Deep Learning-Based Credit Scoring Model: Principles and Implementation

Deep Learning-Based Credit Scoring Model: Principles and Implementation

I. Problem Description
Credit scoring is one of the core tasks in fintech, aiming to predict a user's default probability by analyzing their historical data (e.g., income, liabilities, repayment records). Traditional methods (e.g., logistic regression, decision trees) rely on feature engineering and linear assumptions, whereas deep learning models can automatically capture complex non-linear relationships, making them particularly suitable for high-dimensional sparse data (e.g., multi-source behavioral data). This section will explain how to build a credit scoring model using deep learning, focusing on data preprocessing, model design, training optimization, and interpretability handling.

II. Detailed Solution Steps

1. Data Preprocessing and Feature Engineering

  • Missing Value Handling:
    • Numerical features (e.g., income) are filled with the median, while categorical features (e.g., occupation) are filled with the mode or an "unknown" category.
    • If the missing rate exceeds 30%, consider directly removing the feature.
  • Outlier Handling:
    • For continuous variables (e.g., debt-to-income ratio), use the IQR (Interquartile Range) method to detect outliers and apply Winsorization or truncation.
  • Feature Encoding:
    • Categorical features (e.g., education level) are encoded using One-Hot Encoding or Target Encoding. The latter maps based on the mean of the target label corresponding to the category to avoid dimensionality explosion.
  • Feature Standardization:
    • Apply Z-score standardization to numerical features (e.g., age, income) to stabilize model training.

2. Model Selection and Architecture Design

  • Basic Architecture: Use a Fully Connected Neural Network.
  • Input Layer: The number of nodes equals the feature dimension (e.g., 50 dimensions after processing).
  • Hidden Layer Design:
    • Number of layers: 2~3 layers (to avoid overfitting), with the number of neurons decreasing layer by layer (e.g., 128→64→32).
    • Activation function: Use ReLU for hidden layers due to its fast convergence and mitigation of gradient vanishing.
  • Output Layer:
    • For binary classification (default/non-default), use 1 neuron with a Sigmoid activation function to output the default probability.
  • Regularization Measures:
    • Add Dropout layers (dropout rate 0.2~0.5) to randomly disconnect some neurons and reduce overfitting.
    • Apply L2 regularization (weight decay) to the weights.

3. Loss Function and Evaluation Metrics

  • Loss Function: Binary Cross-Entropy, with the formula:
    \(L = -\frac{1}{N} \sum_{i=1}^N [y_i \log(p_i) + (1-y_i) \log(1-p_i)]\)
    where \(y_i\) is the true label and \(p_i\) is the predicted probability.
  • Evaluation Metrics:
    • AUC-ROC: Measures the model's ranking ability (placing high-risk users before low-risk users).
    • KS Value: Evaluates the maximum separation between positive and negative samples; in financial scenarios, KS > 0.3 is typically required.

4. Model Training and Optimization

  • Optimizer Selection: Use the Adam optimizer, which adaptively adjusts the learning rate and incorporates momentum to accelerate convergence.
  • Learning Rate Scheduling: Set the initial learning rate to 0.001. If the validation loss does not decrease for 3 consecutive epochs, reduce it by half.
  • Class Imbalance Handling:
    • If default samples account for only 5%, use oversampling (SMOTE) or weight the loss function (e.g., increase the weight of default samples by 10 times).
  • Early Stopping: Monitor the validation AUC and stop training if there is no improvement for 5 consecutive epochs.

5. Model Interpretability Handling

  • SHAP Value Analysis:
    • Use SHAP (Shapley Additive Explanations) to calculate the contribution of each feature to a single prediction.
    • For example, it may reveal that "number of overdue incidents in the past 3 months" is a core feature influencing default probability.
  • Global Feature Importance:
    • Rank key risk factors through feature permutation importance or average |SHAP values|.

III. Practical Considerations

  1. Data Leakage Prevention: Avoid using future information (e.g., features related to "default status in the next 3 months").
  2. Online Deployment: The model should be converted to ONNX format, deployed via API services for real-time inference, and monitored for distribution stability (e.g., using PSI metrics).
  3. Ethical Risks: Ensure features are non-discriminatory (e.g., based on gender, race) by using adversarial learning to remove the influence of sensitive attributes.

IV. Summary
Deep learning credit scoring models improve prediction accuracy through end-to-end learning but must balance data quality, overfitting control, and interpretability. Future optimizations may involve integrating graph neural networks (e.g., incorporating user social relationships).