Operational Recommendations

What you'll see here

Three-tier decision policy the OFLC can adopt today, backed by the feature importance and denial-rate segments the model surfaced.

Fast-track

Profile: Master's / Doctorate · Has job experience · Full-time · Yearly wage unit · Northeast/West region

Action: Auto-recommend certification, spot-check 10% for audit compliance.

Expected impact: Expected certification rate > 85%. Frees analyst hours for higher-risk cases.

Standard review

Profile: Bachelor's · Mixed experience · Yearly / Monthly wage · Any region

Action: Route through the current review workflow; use model score as advisory input.

Expected impact: Model confidence between 40–70% — human judgement adds real value here.

Heightened scrutiny

Profile: High School · No job experience · Hourly wage unit · Small employer (<50 employees)

Action: Manual senior analyst review; require additional employer documentation.

Expected impact: Expected denial rate > 55%. Concentrates analyst attention where it matters most.

Governance & guardrails

• Publish the model card, including feature list, training window, and test-set metrics, to every reviewer.

• Log every override (human decision ≠ model recommendation) and review monthly for drift.

• Re-train quarterly on the trailing 12 months of adjudicated cases.

• Do not use continent, gender, or any protected-class proxy without disparate-impact testing.

Limitations we're honest about

• Historical labels reflect past OFLC decisions — the model will replicate any historical bias unless audited.

• The dataset lacks free-text employer justifications; a real production system should incorporate document review.

• Random oversampling is a weak substitute for SMOTE; a full sklearn pipeline should be the production reference.

EDA-backed segmentation

Model-driven prioritization

Auditable policy

AI: How to phrase this to OFLC leadership

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