https://github.com/micaelleos/ibm-hackathon-complyflow
ComplyFlow automates regulatory impact analysis using AI, streamlining compliance workflows for financial institutions. Built with LangChain, Streamlit, and IBM Granite. This project was developed as part of the Generative AI Hackathon with IBM Granite, hosted by IBM and LabLabAI.
https://github.com/micaelleos/ibm-hackathon-complyflow
ai granite langchain python streamlit
Last synced: 7 months ago
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ComplyFlow automates regulatory impact analysis using AI, streamlining compliance workflows for financial institutions. Built with LangChain, Streamlit, and IBM Granite. This project was developed as part of the Generative AI Hackathon with IBM Granite, hosted by IBM and LabLabAI.
- Host: GitHub
- URL: https://github.com/micaelleos/ibm-hackathon-complyflow
- Owner: micaelleos
- Created: 2025-02-23T10:03:16.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-02-24T13:22:18.000Z (7 months ago)
- Last Synced: 2025-02-24T21:05:58.404Z (7 months ago)
- Topics: ai, granite, langchain, python, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 202 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
# IBM Hackathon - ComplyFlow: AI-Powered Regulatory Compliance
## 🚀 About the Project
**ComplyFlow** is an AI-powered platform designed to **automate and streamline regulatory impact analysis** for financial institutions. Built using **LangChain, Streamlit, and IBM Granite**, this tool simplifies compliance workflows by automatically analyzing new regulations, assessing their impact, and generating action plans.
This project was developed as part of the **Generative AI Hackathon with IBM Granite**, hosted by **IBM and LabLabAI**.
## 🎯 Key Features
✅ **Automated Regulation Analysis** – AI reads and summarizes regulatory documents.\
✅ **Regulatory Impact Assessment** – Identifies affected areas and generates a compliance matrix.\
✅ **Dynamic Workflow & Approvals** – Ensures structured multi-team collaboration.\
✅ **Automated Action Plan Generation** – Streamlines compliance updates and implementation.\
✅ **Real-Time Document Editing** – Users can interact and refine the AI-generated analysis.\
✅ **AI Chatbot for Compliance Assistance** - Users can interact with Compliance AI assistent.
## 🛠️ Tech Stack
- **LangChain** – For AI-driven document analysis and impact assessment.
- **Streamlit** – To create an interactive and user-friendly interface.
- **IBM Granite AI Model** – For NLP-powered regulatory processing.
- **Python** – Core language for backend processing.
## 🔄 Workflow Process
1️⃣ **Regulation Reception** – The user upload the regulatory document for ingestion into the system.\
2️⃣ **Initial Analysis & Summary** – AI extracts key requirements and summaries.\
3️⃣ **Regulatory Impact Assessment** – AI Identifies affected business areas and compliance needs.\
4️⃣ **Approval Process** – All impacted areas (Compliance, Legal, Risk, IT, Operations) must validate the assessment.\
5️⃣ **Action Plan Creation** – AI generates a structured plan for implementation.\
6️⃣ **Final Approvals** – Ensures all stakeholders validate the compliance strategy.\
7️⃣ **Policy & Procedure Updates** – AI assists in drafting updated policies for compliance.
## 📦 Installation & Usage
To run **ComplyFlow** locally, follow these steps:
```bash
# Clone the repository
git clone https://github.com/micaelleos/complyflow.git
cd complyflow
# Install dependencies
pip install -r requirements.txt
# Run the Streamlit app
streamlit run directory.py
```
## 🚀 Future Enhancements
- 🌐 **Multi-Language Support**
- 📊 **Advanced Risk Scoring System**
- 🔗 **API Integrations with Compliance Databases**
## Hackathon Participation
This project was developed for the **Generative AI Hackathon with IBM Granite**, focusing on **efficiency, automation, and smarter business operations**.
## 🤝 Contributing
Pull requests are welcome! For major changes, please open an issue first to discuss improvements.
## 📜 License
MIT License - see [LICENSE](LICENSE) file for details.
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💡 *Developed with ❤️ for the IBM x LabLabAI Hackathon.*