Course Overview
🎓 Course Title: Generative AI with Financial Technology
Duration: 12 Weeks (3 Months)
Level: Intermediate to Advanced
Mode: Online (with Projects & Certificate)
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Course Objectives:
- Understand core concepts of Generative AI (GANs, Transformers, Diffusion Models)
- Apply AI to algorithmic trading, fraud detection, risk modeling, and chatbots
- Build fintech prototypes using real-time datasets
- Explore regulatory & ethical implications
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Course Modules:
📘 Week 1–2: Foundations
✅ Module 1: Introduction to Generative AI
- What is Generative AI?
- Types: GANs, VAEs, Transformers, Diffusion Models
- Real-world use cases
✅ Module 2: Fintech Fundamentals
- Overview of FinTech Ecosystem
- Financial systems, APIs, and datasets
- Regulatory frameworks (KYC, AML, GDPR)
🧰 Week 3–4: Core AI Tools & Stack
✅ Module 3: Tech Stack Setup
- Python, PyTorch/TensorFlow, Hugging Face
- Working with financial APIs (Alpaca, Yahoo Finance, OpenBB)
✅ Module 4: Data in FinTech
- Financial time series
- Market data preprocessing
- Synthetic data generation using GANs
⚙️ Week 5–6: Generative AI in Action
✅ Module 5: AI-Powered Trading Systems
- Reinforcement learning for stock trading
- Forecasting with LSTMs and Transformers
- Backtesting strategies
✅ Module 6: Fraud Detection and Risk Scoring
- Anomaly detection with Autoencoders
- Generative data augmentation for fraud cases
- AI for credit scoring
🧪 Week 7–8: Advanced Applications
✅ Module 7: Financial Chatbots & NLP
- GPT-based financial advisors
- Embeddings for financial question answering
- RAG (Retrieval Augmented Generation) for compliance bots
✅ Module 8: Report Generation with AI
- Auto-generating earnings reports
- Summarizing SEC filings with LLMs
- Generative Excel assistants with OpenAI API
🏗️ Week 9–10: Project Phase
✅ Module 9: Capstone Project Selection
- Choose one:
• AI-powered robo-advisor
• Risk modeling system using synthetic data
• Personalized financial chatbot
• Predictive fraud prevention platform
🚀 Week 11–12: Deployment & Ethics
✅ Module 10: Deployment + Ethics
- Deploying models with Streamlit, FastAPI, or Gradio
- Fintech app integration via RESTful APIs
- Bias, fairness, auditability in financial AI
- Responsible AI & FinTech regulations
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Tools & Technologies Used:
- Python, Hugging Face, OpenAI API
- LangChain, Gradio, Streamlit
- Financial APIs (Alpaca, Plaid, Stripe, Quandl)
- MongoDB / PostgreSQL for financial data
- AWS / Google Cloud for deployment
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Assessment & Certification
- Weekly quizzes and mini-projects
- Final capstone demo + review
- Certificate of Completion
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Extras:
- Access to AI-fintech Discord community
- Guest lectures from industry experts
- Portfolio-ready project showcase
