Expected credit losses are among the most significant risks faced by commercial banks, especially in an economic environment characterized by volatility and instability. These losses have gained increasing attention following the adoption of International Financial Reporting Standard No. (IFRS 9), which introduced a proactive approach to recognizing credit losses based on future estimates rather than actual default events. This approach aims to enhance the quality of financial information and achieve greater transparency in the financial reports issued by banks.
In this context, the importance of studying the relationship between expected credit losses and net profit becomes evident, given its direct impact on evaluating the financial performance of banks, their ability to attract investments, and the sustainability of their operations. Understanding this effect also helps bank management and financial statement users make more accurate decisions in areas such as lending, risk management, and financial oversight.
Accordingly, this research seeks to analyze and assess the extent of the impact of expected credit losses on net profit in a sample of private commercial banks, with the aim of reaching results that contribute to developing accounting and regulatory policies related to credit risk management, and enhancing the overall effectiveness of the financial and banking system.
Private commercial banks face growing challenges due to the increasing volume of credit facilities and the diversification of associated risk sources. This compels them to adopt more accurate and transparent accounting methods in measuring expected credit losses. With the implementation of IFRS 9, banks are now required to estimate these losses proactively, which may lead to fluctuations in net profits and accounting discrepancies that affect the quality of financial disclosure.
The core research problem can be formulated through the following main question:"What is the impact of expected credit losses on net profit in private commercial banks?"
From this central question, several sub-questions arise, including:
To what extent does the methodology of calculating expected credit losses affect financial performance outcomes?
Does early recognition of credit losses lead to a significant decrease in net profit?
How do credit loss estimates reflect on the reliability of financial statements and the financial performance analysis of private banks?
This research is significant as it highlights the effect of expected credit losses on net profit in private commercial banks in the context of applying international financial reporting standards, particularly IFRS 9. The study helps clarify the relationship between the estimation of such losses and financial outcomes, aiding bank management and financial statement users in enhancing the quality of financial decision-making and assessing profitability performance.
This research aims to:
Define the concept of expected credit losses according to IFRS 9.
Clarify how expected credit losses affect net profit in private commercial banks.
Analyze the relationship between credit loss estimation and financial performance results.
Provide indicators to improve accounting estimation methods for credit losses.
The study is based on the following main hypotheses:
Expected credit losses lead to a decrease in net profit.
Greater accuracy in estimating credit losses contributes to improving the quality of financial outcomes.
The application of IFRS 9 has a tangible effect on the profitability reflected in banks' financial statements.
To achieve the research objectives and test its hypotheses, a scientific methodology was adopted, combining both theoretical and applied approaches to ensure integration between the conceptual framework and practical analysis. This was carried out as follows:
Theoretical Aspect: The deductive method was used to review literature related to the research topic by discussing and analyzing concepts associated with credit risk and financial decision-making in banking institutions. Previous studies and relevant economic and accounting theories were reviewed to construct a solid theoretical framework supporting the applied part of the research.
Practical Aspect: The descriptive-analytical method was used to analyze the financial data of a sample consisting of four banks. Data were collected regarding their credit portfolios, default rates, liquidity levels, and financial performance indicators. This data was analyzed to identify the nature of the relationship between credit risks and the stability of financial decision-making within these institutions. Appropriate statistical tools were employed to process the data and obtain objective results that support the research hypotheses.
Credit losses represent one of the most significant challenges faced by commercial banks operating in environments marked by risk and uncertainty, particularly amid increasing loan issuance and credit facilities. Interest in this concept has grown following the implementation of International Financial Reporting Standard No. 9 (IFRS 9), which emphasizes the principle of
Credit losses are defined as the financial loss resulting from a borrower's failure to meet their payment obligations on time, whether partially or fully. This concept evolved significantly during global financial crises, which revealed the limitations of traditional models based on incurred losses, giving rise to the concept of
The essence of ECL lies in forecasting potential future losses using statistical models, historical data, and forward-looking indicators, rather than waiting for an actual default to occur. ECL is an accounting estimate based on three main components:
Probability of Default (PD)
Loss Given Default (LGD)
Exposure at Default (EAD)
This represents a qualitative shift in accounting for credit risk, promoting prudence and enhancing the quality and transparency of financial disclosures particularly in commercial banks.
