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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 form a rudimentary expert system for selecting the three parameters optimally, 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.

A new procedure is presented for the objective comparison and evaluation of default definitions. This allows the lender to find a default threshold at which the financial loss of a loan portfolio is minimised, in accordance with Basel II. Alternative delinquency measures, other than simply measuring payments in arrears, can also be evaluated using this optimisation procedure. Furthermore, a simulation study is performed in testing the procedure from 'first principles' across a wide range of credit risk scenarios. Specifically, three probabilistic techniques are used to generate cash flows, while the parameters of each are varied, as part of the simulation study. The results show that loss minima can exist for a select range of credit risk profiles, which suggests that the loss optimisation of default thresholds can become a viable practice. The default decision is therefore framed anew as an optimisation problem in choosing a default threshold that is neither too early nor too late in loan life. These results also challenges current practices wherein default is pragmatically defined as '90 days past due', with little objective evidence for its overall suitability or financial impact, at least beyond flawed roll rate analyses or a regulator's decree.