Credit Scoring Model

A Credit Scoring Model is a statistical algorithm that predicts an economic entity's credit score based on credit data attributes (supporting creditworthiness assessment).

• AKA: Scorecard, Credit Risk Model.
• Context:
  • It can typically analyze Credit Scoring Model Factors such as credit scoring model payment history, credit scoring model debt level, and credit scoring model account age.
  • It can typically generate Credit Scoring Model Score as a credit scoring model numerical representation of credit scoring model risk level.
  • It can typically predict Credit Scoring Model Default Probability based on credit scoring model historical behavior.
  • It can typically support Credit Scoring Model Decision such as credit scoring model approval, credit scoring model rejection, or credit scoring model conditional approval.
  • It can typically segment Credit Scoring Model Population into credit scoring model risk bands for credit scoring model targeted strategy.
  • ...
  • It can often incorporate Credit Scoring Model Algorithm such as credit scoring model logistic regression, credit scoring model decision tree, or credit scoring model neural network.
  • It can often use Credit Scoring Model Data Source including credit scoring model bureau data, credit scoring model application data, and credit scoring model behavioral data.
  • It can often require Credit Scoring Model Validation through credit scoring model backtesting, credit scoring model benchmarking, and credit scoring model statistical analysis.
  • It can often undergo Credit Scoring Model Monitoring to detect credit scoring model performance degradation and credit scoring model population drift.
  • ...
  • It can range from being a Simple Credit Scoring Model to being a Complex Credit Scoring Model, depending on its credit scoring model variable count.
  • It can range from being a Generic Credit Scoring Model to being a Custom Credit Scoring Model, depending on its credit scoring model development specificity.
  • It can range from being a Traditional Credit Scoring Model to being an Alternative Credit Scoring Model, depending on its credit scoring model data type.
  • ...
  • It can integrate with Underwriting System for automated credit scoring model decisioning.
  • It can connect to Loan Origination System for streamlined credit scoring model implementation.
  • It can support Portfolio Management System for ongoing credit scoring model monitoring.
  • ...
• Examples:
  • Credit Scoring Model Types, such as:
    • Consumer Credit Scoring Models, such as:
      • FICO Credit Scoring Model for consumer lending credit evaluation.
      • VantageScore Credit Scoring Model for alternative consumer credit assessment.
      • Custom Internal Credit Scoring Model for institution-specific consumer risk evaluation.
    • Business Credit Scoring Models, such as:
      • Small Business Credit Scoring Model for small business loan underwriting.
      • Corporate Credit Scoring Model for large enterprise credit risk assessment.
      • Supplier Credit Scoring Model for vendor financing risk evaluation.
  • Credit Scoring Model Methodologys, such as:
    • Statistical Credit Scoring Models, such as:
      • Logistic Regression Credit Scoring Model for probability-based credit scoring.
      • Linear Discriminant Analysis Credit Scoring Model for classification-based credit scoring.
    • Machine Learning Credit Scoring Models, such as:
      • Random Forest Credit Scoring Model for ensemble-based credit scoring.
      • Neural Network Credit Scoring Model for complex pattern credit scoring.
      • Gradient Boosting Credit Scoring Model for iterative credit risk prediction.
  • Credit Scoring Model Application Domains, such as:
    • Lending Credit Scoring Models, such as:
      • Mortgage Credit Scoring Model for home loan risk assessment.
      • Auto Loan Credit Scoring Model for vehicle financing risk prediction.
      • Credit Card Credit Scoring Model for revolving credit limit determination.
    • Account Management Credit Scoring Models, such as:
      • Behavior Credit Scoring Model for existing customer risk monitoring.
      • Collection Credit Scoring Model for delinquency recovery optimization.
      • Attrition Credit Scoring Model for customer retention probability prediction.
  • Credit Scoring Model Development Eras, such as:
    • Early Credit Scoring Model (1950s-1980s), characterized by credit scoring model expert judgment and credit scoring model simple statistical techniques.
    • Modern Credit Scoring Model (1990s-2010s), featuring credit scoring model advanced analytics and credit scoring model standardized approaches.
    • Next-Generation Credit Scoring Model (2010s-present), incorporating credit scoring model alternative data, credit scoring model machine learning techniques, and credit scoring model real-time decisioning.
  • ...
• Counter-Examples:
  • Judgmental Credit Assessment, which relies on expert opinion rather than statistical analysis of credit data.
  • Credit Report, which provides raw credit information without predictive scoring.
  • Financial Ratio Analysis, which examines financial statements rather than credit behavior patterns.
  • Income Verification Process, which confirms income sources but doesn't predict repayment likelihood.
  • Asset Valuation Model, which estimates asset worth rather than borrower creditworthiness.
• See: Credit Scoring, Credit Scoring Model Generation Task, Predictive Model, Credit Risk Assessment Tool, Underwriting Decision Support System.

---

---

References

1997

• (Mester, 1997) ⇒ Loretta Mester. (1997). “What’s the Point of Credit Scoring.” In: Federal Reserve Bank of Philadelphia Business Review 3-16.
  • QUOTE: … To build a scoring model, or “scorecard,” developers analyze historical data on the performance of previously made loans to determine which borrower characteristics are useful in predicting whether the loan performed well. A well-designed model should give a higher percentage of high scores to borrowers whose loans will perform well and a higher percentage of low scores to borrowers whose loans won’t perform well. But no model is perfect, and some bad accounts will receive higher scores than some good accounts.

---