According to Abdel Hamid (2000:103), credit losses are defined as:
Al-Dhabahawi and Al-Mousawi (2011:17) define credit losses as:
Credit losses are among the most significant indicators of a bank’s credit portfolio quality. The accounting concept of these losses evolved notably from the incurred loss model to the expected loss model. (Mohsen, 2020:12)
Incurred losses are recognized after an actual default has occurred. These are realized losses based on past events, as defined under the previous accounting standard IAS 39. (Frolova & Vasilyeva, 2019:142)
Expected losses, on the other hand, are future potential losses forecasted based on both quantitative and qualitative indicators, such as a customer’s credit rating and macroeconomic conditions. IFRS 9 introduced the ECL model based on PD, LGD, and EAD. (Hassanein, 2022:72)
Many researchers view this shift as a fundamental transformation in credit risk management methodology. It reinforces prudence and enhances the quality of accounting information for decision-makers.
Mohsen (2020) affirmed that the shift reduces surprise elements in financial statements and improves their reliability. Hassanein (2022) stated that the new model enhanced risk forecasting efficiency despite requiring advanced technological systems and analytical expertise. Frolova & Vasilyeva (2019) noted that the ECL approach strengthens banks’ resilience during crises by enabling more realistic provisioning aligned with the economic environment.
Credit losses stem from various internal and external factors. These can be classified as follows (Birn et al., 2023:243): Internal Factors:
Weak credit granting procedures, such as ignoring creditworthiness assessments or overextending credit without sufficient collateral.
Poor risk management, due to inadequate loan monitoring and borrower assessment systems.
Weak collection efforts, including delays in pursuing overdue payments or legal actions.
External Factors:
Economic fluctuations, including recession, inflation, or financial crises that affect customers’ ability to repay.
Political and security instability, which adversely impacts business operations and repayment capacities.
Interest rate changes, especially in long-term loans, increasing financial burdens on borrowers.
Credit losses vary based on timing, nature, and recognition method. The main types include (Ong & Wei, 2023:60): Realized losses recognized after a default event (e.g., non-payment or bankruptcy), as per IAS 39. (Federal, 2023:90) Anticipated losses estimated using quantitative and qualitative models under IFRS 9, categorized into three stages (IFRS Foundation, 2014:101):
Incurred Credit Losses:
Expected Credit Losses (ECL):
Stage 1: Financial instruments without significant credit deterioration loss measured over 12 months.
Stage 2: Instruments with significant deterioration but no default loss measured over the instrument’s lifetime.
Stage 3: Defaulted instruments full lifetime loss recognized.
Accounting vs. Economic Losses:
Individual vs. Collective Losses:
IFRS 9 adopts a forward-looking approach to credit loss measurement through the Expected Credit Loss (ECL) model, which estimates future risks based on various assumptions and both quantitative and qualitative factors. This model differs significantly from the incurred loss model of previous standards. (Federal, 2023:101)
1. Three-Stage Model: Financial assets are assessed in three stages depending on credit quality:
Stage 1: No significant deterioration—12-month ECL calculated.
Stage 2: Significant deterioration without default—lifetime ECL calculated.
Stage 3: Default has occurred—lifetime ECL recognized with interest revenue adjustments.
2. ECL Components (Botha et al., 2023:23): ECL is calculated as: ECL = PD × LGD × EAD
PD (Probability of Default): Likelihood of borrower defaulting within a set period.
LGD (Loss Given Default): Loss proportion after accounting for recoveries and collateral.
EAD (Exposure at Default): Outstanding debt at the time of default.
3. Forward-Looking Information:
The standard mandates incorporating macroeconomic forecasts (e.g., GDP growth, inflation, unemployment) to reflect expected economic conditions more accurately.
4. Simplified Approach:
Used for short-term receivables (e.g., trade receivables), where lifetime ECL is directly applied without stage categorization. (Jian et al., 2023:95)
Credit risk management is considered one of the most prominent issues occupying a high priority in the banking sector due to its direct impact on the sustainability of financial activity and achieving stability and profitability. Its importance is further heightened in an economic environment surrounded by monetary fluctuations, geopolitical crises, and regulatory pressures. This compels commercial banks especially private ones to adopt strict policies in credit granting and provision formation, in accordance with international standards such as IFRS 9 and Basel III recommendations.
Within this context, this section presents an applied analysis of the practices related to managing expected credit losses in a sample of Iraqi private commercial banks during the period (2021–2023). It monitors credit granting mechanisms, customer classification, types of collateral, and compares provision ratios prepared for expected losses. The analysis also covers the impact of these practices on net profit and the extent of the banks’ compliance with disclosure requirements in accordance with international standards.
The objective of this section is to assess the efficiency of accounting and supervisory policies in early detection of potential credit losses and the banks’ ability to absorb financial shocks without significantly affecting their operational results. It also highlights the importance of the differences among banks in default rates and provisions formed, and their effect on the stability of financial decision-making by management and financial statement users.
