The International Accounting Standards Board (IASB), in collaboration with other standard-setting entities, establishes principles-based regulations for banks on the acknowledgment and provisioning for credit losses in financial reporting. July 2014 marked the issuance of International Financial Reporting Standard 9 – Financial Instruments (IFRS 9) by the IASB, introducing the “expected credit loss” (ECL) model for impairment recognition. This document presents a detailed overview of the ECL approach as defined in IFRS 9, and examines its influence on the treatment of accounting provisions within the Basel capital framework, focusing on regulatory implications.Start Free Trial
Roopya collects and consolidate data from various sources relevant to IFRS 9 risk modelling, such as loan contracts, customer information, internal ratings, and external data sources. This helps ensure data accuracy and consistency for model development and calculations.
The impairment model based on the International Financial Reporting Standard 9 (IFRS 9) and its interaction with the Basel capital framework plays a crucial role in the global financial system for several reasons:
Banks governed by IFRS 9 typically also fall under the Basel III Accord’s capital requirements. To determine credit risk-weighted assets, they utilize either standardized approaches or internal ratings-based methods. The recent provisions introduced by IFRS 9 will affect the profit and loss statements, which must then be considered when calculating impairment provisions for regulatory capital purposes. The systems required to compute and report on the expected loss and its impact on capital adequacy are already established. The data, methodologies, and procedures currently employed within the Basel framework may, to some extent, be adapted for modeling provisions under IFRS 9, though significant modifications are necessary.
One of the most significant transformations introduced by IFRS 9 is the integration of credit risk data into the accounting and financial reporting processes. This necessitates a novel form of collaboration between the finance and risk departments within an organization, which will subsequently influence the management of data. The adoption of the IFRS 9 impairment model revises the existing methodologies for defining, utilizing, and overseeing risk and finance data analytics across an institution. However, IFRS 9 isn’t the sole catalyst for this shift. Recommendations from the Basel Committee, guidelines and consultation papers from the European Banking Authority (EBA), along with specific regulatory initiatives like stress testing and the Internal Capital Adequacy Assessment Process (ICAAP), are compelling organizations to adopt a more forward-looking and data-centric stance in both risk management and financial reporting.
|Backward-looking, recognizes losses only when evidence of impairment exists
|Forward-looking, recognizes expected losses throughout the life of the instrument, even if not yet incurred
|Probability of default (PD)
|Through-the-cycle PD considers historical, current, and stress scenario information to estimate default probability over a business cycle
|Point-in-time PD uses current information at the reporting date to estimate the immediate default probability
|Loss given default (LGD)
|Downturn LGD reflects maximum expected loss in a severe economic downturn
|Point-in-time LGD reflects expected loss given default at the reporting date
|Primarily uses 12-month expected loss horizon
|Considers lifetime expected loss over the entire contract duration
|Standardized approaches with limited options for differentiation
|Internal Ratings-Based (IRB) approach allows for more granular risk assessment based on bank-specific data and models
|Primarily calibrated to historical loss data
|Requires calibration to both historical and forward-looking economic scenarios
|Aims to ensure minimum capital adequacy requirements are met
|Aims to provide a more accurate picture of credit risk for financial reporting purposes
|Impact on capital requirements
|May result in lower capital requirements due to more conservative assumptions
|May lead to higher capital requirements due to earlier recognition of expected losses
|Less data-intensive, relies mainly on historical internal and external data
|More data-intensive, requires comprehensive historical and forward-looking economic data
|Standardized approaches are relatively simple, IRB approaches are more complex
|Can be significantly more complex and require advanced modeling techniques
|Standardized approaches offer limited transparency, IRB approaches offer more flexibility in model development and disclosure
|Requires detailed disclosure of model assumptions and methodologies
Roopya automates routine calculations for expected credit losses (ECL) under IFRS 9, including probability of default (PD), loss given default (LGD), and exposure at default (EAD). This saves time and reduces manual errors in risk assessments.
Roopya also facilitates scenario analysis and stress testing, allowing banks to assess the impact of different economic conditions on credit losses and capital adequacy under IFRS 9.
Model Development and Validation:
Integration and Reporting:
Additional benefits with Roopya:
Improved efficiency and accuracy: By automating tasks and centralizing data, Roopya can significantly improve the efficiency and accuracy of risk modelling under IFRS 9.
Reduced regulatory burden: Roopya can streamline regulatory reporting processes and ensure compliance with IFRS 9 disclosure requirements.