last updated @ 7/3/25
Tags: fintech· machine learning · data science · XGBoost · SHAP · streamlit · financial modeling · risk analytics · underwriting automation · portfolio scoring · quantitative finance
This project simulates a lightweight credit risk engine similar to what fintechs or prop trading firms might use for real-time underwriting, risk flagging, or capital allocation decisions.
Additionally, this project is a practical demonstration of machine learning applied to financial decision support.
This project is intended strictly for educational and demonstrative purposes.
AI systems — especially in finance — can unintentionally reinforce existing biases or create unequal outcomes if not designed and audited carefully.
If you're interested in building more ethical, transparent, and fair AI systems, here are some helpful resources:
artificialintelligenceact.eu – EU AI Act overview; defines high-risk systems
partnershiponai.org – Industry-led initiative promoting responsible AI development and deployment
wikipedia.org/wiki/Weapons_of_Math_Destruction – Book outlining real-world consequences of unchecked algorithms in finance, education, and more
This README includes SHAP-based model explanations to promote transparency — but does not implement bias mitigation or fairness auditing. Please use responsibly. Just for learning purposes
Use Case:
"Can we estimate, with transparency, how various financial factors influence lending decisions?"
This tool enables that — combining predictive insights with model explainability (via SHAP).
It transforms complex CSV data into clear, interpretable visual guidance.
Think of this app like a virtual assistant for financial analysis:
- The ML model estimates the relative risk based on patterns from past data
- It uses XGBoost — a fast, interpretable machine learning model
- It also shows why the model made its decision using SHAP (e.g., “What factors contributed to higher risk”)
Analogy:
It’s like a financial calculator with explainable reasoning — helping analysts understand risk patterns
> Note: This tool is for educational purposes and should not be used to make real-world lending decisions.
- Banks & Fintechs: Streamline risk scoring for consumer lending
- Analysts: Get transparency into why a loan is flagged as risky
- Students/Builders: Learn end-to-end ML pipeline + UI + explainability
Visual examples from the pipeline and Streamlit app. Each graphic helps explain how this tool works for both technical and non-technical audiences.
| What You See | What It Shows |
|---|---|
 |
A performance summary: how well the model predicts defaults vs non-defaults. Great for model audit snapshots. |
 |
Shows the tradeoff between precision and recall — especially helpful in imbalanced datasets like credit risk. |
 |
A global SHAP beeswarm plot that highlights which features push the model toward predicting risk. Think of this as “what the model pays attention to”. |
 |
A look at the real-time scoring UI, where users input data manually or upload a CSV to get loan risk predictions. |
kaggle dataset: https://www.kaggle.com/datasets/nikhil1e9/loan-default?resource=download
current scores (as of 7/3/25) ROC AUC: 0.7591999315970401 precision recall f1-score support
0 0.89 1.00 0.94 45139
1 0.63 0.06 0.11 5931
accuracy 0.89 51070
macro avg 0.76 0.53 0.52 51070 weighted avg 0.86 0.89 0.84 51070
Metric Meaning Accuracy 89% of total predictions were correct Macro Avg Unweighted average over both classes (fairer) Weighted Avg Average, weighted by class size (skewed by class 0)
- Run the app:
streamlit run app/streamlit.py
- Exploratory Data Analysis (EDA) – Understand correlations, outliers, and data health
- Modeling – Logistic Regression, Random Forest, and XGBoost with Grid Search
- Explainability – SHAP visualizations to satisfy regulatory transparency
- Streamlit App – Real-time prediction interface with interpretability toggle
- Python · Pandas · Scikit-learn · XGBoost
- Streamlit (UI) · SHAP (model explanations)
- Matplotlib & Seaborn (visualization)
1. Data Understanding
├─ Gather example datasets (e.g., LendingClub, Kaggle credit datasets)
├─ Explore feature types: income, credit score, loan amount, etc.
└─ Identify target variable (loan default = 0/1)
2. Exploratory Data Analysis (EDA)
├─ Correlation analysis, outlier detection
├─ Missing value imputation
└─ Visualizations: boxplots, heatmaps, histograms
3. Data Preprocessing
├─ Encoding categorical variables
├─ Normalization/Standardization
└─ Train-test split (stratified)
4. Model Development
├─ Baseline: Logistic Regression
├─ Advanced: Random Forest, XGBoost
├─ Cross-validation (e.g., StratifiedKFold)
└─ Hyperparameter tuning (GridSearchCV / Optuna)
5. Model Evaluation
├─ Metrics: ROC AUC, F1, Precision-Recall
└─ Confusion matrix visualizations
6. Interpretability
├─ Feature importance (XGBoost built-in)
└─ SHAP plots (force, beeswarm, summary)
7. Streamlit App
├─ Input form for user financial data
├─ Risk prediction output
└─ Display SHAP explanations
8. Deployment (Optional)
└─ Streamlit Cloud / Dockerize for local hosting
├── data/
│ ├── raw/
│ └── processed/
│
├── notebooks/
│ ├── 01_eda.ipynb
│ └── 02_model_dev.ipynb
│
├── src/
│ ├── data_prep.py
│ ├── train_model.py
│ ├── evaluate.py
│ └── shap_analysis.py
│
├── app/
│ └── streamlit_app.py
│
├── models/
│ └── xgb_credit_model.pkl
│
├── requirements.txt
├── README.md
└── .gitignore
-
Install Dependencies
pip install -r requirements.txt
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Activate venv source venv/bin/activate
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If retraining is needed python run_pipeline.py
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Launch UI streamlit run app/streamlit.py