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Survival probability

As explained in Financial AI in Practice

The probability that an entity has not yet experienced the event of interest by a specified time.

Survival probability is the chance that a loan remains current—has not defaulted—at a given time. A Kaplan–Meier curve starts at 1.0 and steps downward when defaults occur; a value near 0.90 at month 12 indicates about 10% defaulted. section 4.2.2

Figure 4.3 Kaplan–Meier survival curve for a synthetic loan portfolio. Each downward step in the curve reflects a default event. By month 12, about 10% of the loans have defaulted, consistent with our simulated parameters. Survival analysis enables granular, time-aware risk modeling by tracking each account independently rather than in aggregate.
figure

The curve shows how each default reduces the estimated share of loans still current over time. section 4.2.2

How Financial AI in Practice covers it

How deep it goes
Interprets a stepwise Kaplan–Meier curve and demonstrates fitting it, without deriving the estimator mathematically.
Examples
Uses 50 synthetic loans, roughly 10% defaulting within 12 months, to illustrate the survival curve.
What you can do after
Apply it via Kaplan–Meier estimation to plot the probability that loans remain current over time.
How it connects
Connects survival analysis and Kaplan–Meier estimation with vintage analysis, censored observations, and borrower-level covariates.

This explainer was generated by Manning's knowledge graph from the text of Financial AI in Practice. The section links point to the parts of the book it draws on.