mwandirakings-prog/malawi-banking-analytics

Financial Performance and Credit Risk Analytics Dashboard for Malawi Banking Sector # Malawi Banking Analytics Dashboard ### Financial Performance & Credit Risk Analytics Platform --- ## Project Overview A fully deployable, enterprise-grade analytics platform that monitors the financial performance of Malawi's major commercial banks and assesses credit risk using advanced machine learning models. This project is built to international standards including: - Basel III/IV capital adequacy framework - IFRS 9 Expected Credit Loss (ECL) modeling - SHAP model explainability (EU GDPR compliant) - Production-grade cloud pipeline architecture --- ## Banks Covered | # | Bank | |---|------| | 1 | National Bank of Malawi | | 2 | Standard Bank Malawi | | 3 | First Capital Bank | | 4 | NBS Bank | | 5 | FDH Bank | | 6 | Ecobank Malawi | **Period:** 2018 — 2023 --- ## Technology Stack | Layer | Tools | |-------|-------| | Data Collection | Python, pdfplumber, pandas | | Machine Learning | XGBoost, Scikit-learn, SHAP | | Statistical Modeling | R, ggplot2, caret | | Visualization | Power BI, matplotlib, seaborn | | Cloud Pipeline | AWS Lambda, Apache Airflow | | Data Warehouse | Google BigQuery | | Engineering | GitHub, Docker, pytest | --- ## Model Performance | Metric | Score | |--------|-------| | AUC-ROC | **0.8161** | | Gini Score | **0.6323** | | CV Mean AUC | **0.8184** | | Training Records | 100,000 | --- ## Project Structure malawi-banking-analytics/ ├── data/ │ ├── raw/ # Source data from RBM and banks │ ├── processed/ # Cleaned data and model outputs │ └── synthetic/ # 100,000 synthetic loan records ├── src/ │ ├── data_collection.py # CAMELS ratio calculation │ ├── synthetic_loans.py # Loan data generator │ └── credit_risk_model.py # XGBoost + SHAP model ├── notebooks/ # Jupyter EDA notebooks ├── tests/ # pytest unit tests ├── airflow/ # Pipeline DAG definitions └ …