Generative AI for Risk-Aware Decision Making

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  GenerativeRiskAI System Architecture – showing data flow, scenario generation, and decision analysis modules

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  GenerativeRiskAI Interactive Dashboard – part of the live demo, visualizing scenarios and outcomes

Inspiration

The project was inspired by the need to make better decisions under uncertainty. Many systems, from financial markets to personal planning, face unpredictable events. Traditional prediction models often fail to capture multiple possible futures. I wanted to build a tool that explores different scenarios, helping users make robust decisions instead of relying on a single-point prediction.

What it does

GenerativeRiskAI simulates multiple plausible future scenarios from historical data. Users can compare different strategies and see which decisions are most robust under uncertainty, allowing for more informed and safer choices.

How we built it

Languages & Frameworks: Python, PyTorch, TensorFlow
Data Handling: Pandas, NumPy
Visualization & Dashboard: Streamlit, Plotly, Matplotlib
Data Sources: Public financial and economic datasets
Approach: Lightweight generative models (VAE / Transformer) to create multi-horizon scenarios

Challenges we ran into

• Generating realistic synthetic scenarios with limited historical data
  
• Designing an interface simple enough for users to explore outcomes intuitively
  
• Ensuring computational efficiency to run simulations in real-time
  

Accomplishments that we're proud of

• Built a functional MVP capable of generating multiple scenarios
  
• Designed an interactive dashboard to visualize strategies and risks
  
• Created a system that emphasizes decision robustness rather than single-value prediction
  

What we learned

• Applied generative AI to simulate complex, stochastic systems
  
• Improved skills in Python, AI modeling, and interactive data visualization
  
• Learned how to translate theoretical AI concepts into a practical, usable tool
  

What's next

• Expand the model to include more diverse datasets and variables
  
• Improve scenario realism and computational performance
  
• Add more visualization features for deeper insights
  
• Potentially adapt the system to other domains like supply chain, healthcare, or climate risk
  

Built With

• built-with:-python
• matplotlib
• plotly
• public-datasets
• pytorch
• streamlit