The TruEnd-procedure: Treating trailing zero-valued balances in credit data

TL;DR

This paper introduces the TruEnd-procedure, a method to identify and remove false trailing zero balances in loan repayment histories, improving credit risk prediction accuracy and reducing bias in loss estimates.

Contribution

The paper presents a novel, data-driven approach to detect true loan endpoints and eliminate corrupted data, enhancing the reliability of credit risk modeling.

Findings

Corrupted loan histories are common and often very long.

Removing false trailing zeros improves timing and severity predictions of risk events.

Lower, less biased credit loss estimates result from data cleaning.

Abstract

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. Detecting these false observations is difficult at scale since each affected loan history might have a different sequence of trailing zero (or very small) month-end balances. Identifying these trailing balances requires an exact definition of a "small balance", which our method informs. We demonstrate this procedure and isolate the ideal small-balance definition using two different South African datasets. Evidently, corrupted loans are remarkably prevalent and have excess histories that are surprisingly long, which ruin the timing of risk events and compromise any subsequent time-to-event model, e.g., survival analysis. Having discarded these excess histories, we demonstrably improve the accuracy of both the predicted timing and severity of risk events, without materially impacting the portfolio. The resulting estimates of credit losses are lower and less biased, which augurs well for raising accurate credit impairments under IFRS 9. Our work therefore addresses a pernicious data error, which highlights the pivotal role of data preparation in producing credible forecasts of credit risk.