In the dynamic landscape of financial accounting, the shift from the incurred loss model to the Expected Credit Loss (ECL) model has brought about transformative changes.

Therefore, it’s crucial to establish robust frameworks for ECL modelling, shedding light on the challenges and benefits that organisations face in navigating this intricate practice. As we journey through the complexities of ECL modelling, it becomes evident that a solid foundation is not merely a choice – but a necessity.

This is underpinned by APRA’s recent article “Demystifying Credit Risk Capital Requirements for Housing Loans”, sharing monitoring practices and guidance set by the regulator. There are many complimentary elements to ECL under AASB 9: Financial Instruments and Capital Adequacy, emphasising the importance of a holistic approach for effective implementation.

Understanding ECL

ECL modelling represents a shift in accounting standards, notably emphasised in AASB 9. In contrast to the conventional incurred loss model, which recognised losses only when they occurred, ECL’s perspective is to the future by considering potential future credit losses. This shift is motivated by the imperative for financial institutions to take a more proactive and transparent approach when assessing credit risk.

Data availability and quality

ECL modelling requires a high calibre of data – being more granular and forward-looking. The challenge then lies in collecting, validating, and governing this data.

Many organisations struggle to ensure accurate and reliable data, which directly impacts the precision of ECL estimates. It’s therefore important to set up strong data management frameworks, ensuring your businesses reduces the need for manual interventions, boosts credibility and therefore accuracy.

Methodology and assumptions

ECL modelling involves navigating through countless methodological choices and assumptions. From estimating probability, loss, and exposure, each decision impacts the outcomes. Robust and consistent methodologies tailored to how your business operates will enhance comparability and transparency. In the absence of modelling assumptions with internal data there may be consideration for proxies such as the prudential guidance by regulators like APRA.


The strength of any financial institution's ECL framework lies in its governance and oversight. Clear roles and responsibilities, comprehensive policies and procedures, meticulous documentation, and rigorous internal and external audits are the foundation of your success. In addition, independent validation processes will improve compliance, quality, and effective communication of ECL results to stakeholders.

For your organisation to succeed in ECL modelling, a robust framework is essential. Dealing with data complexities, methodological nuances, and governance demands requires a strategic and thorough approach. To make this happen, organisations should focus on both regulatory requirements, but also creating a resilient and transparent financial future.

For advice around best practice in the context of your organisation, please don’t hesitate to get in touch.  

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