  The IFRS 9 accounting standard requires the prediction of credit
deterioration in financial instruments, i.e., significant increases in credit
risk (SICR). However, the definition of such a SICR-event is inherently
ambiguous, given its reliance on comparing two subsequent estimates of default
risk against some arbitrary threshold. We examine the shortcomings of this
approach and propose an alternative framework for generating SICR-definitions,
based on three parameters: delinquency, stickiness, and the outcome period.
Having varied these parameters, we obtain 27 unique SICR-definitions and fit
logistic regression models accordingly using rich South African mortgage data;
itself containing various macroeconomic and obligor-specific input variables.
This new SICR-modelling approach is demonstrated by analysing the resulting
portfolio-level SICR-rates (of each SICR-definition) on their stability over
time and their responsiveness to economic downturns. At the account-level, we
compare both the accuracy and flexibility of the SICR-predictions across all
SICR-definitions, and discover several interesting trends during this process.
These trends and trade-offs can help in selecting the three parameters, as
demonstrated in our recommendations for defining SICR-events. In summary, our
work can guide the formulation, testing, and modelling of any SICR-definition,
thereby promoting the timeous recognition of credit losses; the main imperative
of IFRS 9.

Risk Management;Statistical Finance;Applications