Leverage Existing Model for Low Credit Score Applicants

Q: Leverage Existing Model for Low Credit Score Applicants

This question evaluates competency in handling dataset shift and selection bias, adapting and recalibrating credit-risk models for an unobserved applicant subpopulation, and reasoning about model robustness, statistical diagnostics, and safe deployment constraints.

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Question

Expanding a Credit-Risk Model to a New Score Band

Scenario

Your current probability-of-default (PD) lending model was trained only on applicants with credit scores ≥ 650 because those were historically considered for lending. Management now wants to evaluate and potentially lend to applicants with scores < 650.

Question

How would you leverage the existing model and available data to score the < 650 population, while addressing dataset shift and selection bias? Outline a practical plan that:

Diagnoses distribution/selection shift between ≥ 650 and < 650 populations.
Reuses and adapts the existing model instead of training from scratch.
Obtains or infers labels and/or corrects bias for the previously unserved group (< 650).
Imposes reasonable inductive biases (e.g., monotonic constraints) to reduce risky extrapolation.
Validates, calibrates, and proposes a safe rollout to production.

Hints: covariate/selection shift, importance weighting, reject inference, semi-supervised learning, synthetic augmentation, boundary expansion, monotonic constraints, and conservative calibration.

Leverage Existing Model for Low Credit Score Applicants

Expanding a Credit-Risk Model to a New Score Band

Scenario

Question

Solution

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