Scaling financial trust with intelligent data agents
What if we could fundamentally shift how we ensure trust in financial systems?
Rather than adding more bespoke code and narrowly focused solutions for each risk or compliance issue, what if we could create a dynamic, intelligent system that proactively identifies and mitigates risk and compliance issues?
This question drove my exploration of data agents using Hasura's technology, with results that point toward an exciting north star for financial integrity.
The challenge: achieving scalable transparency
The financial services industry generates enormous volumes of data containing valuable signals about potential risks. However, extracting these signals effectively and scaling this process has remained elusive. I wanted to explore whether AI could evolve beyond simple pattern recognition to become a critical partner in ensuring financial integrity, while being rapidly deployable for new challenges.
My goal wasn't to solve just a single, narrow problem. Instead, I wanted to create a fundamentally adaptable system capable of addressing a wide spectrum of financial challenges.
Why current solutions fall short
Financial institutions have long employed fraud detection and compliance systems, but these solutions suffer from a critical weakness: They require bespoke development for each specific problem, creating siloed solutions with limited flexibility.
Imagine building an entirely new system each time a regulatory requirement changes or a novel fraud pattern emerges – this is the reality for many organizations.
The Hasura advantage
Hasura Data Delivery Network (DDN) and PromptQL data agent technology offer a paradigm shift. By leveraging semantic understanding and metadata, we can create a generalized framework adaptable to numerous problems. Instead of building individual solutions, Hasura provides a platform for rapid deployment and easy modification, which is crucial in today's dynamic financial landscape.
For my proof of concept, I focused on detecting fiduciary misconduct – a powerful test case that demonstrates how this approach can be applied to complex compliance challenges. This example served to validate the broader vision of a flexible, adaptable system.
The challenge lies in ensuring this approach is repeatable, completely accurate, and explainable – areas where current GenAI often falls short. By focusing on meticulous data modeling, rich metadata, and traceable analytical processes, Hasura's technology helps overcome these limitations to build a foundation of trust.
Building trust, not just speed
In finance, "black box" AI solutions aren't acceptable. That's why I emphasized testability and repeatability with Hasura's technology. By meticulously modeling data relationships and ensuring rich metadata, we created a system that can be audited and validated at every step. This metadata and semantic analysis make it easier to understand how the system reaches its conclusions.
Empowering human analysts
One exciting aspect of this project was seeing how Hasura's data agents augment human intelligence. Rather than replacing analysts, they free them from tedious, repetitive tasks. Analysts can focus on defining problems and refining analytical prompts instead of building and maintaining complex systems, enabling faster, more informed decision-making.
Looking ahead
Hasura's data agent technology has the potential to revolutionize financial services. Beyond detecting misconduct after the fact, we can predict and prevent it. While specialized risk detection systems exist today, they're often limited in scope and resource-intensive to expand.
Hasura's data agents offer the potential to achieve this at scale, enabling institutions to rapidly deploy and adapt risk detection across a broad range of issues.
Practical takeaways
Start by building robust data models and ensuring high-quality metadata.
Prioritize testability and repeatability to build trust in your AI systems.
Empower human analysts to define problems, not build solutions.
Engage in open discussions about the ethical implications of AI in finance.
Explore use cases beyond compliance, emphasizing Hasura's capabilities for rapid adaptation.
See it in action
I've created a demonstration video showcasing this proof of concept using Hasura DDN and PromptQL technology. You'll see firsthand how intelligent data agents can transform financial compliance and risk management through scalable, adaptable approaches.
The demonstration highlights how these technologies enable rapid deployment for new risk scenarios while maintaining the accuracy and explainability crucial for effective financial systems.
