A risk factor is any variable or condition that influences the likelihood of a credit event or portfolio loss. Identifying these factors is critical for robust scenario analysis and effective model construction in private credit.

Data integrity is the process of ensuring data accuracy, consistency, and reliability from collection through analysis. It is foundational to credible risk modeling, especially in illiquid private credit.

Scenario analysis involves simulating a range of plausible future events or market conditions to evaluate how credit exposures may respond. This approach is vital in markets where historic data is limited or non-standard.

Peer review is the practice of independent evaluation by experts to ensure methodological rigor, accuracy, and transparency in risk model development.

Regulatory alignment means ensuring that risk models and reporting practices meet current standards set by regulatory authorities, reducing compliance risk for institutions.

Transparent documentation refers to recording every modeling decision, assumption, and data source, supporting auditability and external review.

Illiquidity premium is the additional compensation required by investors for holding assets that cannot be easily sold or priced. Quantifying this premium is essential for private credit modeling.

Model adaptation describes the process of customizing risk models to fit the unique exposures and characteristics of specific portfolios, rather than relying on generic market assumptions.

Sensitivity analysis tests how changes in key variables affect model outputs. It is a tool for identifying which assumptions have the greatest impact on risk estimates.

Model governance refers to the framework of policies and controls ensuring models remain accurate, relevant, and compliant as conditions evolve.