Final Capstone Project: AI in Finance & FinTech
data-science.
Course Modules:
Module 1: Project Scoping and Planning
Select your use case (e.g., trading, fraud detection, credit scoring)
Define your business problem, data needs, and system requirements
Set objectives, timeline, and success metrics
Module 2: Data Acquisition and Preprocessing
Source or simulate financial data
Clean and preprocess time series, transaction, or user data
Ensure data anonymization and compliance (GDPR, PCI DSS)
Module 3: Model Development and Evaluation
Choose an appropriate ML or DL model (e.g., XGBoost, LSTM, BERT)
Train and evaluate model performance (e.g., ROC, precision, Sharpe ratio)
Address model bias and overfitting
Module 4: Compliance, Ethics & Explainability
Document ethical and regulatory considerations
Add SHAP or LIME to explain AI decisions
Prepare an audit-ready compliance report
Module 5: Deployment & Demonstration
Package your AI model as a demo app or dashboard (e.g., Streamlit, Flask)
Include visualizations, risk scores, or recommendations
Prepare documentation, performance insights, and user interaction report
Module 6: Final Submission and Peer Review
Submit your GitHub repo, final report, and model demo
Review two peer projects and provide structured feedback
Receive Master Study certification and showcase badge
Tools & Technologies Used:
Python
Scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch
SHAP, LIME, Fairlearn
Streamlit, Flask, or Gradio (for app demo)
Pandas, Plotly, Matplotlib (for visualization)
Target Audience:
FinTech professionals and aspiring AI engineers
Data science students building finance portfolios
Developers entering banking, insurance, or wealth tech sectors
Researchers and startup founders in digital finance
Global Learning Benefits:
Create a production-ready financial AI solution
Apply end-to-end knowledge from model to deployment
Address risk, regulation, and explainability in real-world systems
Stand out with a tangible, portfolio-grade FinTech project
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