3 papers in the archive (2025–2026), average empirical rigor 7.7/10, average math complexity 5.5/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in Risk Management & Tail Risk, NLP & LLMs in Finance, Fixed Income & Interest Rates.
Frequent co-authors: Arno Botha (2), Bernard Scheepers (1), Dian Prinsloo (1), Neill Smit (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
In the pursuit of modelling a loan’s probability of default (PD) over its lifetime, repeat default events are often ignored when using Cox Proportional Hazard (PH) models. Excluding such events may produce biased and inaccurate PD-estimates, which can compromise financial buffers against future loss
Under the International Financial Reporting Standards (IFRS) 9, credit losses ought to be recognised timeously and accurately. This requirement belies a certain degree of dynamicity when estimating the constituent parts of a credit loss event, most notably the probability of default (PD). It is noto
Background: Credit risk modelling is fundamental in financial institutions, typically using credit history, repayment behaviour, income, and outstanding debt to estimate a borrower’s probability of default. Although transactional data are sometimes incorporated through aggregated measures such as re