  A novel procedure is presented for finding the true but latent endpoints
within the repayment histories of individual loans. The monthly observations
beyond these true endpoints are false, largely due to operational failures that
delay account closure, thereby corrupting some loans in the dataset with
`false' observations. Detecting these false observations is difficult at scale
since each affected loan history might have a different sequence of zero (or
very small) month-end balances that persist towards the end. Identifying these
trails of diminutive balances would require an exact definition of a "small
balance", which can be found using our so-called TruEnd-procedure. We
demonstrate this procedure and isolate the ideal small-balance definition using
residential mortgages from a large South African bank. Evidently, corrupted
loans are remarkably prevalent and have excess histories that are surprisingly
long, which ruin the timing of certain risk events and compromise any
subsequent time-to-event model such as survival analysis. Excess histories can
be discarded using the ideal small-balance definition, which demonstrably
improves the accuracy of both the predicted timing and severity of risk events,
without materially impacting the monetary value of the portfolio. The resulting
estimates of credit losses are lower and less biased, which augurs well for
raising accurate credit impairments under the IFRS 9 accounting standard. Our
work therefore addresses a pernicious data error, which highlights the pivotal
role of data preparation in producing credible forecasts of credit risk.
