Design a Credit Scoring ML Pipeline — System Design Interview Practice
Design a credit scoring system that uses ML to assess creditworthiness, handles imbalanced data, provides explainable decisions, and complies with fair lending regulations. Work through the requirements, architecture trade-offs, and an interactive design review.
Requirements and concepts to consider
- Train credit scoring models on historical dataRequirement
- Handle imbalanced classes (default vs non-default)Requirement
- Provide feature importance for each decisionRequirement
- Detect and mitigate bias across protected groupsRequirement
- Generate adverse action reason codesRequirement
- Support model versioning and comparisonRequirement
- Validate model performance on holdout setsRequirement
- Produce regulatory compliance documentationRequirement
- mlConcept to explore
- credit scoringConcept to explore
- explainabilityConcept to explore
- fairnessConcept to explore
- fintechConcept to explore
- classificationConcept to explore