This project implements an intelligent credit scoring system using neural networks to assess credit risk for banking and financial institutions. The system analyzes customer credit card behavior patterns to predict credit risk and identify potential loan defaulters.

• Source: CC GENERAL.csv
• Size: 8,950 customer records
• Features: 18 behavioral and financial indicators
• Target: Credit risk assessment (derived from payment patterns)

• Handle missing values (313 in MINIMUM_PAYMENTS, 1 in CREDIT_LIMIT)
• Create derived features:
  • Credit utilization ratio (BALANCE/CREDIT_LIMIT)
  • Payment-to-minimum ratio (PAYMENTS/MINIMUM_PAYMENTS)
  • Purchase intensity (PURCHASES/CREDIT_LIMIT)
  • Risk indicators based on payment behavior
• Normalize numerical features
• Encode categorical variables

Create credit risk categories based on:

• High Risk: Low payment frequency, high credit utilization, missed minimum payments
• Medium Risk: Moderate payment behavior
• Low Risk: Consistent payments, low utilization, frequent full payments

• Analyze feature distributions and correlations
• Identify key risk indicators
• Visualize payment patterns vs. risk levels
• Check for data quality issues

• Input Layer: 18+ engineered features
• Hidden Layers: 2-3 layers with 64-128 neurons each
• Output Layer: 3-class (High/Medium/Low Risk)
• Activation Functions: ReLU for hidden layers, Softmax for output
• Regularization: Dropout layers to prevent overfitting

• Data split: 70% training, 15% validation, 15% testing
• Early stopping implementation
• Cross-validation for robust evaluation
• Monitor training/validation loss curves

• Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC
• Feature Importance: Analyze contributing features
• Hyperparameter Tuning: Learning rate, batch size, architecture
• Comparison: Compare with traditional methods

• Create prediction pipeline
• Save trained model and preprocessing steps
• Build API for real-time credit scoring
• Document model performance and limitations

• Real-time Scoring: Instant credit risk evaluation for new applications
• Portfolio Management: Monitor existing customer risk levels
• Risk-based Pricing: Adjust interest rates based on risk scores

• Anomaly Detection: Identify unusual spending patterns
• Behavioral Analysis: Detect changes in customer behavior
• Early Warning System: Flag potential defaulters before they default

• Risk-based Segmentation: Group customers by risk levels
• Targeted Marketing: Customize offers based on risk profiles
• Retention Strategies: Focus on high-value, low-risk customers

• Basel III Compliance: Meet regulatory capital requirements
• Stress Testing: Assess portfolio resilience under stress scenarios
• Reporting: Generate risk reports for regulatory bodies

• Python 3.7+
• TensorFlow/Keras
• Pandas, NumPy
• Scikit-learn
• Matplotlib, Seaborn
• Jupyter Notebook (optional)

credit-neural-networks/
├── README.md
├── CC GENERAL.csv
├── data_preprocessing.py
├── exploratory_analysis.py
├── neural_network_model.py
├── model_evaluation.py
├── requirements.txt
└── notebooks/
    └── credit_scoring_analysis.ipynb

1. Install dependencies: pip install -r requirements.txt
2. Run data preprocessing: python data_preprocessing.py
3. Execute exploratory analysis: python exploratory_analysis.py
4. Train neural network: python neural_network_model.py
5. Evaluate model: python model_evaluation.py

• Accuracy: Overall prediction accuracy
• Precision: True positive rate for each risk category
• Recall: Sensitivity for detecting high-risk customers
• F1-Score: Harmonic mean of precision and recall
• ROC-AUC: Area under the receiver operating characteristic curve

• Reduced Defaults: Early identification of high-risk customers
• Improved Profitability: Better risk-based pricing and customer selection
• Operational Efficiency: Automated risk assessment
• Competitive Advantage: Advanced AI-powered credit scoring system