First: Procedures for Classifying Credit Granted to Borrowers in the Sample BanksThe banks included in the research sample categorize credit facilities granted to clients into six distinct levels. The initial three levels are treated as performing credit, while the remaining three are regarded as non-performing. This classification is updated on a monthly basis, allowing credit status to shift upward or downward based on specific criteria. The classification levels are summarized as follows:
Excellent Credit: This category comprises credit backed by guarantees that can be easily and promptly liquidated such as precious metals (e.g., gold, silver), pledged fixed-term deposits, savings balances, and sovereign bonds provided their value is at least twice the amount of credit granted. It also includes guarantees from the government or member countries of the OECD. Credits in this category are exempt from credit loss provisions until maturity.
Good Credit – Not Yet Matured: Characterized by normal risks, good repayment sources, strong financial positions of borrowers, not renewed or rescheduled. Also includes undrawn committed credit. Drawn committed credit is classified according to the categories below based on payment date.
Average Credit: Credit that is due but less than 90 days past due and needs monitoring to avoid reclassification as non-performing.
Below Average Credit: Credit overdue by more than 90 days but less than 180 days, including fully utilized overdraft accounts with unpaid interest for three months, unused overdraft accounts without interest payments or activity for six months, and all types of credit renewed or rescheduled once.
Poor Credit: Credit overdue by more than 180 days but less than 360 days, or renewed/rescheduled twice only, with no allowance for further rescheduling.
Loss Credit: Overdue by more than one year, deemed uncollectible. All types of foreign currency-denominated credit are classified and provisioned after conversion into Iraqi Dinar using the official exchange rate from the Central Bank of Iraq on the classification date. These are added to the provisions calculated for all types of credit in Iraqi Dinar.
All loans to related parties are subject to the same classification if any party or their companies defaults, especially when secured by the same guarantees. Provisions are calculated from the classification date.
Additional classifications applied by the research sample banks, as required by the Central Bank of Iraq, to ensure transparency and risk identification, include:
By type of collateral (e.g., unsecured, personal guarantee, property mortgage, stock pledge, etc.)
By sector (e.g., industry, agriculture, trade, services, construction, transport, communications)
By geographic location (e.g., cities, villages, rural areas, inside/outside the city where the bank is located)
By loan amount category (e.g., less than 1 million, 1–<10 million, 10–<100 million, 100–<500 million, 500 million–<1 billion, 1 billion or more)
According to the residency status of the borrower: This includes loans extended to residents versus those granted to non-resident individuals or entities.
Based on the nature of the borrower: This involves categorizing loans by recipient type, such as individuals, private sector companies, or governmental bodies.
To mitigate credit losses and avoid losses from non-performing credit, the bank must form credit loss provisions monthly, with the following minimum rates according to the classification:
Excellent Credit – No allowance for credit losses is required.
Good Credit – A provision equivalent to 2% of the total balance under this category is recognized.
Average Credit – A provision equal to 10% of the outstanding credit is maintained.
Below Average Credit – Requires a credit loss provision of 25% of the total credit classified under this level.
Poor Credit – A 50% provision is applied to the total value of credit in this category.
Credit Classified as Loss – A full provision of 100% of the credit amount is recorded.
These credit losses are recorded as a credit in the credit loss provision account and as a debit in the expense account in the income statement.
Exempted from provisioning are credits secured by highly liquid collateral (e.g., term deposits, savings deposits, pledged gold, pledged stocks if easily sellable at a value at least double the granted credit). These are classified as excellent unless they become due.
Penalty interest on non-performing credit is recorded as a debit in the "Accrued but Uncollected Interest" account and a credit in the "Provision for Interest on Non-performing Loans" account. Such interest is not recognized as income unless actually collected, according pursuant to subparagraph (c), item (2), of Article 29 of the Iraqi Banking Law No. 94 of 2004, as amended
If such interest is recognized during the performing period, it must be reversed once the credit becomes non-performing. The entry remains valid if collected within The period since the due date is less than 90 days. However, if the bank’s annual financial statements are prepared as of December 31, the accrued interest must be reversed on that date, regardless of whether the 90-day period has been completed.
