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

Paper: arXiv 2404.17008

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.

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper is methodologically grounded with statistical references but relies on a relatively simple optimization procedure rather than dense mathematics, while demonstrating the procedure on real-world datasets and quantifying improvements in risk predictions.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Identify true repayment endpoints<br>and trailing zero balances"] --> B["Data Source:<br>Two South African<br>credit datasets"]
  B --> C["Key Methodology:<br>TruEnd-procedure<br>Define 'small balance' threshold"]
  C --> D["Computational Process:<br>Detect & remove false<br>trailing zero observations"]
  D --> E["Outcome:<br>Cleaned loan histories"]
  E --> F["Analysis:<br>Survival modeling on<br>corrected time-to-event data"]
  F --> G["Key Findings:<br>1. Reduced credit loss estimates<br>2. Less biased IFRS 9 impairments<br>3. Improved risk timing accuracy"]

Cited References (44)

1. A procedure for loss-optimising the timing of loan recovery under uncertainty (2021) — link
2. Simulation-based optimisation of the timing of loan recovery across different portfolios (2021) — link
3. The loss optimisation of loan recovery decision times using forecast cash flows (2022) — link
4. The TruEnd-procedure [source code] for treating trailing zero-valued balances in credit data (2024) — link
5. Survival analysis: a self-learning text (2012) — link
6. The elements of statistical learning: Data mining, inference, and prediction (2009) — link
7. An introduction to statistical learning: with applications in R (2013) — link
8. Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS (2016)

0 of 44 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.