Context

SoFi has deployed a regression model to predict the final APR offered to applicants for SoFi Personal Loans before the pricing engine applies policy adjustments. The model is used by the underwriting and pricing teams to estimate applicant-level rates, but recent monitoring shows larger-than-expected pricing errors for some borrower segments.

Current Performance

The Problem

The model still looks acceptable on aggregate, but it systematically underpredicts APR for thin-file and larger-loan applicants. That creates downstream pricing corrections in the SoFi underwriting flow and a poorer member experience when quoted rates change.

Requirements

1. Evaluate the model using the metrics above and explain which metrics matter most.
2. Diagnose what the aggregate metrics may be hiding across segments.
3. Recommend a validation approach to confirm whether this is drift, poor calibration, or segment underfit.
4. Propose concrete improvements to the model and monitoring plan.

Constraints

• APR prediction errors above 0.50 points materially increase quote-to-funded drop-off.
• Underpricing risk is more costly than overpricing risk.
• Any replacement model must be explainable enough for risk and pricing review.