Course Outline:

Module 1: Introduction to Credit Scoring and Predictive Modeling

• Overview of credit scoring and its importance in lending decisions
• Introduction to predictive modeling techniques for credit scoring
  • Types of credit scoring models (e.g., logistic regression, decision trees)
  • Importance of model interpretability in credit scoring

Module 2: Data Pre-processing and Feature Engineering

• Data pre-processing techniques: cleaning, transformation, and normalization
  • Handling missing values
  • Outlier detection and treatment
• Feature engineering methods for extracting meaningful features from credit data
  • Feature scaling
  • Feature selection techniques (e.g., correlation analysis, recursive feature elimination)

Module 3: Model Building and Selection

• Overview of regression and classification algorithms for predictive modeling
  • Logistic regression
  • Decision trees and ensemble methods (e.g., random forests, gradient boosting)
• Model selection techniques: cross-validation, hyperparameter tuning
  • Grid search
  • Random search

Module 4: Model Evaluation and Validation

• Techniques for evaluating model performance: accuracy, precision, recall, and F1-score
  • Confusion matrix
  • ROC curve analysis
• Model validation methods: holdout validation, k-fold cross-validation
  • Stratified k-fold cross-validation
  • Leave-one-out cross-validation

Module 5: Model Deployment and Implementation

• Strategies for deploying predictive models in credit scoring applications
  • Model deployment architectures (e.g., batch scoring, real-time scoring)
  • Integration with existing credit scoring systems
• Monitoring model performance and adapting to changing credit dynamics
  • Model monitoring metrics (e.g., drift detection, model decay)
  • Model retraining and updating strategies