Abstract

The International Financial Reporting Standard 9 (IFRS 9) introduced a forward-looking impairment model that significantly transformed how financial institutions assess and recognize credit losses. Unlike its predecessor IAS 39, IFRS 9 mandates the calculation of Expected Credit Losses (ECL) across all financial assets not measured at fair value through profit or loss. This shift demands high-frequency data processing, dynamic risk modeling, and the incorporation of macroeconomic forecasts—all of which present substantial challenges to institutions relying on manual or semi-automated systems. In response, this proposes an IFRS 9-compliant Impairment Automation Framework designed to ensure regulatory compliance while enhancing financial accuracy, scalability, and operational efficiency. The proposed framework comprises modular, cloud-native components that automate the ingestion, transformation, and governance of credit data from core banking systems, customer databases, and external market feeds. It integrates machine learning algorithms to enhance the estimation of key risk parameters—Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD)—and supports multi-scenario macroeconomic overlays to meet forward-looking requirements. The architecture also features a stage assessment engine for classifying assets according to significant increases in credit risk (SICR), in line with IFRS 9’s three-stage model. Further, the system enables seamless interaction with general ledger systems and regulatory reporting tools through API-based orchestration and real-time audit trails. By automating the end-to-end impairment process, financial institutions can achieve greater accuracy, transparency, and responsiveness in credit loss recognition. This framework not only ensures compliance with global accounting standards but also equips institutions with predictive insights that can inform strategic credit risk decisions. Ultimately, the paper calls for Chief Financial Officers (CFOs), Chief Risk Officers (CROs), and data science teams to adopt intelligent automation strategies to future-proof their impairment processes in an increasingly data-driven regulatory environment.