Unlocking Credit Cycles

Wholesale credit risk over the past 30-40 years has exhibited pronounced cycles, with losses varying widely over time and across industries and regions. Coupled with new regulations for IFRS9/CECL and Stress Testing, this creates the need for Point-in-Time (PIT) models.

PIT models for PD, LGD and EAD are capable of accurately assessing credit losses at all points in the cycle, and across all borrowers and individual exposures.

Z-Risk Engine® is brought to you by Aguais And Associates, in association with Deloitte

Z-Risk Engine is an advanced suite of software solutions for IFRS9/CECL and Stress Testing (PRA/EBA/CCAR). Developed and refined over the last decade, the solution is a single, integrated and customizable Batch Analytics Platform for wholesale credit portfolios. Z-Risk Engine enables financial institutions to successfully meet the requirements of complex regulations.

Advanced analytics to support global banks and institutions with IFRS9/CECL and Stress Testing

SAS® software-based solution, Z-Risk Engine can be implemented in a dynamic batch process with your existing credit models. It incorporates an advanced credit analytics framework designed to unlock Credit Cycles – the key component of accurately projecting ECL impairments.

The Z in Z-Risk Engine

'Z' represents our notation for Credit Cycle indices, which we first published in our technical papers in 1998. From there on, the industry started using 'Z' as a notation for systematic credit factors. 'Z', the credit cycle indices, are at the core of Z-Risk Engine and are key to measuring PIT risks in our advanced solution.

Subjective vs Objective

Subjective forecast refers to a user-based prediction of the future where a point estimate or distribution of a variable is derived based on users' judgement which cannot be ensured to be unbiased or correctly probability weighted.

Objective forecasts attempt to predict future outcomes on the basis of current and past data describing the deterministic and random factors that affect future outcomes, according to a credible model calibrated to historical experience.


Point in Time (PIT) measures draw on up-to-date, comprehensive information on the related obligors, accounting fully for the future effects of accumulating, systematic and idiosyncratic risk. For example PIT PD is defined as the unconditional expectation of an entity's probability of default and should track closely the temporal fluctuation in portfolio's observed default rates.

Through the Cycle (TTC) is defined as conditional expectation assuming that credit conditions are close to long term average. For example, in PD context this means that average TTC PDs are close to long term average default rates.

Unconditional vs Conditional Credit Losses

Unconditional refers to a distribution or point estimate of a random variable of interest, when no specific assumption is made about another related variable e.g. PIT PD when nothing arbitrary is assumed about GDP movement next year. Unconditional means that the related variable will continue to behave as it has had in the past.

Conditional refers to a distribution or point estimate of a random variable of interest, when a specific assumption is made about another related variable e.g. PIT PD when GDP shrinks by 4% next year. Another example of conditionality is TTC PD which is PIT PD when credit conditions are close to long run average.