Dynamic Underwriting Agent

Inspiration

I wanted to bridge the gap between AI-driven intelligence and traditional enterprise automation. My goal was to make financial underwriting faster and more accurate.

What it does

The Dynamic Underwriting Agent automates financial data analysis by using AI to evaluate risk factors like Debt-to-Income (DTI) ratios and credit scores. It determines if an application should be approved, mitigated, or escalated.

How we built it

The project uses a two-layer architecture:

• Agentic Backend: Built with Python, CrewAI, and LangChain to handle decision-making logic.
• Automation Layer: Built with UiPath to orchestrate the process, manage cloud queues, and trigger the AI engine.

Challenges we ran into

Integrating a Python-based agentic backend with UiPath's automation ecosystem required careful configuration of web services and API endpoints.

Accomplishments that we're proud of

I successfully created a seamless pipeline where UiPath retrieves real-world financial data and hands it off to an AI agent for high-level reasoning.

What we learned

I gained deep experience in orchestrating AI agents within enterprise workflows and using UiPath's web integration capabilities.

What's next for Dynamic Underwriting Agent

I plan to add more complex risk assessment models and enhance the reporting features to provide deeper insights for underwriters.

Built With

• crewai
• gemini
• generative-ai
• langchain
• machine-learning
• python
• uipath