Financial Services
Modernize and unify your business processes with LLM solutions while retaining ownership of your sensitive data.
Whether you are fine-tuning your models to democratize access to data across departments or using proprietary LLM providers, ensure your crucial data has the best-in-class protection with Stained Glass.
Financial Services Use Cases
AML and Fraud Detection
Teams use Stained Glass Transform™ to collaborate on fraud and AML, KYC protocols, portfolio analytics, and regulatory reporting.
Unify Processes and Data-Driven Decisions
Financial and Insurance Organizations see unique opportunities in modernizing and unifying their data-driven decision processes using LLMs.
Stained Glass accelerates this journey by better protecting the sensitive data they need to use with state-of-the-art LLMs to achieve these business goals.
Building Applications on Foundation Models
Even when an LLM provider is chosen, data-related approval cycles for updates
can take time – resulting in delayed time-to-market for product teams building applications with foundation
models.
Stained Glass Transform alleviates concerns of exposing unprotected sensitive data to LLMs, allowing teams to bypass long approval cycles confidently.
Presentation from AI Summit in NY “How Q2 Accesses New Users for Fraud Detection with Breakthrough AI”
Q2’s Chief Data Scientist applies Protopia AI’s technology to enable check fraud detection for their financial institution customers – by giving them access to state‑of‑the‑art computer vision‑based fraud detection.
Latest News & Articles

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MGT, Protopia AI, and HPE Launch AI Experience Center to Accelerate Secure AI across State, Local, and Education Government (SLED)
MGT, Protopia AI, and HPE introduce a national AI Experience Center for SLED agencies to scale governed AI while protecting sensitive data.

Private tool calling for agentic AI with Stained Glass
Secure your agentic AI workflows with private tool calling using Stained Glass. Protect sensitive data in tool schemas, arguments, and outputs with end-to-end privacy.