation, offering an additional isolation layer for those organizations with the strictest data privacy requirements. We are committed to providing options that align with varying data privacy mandates while maintaining the full power of AI automation.

Finance AI automation doesn’t require compromising on data security. Whether using Public ZenLM or Private ZenLM, organizations can confidently embrace AI transformation with the confidence that their sensitive financial data is protected.