Role: Loan review specialist with ML experience
Location: Seattle, WA (Onsite)
Key Responsibilities
Traditional Loan Review Responsibilities
- Conduct independent reviews of commercial, consumer, real estate, agricultural, and specialty loan portfolios.
- Evaluate borrower financial condition, repayment capacity, collateral adequacy, and overall creditworthiness.
- Validate the accuracy and appropriateness of assigned risk ratings and recommend adjustments when necessary.
- Assess compliance with internal credit policies, underwriting standards, and loan documentation requirements.
- Identify emerging risks, trends, and concentrations within the loan portfolio.
- Review loan documentation for completeness, accuracy, and regulatory sufficiency.
- Prepare written reports summarizing findings, exceptions, and recommendations for management and the Board.
- Monitor remediation efforts and follow up on previously identified issues.
Machine Learning (ML) Model Responsibilities
- Review ML-generated credit decisions, risk scores, or early warning indicators to ensure they align with prudent credit risk principles.
- Assess the reasonableness of ML model outputs, including identifying anomalies, outliers, or inconsistent risk rating assignments.
- Evaluate data quality used in ML models for completeness, accuracy, and relevance to credit risk assessment.
- Verify that ML-driven recommendations are appropriately validated by human review and do not override sound underwriting judgment.
- Monitor for potential model bias, disparate impacts, or unintended discriminatory patterns in ML-based credit decisions.
- Collaborate with model risk management teams to understand model assumptions, limitations, validation results, and performance metrics.
- Review model documentation to ensure transparency, explainability, and compliance with regulatory expectations for model governance.
- Provide feedback on how ML tools impact credit quality, underwriting consistency, and portfolio-level risk trends.
- Maintain independence by evaluating ML-based decisions with the same rigor applied to manual underwriting.
Qualifications
- 3 7 years of experience in loan review, credit analysis, commercial lending, or banking risk management.
- Exposure to model risk management, data analytics, or ML-based credit tools is preferred.
- Ability to challenge both human and model-generated decisions constructively.