Introduction: The Challenge of Auditing IFRS 9
Since the introduction of IFRS 9 Financial Instruments, banks have faced increased regulatory scrutiny, requiring robust processes to classify financial instruments, estimate Expected Credit Losses (ECL), and ensure data accuracy across loan portfolios.
For auditors, ensuring compliance with IFRS 9 requires analyzing full populations of transactions, performing complex recalculations, and identifying data inconsistencies across disparate systems. Traditional audit approaches relying on sampling and manual analysis are no longer sufficient.
This is where ACL Analytics (now part of Diligent) empowers auditors to conduct faster, deeper, and more effective audits through data automation, advanced analytics, and risk-focused insights.
IFRS 9 Audit Focus Areas
Auditing IFRS 9 means making sure everything adds up correctly. Auditors need to check that financial instruments are properly classified under amortized cost, FVPL, or FVOCI. They also have to ensure that the Expected Credit Loss (ECL) model is applied correctly, factoring in segmentation, staging, and macroeconomic inputs. Just as important is verifying that loan data is complete and accurate across all systems. Finally, financial statement disclosures must be clear, complete, and fully compliant with regulations.
ACL Analytics: A Game-Changer for IFRS 9 Audits
1. Automated Data Extraction & Full-Population Testing
ACL connects directly to core banking systems, spreadsheets, and data warehouses, enabling auditors to access the full set of loan data instead of just working with samples. This approach ensures that all high-risk loans are reviewed and removes the need to rely on management’s reports, offering a more thorough and accurate audit.
2. Data Quality & Consistency Checks
ACL automatically spots issues like missing data (e.g., credit ratings or loan modification dates), classification errors (e.g., instruments marked as “amortized cost” when they should be something else), and duplicate or mismatched records across systems. This helps ensure that the data used for IFRS 9 modeling is accurate, reliable, and ready for audit.
3. Expected Credit Loss (ECL) Model Recalculation
With ACL Analytics, auditors can recalculate the Expected Credit Loss (ECL) themselves by applying formulas to the loan data they’ve extracted. They can test key areas like stage classification (Stage 1, 2, 3), based on how overdue payments are or credit changes. They can also check calculations for Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD), and run sensitivity tests on macroeconomic scenarios like inflation or GDP decline. This helps auditors challenge management’s assumptions and confirm everything aligns with IFRS 9 requirements.
4. Anomaly & Outlier Detection
ACL’s statistical tools help auditors spot unusual patterns, like loans being reclassified to lower-risk categories just before reporting dates, staging reversals without a clear reason, or outliers in provisioning compared to similar loans or past trends. This makes it easier for auditors to focus their testing on the areas that pose the highest risk.
Practical Example: Applying ACL Analytics to an IFRS 9 Audit
Scenario: A bank’s loan portfolio includes retail loans, corporate facilities, and SME financing spread across multiple systems. The external audit team uses ACL Analytics to validate IFRS 9 compliance through the following steps:
Step 1: Data Extraction
Auditors use ACL to extract loan data from various systems:
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Core banking system (loan balances, product type, customer data).
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Credit risk system (credit ratings, days past due, internal risk classifications).
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Treasury system (collateral valuations, macroeconomic forecasts).
This creates a comprehensive dataset for IFRS 9 testing.
Step 2: Data Integrity & Completeness Check
ACL helps auditors check for:
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Missing credit ratings or customer segment codes.
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Loans in the credit risk system that don’t appear in accounting records.
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Inconsistent staging data (e.g., loans classified differently in different systems).
The result: 152 loans with inconsistent staging and 38 loans missing credit ratings.
Step 3: Recalculating Expected Credit Losses (ECL)
Using ACL’s scripting, auditors apply ECL formulas to the extracted data:
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Classify loans into Stages 1, 2, or 3 based on risk factors.
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Recalculate Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD).
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Run sensitivity analysis on macroeconomic factors.
Result: ACL recalculates ECL for all loans, revealing that management had understated SME loan ECL by 7% due to overly optimistic LGD assumptions.
Step 4: Trend Analysis & Anomaly Detection
Auditors use ACL’s tools to review:
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Movements in loan staging over the reporting period.
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Trends in provisioning rates across products.
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Outliers in loan modifications, such as last-minute restructuring.
The result: 42 loans were reclassified from Stage 2 to Stage 1 at the end of the reporting period, raising concerns about management bias.
Step 5: Automated Disclosure Review
ACL extracts IFRS 9 disclosure tables from financial statements and cross-checks them against audit evidence.
Result: Two missing disclosures related to credit risk sensitivity analysis were flagged.
This process shows how ACL Analytics streamlines IFRS 9 audits, providing auditors with a more thorough and efficient way to assess compliance.
Summary of Findings
The audit revealed several key issues:
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Inconsistent Staging: 152 loans with potential for ECL understatement.
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Missing Credit Ratings: 38 loans, posing a data integrity risk.
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Understated ECL: The SME portfolio showed a 7% understatement in ECL.
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Unusual Reclassifications: 42 loans were reclassified at the last minute, raising concerns about management bias.
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Missing Disclosures: 2 instances of missing disclosures, leading to non-compliance with IFRS 9.
These findings highlight areas for further attention to ensure full compliance and data accuracy.
Why Banks Should Leverage ACL Analytics for IFRS 9 Audits
Using ACL Analytics for IFRS 9 audits offers several benefits:
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Complete Coverage: Full population testing ensures that every loan is reviewed, not just a sample.
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Efficiency Gains: Automation of tasks like data extraction, ECL recalculation, and disclosure review saves time and effort.
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Objective Insights: Data-driven analysis minimizes subjective judgments, leading to more reliable findings.
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Regulatory Confidence: Audits powered by advanced analytics show strong governance and internal controls, boosting regulatory confidence.
Conclusion
In today’s regulatory environment, where data integrity, transparency, and audit quality are crucial, ACL Analytics helps banks:
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Improve the quality and defensibility of IFRS 9 compliance audits.
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Identify data gaps, inconsistencies, and hidden risks early on.
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Streamline the audit process with automation and advanced analytics.
By integrating ACL Analytics into your audit approach, you not only strengthen compliance but also show regulators and stakeholders that your bank is committed to using top-tier audit technology.
Sources and References
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IFRS 9 Standard – IFRS Foundation https://www.ifrs.org/issued-standards/list-of-standards/ifrs-9/
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Diligent (ACL Analytics) Official Site – https://www.diligent.com/acl-analytics
Deloitte Insights – IFRS 9 Implementation Challenges https://www2.deloitte.com/insights/ifrs-9
