Smart Conversations, Smarter Decisions: Integrating SAS Intelligent Decisioning with Azure OpenAI
What if smarter decisions came from natural conversations? Imagine an AI assistant that not only listens to you but drives critical workflows—flawlessly calculating risk ratings, explaining complex outcomes, and ensuring decisions align with trusted, replicable standards. By integrating Azure OpenAI conversational capabilities with SAS Intelligent Decisioning, the approach in the post blends the power of conversational AI and advanced analytics into a unified experience. From secure login to rule-based decisioning, this AI-powered system tackles complex decision-making with speed, transparency, and reliability. Want to know how this changes the game? Watch the demo, explore the innovation, and discover how smarter decisions may reshape the future!
The Essential
Imagine chatting with a GPT model—quick responses, dynamic interactions, and intuitive conversations. But when it comes to calculating something as critical as a risk rating, would you trust it blindly? Of course not! Instead, picture this: redirecting the conversation at the perfect moment to a robust internal system, like SAS Intelligent Decisioning, which handles risk calculations with proven reliability and precision.
By integrating powerful AI tools like Azure OpenAI Assistants and SAS Intelligent Decisioning through a few Python-based applications, organizations gain the ability to automate complex decision-making workflows without much effort.
Not to mention that AI becomes Trustworthy AI.
Most Important Points
Watch the demonstration—hopefully, the approach becomes self-explanatory. If it doesn’t, well, I guess I’ll need to sharpen my explanation skills! 😊
Understanding why this solution is impactful requires looking at its main features:
- Secure Login: Credit officers, like Shannon in the demo, log in securely using Azure credentials to ensure that sensitive data stays protected.
- Conversational AI Assistant: Users interact conversationally via Azure OpenAI, submitting financial and personal inputs such as age, income, credit score, and debt-to-income (DTI) ratio.
- Integrated Decisioning: The AI assistant passes the collected information to SAS Intelligent Decisioning, which calculates risk ratings using advanced rule sets and thresholds.
- Transparency Mode: Upon request, the assistant explains the exact calculations for each variable, ensuring credit officers can confidently justify decisions to customers.
- Scalable Deployment: Front-end and back-end applications (built with Python and Streamlit) deployed on Azure enable flexibility for cloud-based integration.
This solution isn’t just limited to banking—it can help transform decision-making across other industries.
What’s New and Novel About This Approach
What makes this solution stand out is its emphasis on transparency and its ability to merge Azure OpenAI's conversational AI with real-time decisions powered by SAS Intelligent Decisioning.
We explored an integration using Azure Logic Apps and a SAS Container Runtime in From Chat to Decision: Building an AI Assistant with SAS Intelligent Decisioning and Azure. However, this time, the integration is direct, the AI Assistant calls the decision deployed in SAS Micro Analytic Service, in SAS Viya.
Here’s what sets it apart:
- Dynamic Interactions: The AI assistant doesn’t just chat—it collects and processes user inputs, performs complex calculations behind the scenes, and delivers clear explanations of the outcomes.
- Implicit Data Management: Users can provide inputs in nearly any format. Transforming that data into the correct JSON structure to score the decision is handled entirely by the Azure AI Assistant. It even translates JSON responses back into user-friendly paragraphs, eliminating the need for traditional ETL processes.
- Advanced Rule-Based Modeling: SAS Intelligent Decisioning enables trustable, replicable, governed risk calculations based on predefined rules, or custom logic.
- Transparency in Decision-Making: The system doesn’t just compute—it explains. By leveraging decision metadata, the assistant breaks down how calculations are derived, ensuring users understand the reasoning behind every outcome.
- Versatility Across Industries: While demonstrated in banking, this approach is adaptable to sectors like healthcare, insurance, or any field where risk and decision modeling are essential.
Summary of the Components and Their Role
Here’s a breakdown of the components involved:
- Frontend App (Streamlit): Provides an intuitive interface, backed by Python, connecting users with the AI-driven decision system.
- Backend App (FastAPI): Written in Python, the backend bridges Azure and SAS integrations, ensuring authentication, communication and data exchange.
- Azure OpenAI Assistant with function calling feature: Acts as the user-facing interface, facilitating conversational interactions, gathering inputs, and ensuring clear communication.
- SAS Intelligent Decisioning: Processes risk calculations using predefined rules deployed to the SAS Micro Analytic Service, which performs high-speed analytics accessible via REST APIs.
- Secure Infrastructure: By leveraging Azure authentication and SAS Viya authentication, sensitive customer data is safeguarded during processing.
Conclusion
The integration of an AI Assistant with a SAS Intelligent Decisioning decision deployed via SAS Micro Analytic Service, could very well represent the future of decision-making in one alternative universe. By combining conversational AI with advanced analytics, it delivers insights that are transparent, reliable, and lightning-fast.
The demo highlighted how a credit officer can securely interact with the assistant, submit input data, receive a calculated risk rating, and even request detailed explanations—all seamlessly powered by Azure OpenAI and SAS Intelligent Decisioning.
We also took a step further, by packaging it all as sleek web apps deployed in the Azure cloud for ultimate flexibility and scalability.
Thank you for exploring this exciting technology with me. In future posts, we will explore what is hidden behind the frontend and the backend app. Stay tuned for more innovations with SAS Viya.
If you liked the post, give it a thumbs up! Please comment and tell us what you think about the AI Decisioning Assistant. If you wish to get more information, please write me an email.
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