Third: Credit Operations in the Sample Private Commercial BanksIn order to evaluate the credit operations of the selected banks, the assessment of capital adequacy is conducted in line with regulatory guidelines, taking into account the nature and magnitude of the risks involved, as well as the management’s capacity to address and control such risks. The analysis focuses on key financial indicators, including total assets, the volume of granted loans, and the ratio of loan loss provisions to total loans during the period from 2021 to 2023, as illustrated in Table (1) below.
| Bank Name | Year | Total Asset | Total Loans | Total Loan Loss Provisions |
|---|---|---|---|---|
| Across Iraq Bank | 2021 | 542,405,926 | 123,373,493 | 7,532,829 |
| 2022 | 615,935,604 | 180,198,40 | 19,166,780 | |
| 2023 | 535,764,591 | 208,067,330 | 23,651,469 | |
| Sumer Commercial Bank | 2021 | 334,843,250 | 19,125,445 | 9,149,201 |
| 2022 | 449,272,568 | 22,871,847 | 9,970,649 | |
| 2023 | 414,889,154 | 27,214,279 | 10,545,978 | |
| Al-Mansour Investment Bank | 2021 | 1,764,904,558 | 241,795,055 | 17,000,000 |
| 2022 | 1,827,505,325 | 274,409,595 | 17,000,000 | |
| 2023 | 1,549,536,698 | 294,982,250 | 30,000,000 | |
| Middle East Iraqi Investment Bank | 2021 | 355,829,503 | 97,729,242 | 5,807,604 |
| 2022 | 436,248,512 | 102,301,064 | 18,084,714 | |
| 2023 | 451,830,440 | 68,804,685 | 23,512,522 |
Source: Compiled by the researcher using the financial reports of the banks included in the study.With regard to the proportion of total credit facilities to overall assets, and the proportion of loan loss provisions relative to total credit, for the selected private commercial banks during the period 2021–2023, the results are presented in Table (2) below.
| Bank Name | Year | Total Loans to Total Assets (%) | Loan Loss Provisions to Total Loans (%) |
|---|---|---|---|
| Across Iraq Bank | 2021 | 23% | 6% |
| 2022 | 29% | 11% | |
| 2023 | 39% | 11% | |
| Sumer Commercial Bank | 2021 | 6% | 48% |
| 2022 | 5% | 44% | |
| 2023 | 7% | 39% | |
| Al-Mansour Investment Bank | 2021 | 14% | 7% |
| 2022 | 15% | 6% | |
| 2023 | 19% | 10% | |
| Middle East Iraqi Investment Bank | 2021 | 27% | 6% |
| 2022 | 23% | 18% | |
| 2023 | 15% | 34% |
Source: Prepared by the researcher based on Table (4).
Interpretation of the Indicators Presented in Table (2):
Across Iraq Bank demonstrated a notable increase in the ratio of provisions for loan losses to total loans, rising to 11% in both 2022 and 2023, compared to 6% in 2021. Similarly, the proportion of total loans to total assets climbed to 29% in 2022 and further to 39% in 2023. Although the provisioning rate increased, this trend reflects the bank's expanded lending activity and a concentration of assets in credit portfolios carrying higher risk weights.
Sumer Commercial Bank recorded a downward trajectory in the loan loss provision ratio, declining from 48% in 2021 to 44% in 2022 and further to 39% in 2023. The loan-to-asset ratio experienced minor fluctuations during the same period. This decline is primarily attributed to the bank’s strategic orientation toward investments in cash instruments and contingent credit lines. Despite receiving liquidity support, the ratios suggest a relatively high level of hedging compared to peer institutions.
Al-Mansour Investment Bank exhibited a gradual rise in the ratio of loan loss provisions to total loans, reaching 10% in 2023, after recording 6% in 2022 and 7% in 2021. In parallel, the loan-to-asset ratio rose noticeably to 19% in 2023, up from 15% the previous year. This upward movement indicates the bank’s growing emphasis on extending credit, which in turn heightened its exposure to credit risk, prompting a corresponding increase in provisioning.
Middle East Iraqi Investment Bank reported a sharp escalation in the provision-to-loan ratio, which surged to 34% in 2023 compared to only 6% in 2021. Conversely, the ratio of total loans to total assets declined to 15% in 2023. These developments reflect the bank’s strategic pullback from aggressive credit expansion, opting instead for a conservative approach in managing direct cash-based lending.
The strategies implemented by banks in handling non-performing loans are founded on a comprehensive evaluation of several key aspects, including the size, distribution, and directional trends of their asset portfolios. This encompasses the challenges associated with non-performing, doubtful, overdue, and restructured assets whether reflected on the balance sheet or held off-balance. Additionally, these policies involve assessing the sufficiency of loan loss provisions, the bank’s capability to detect and resolve impaired assets, the level of portfolio diversification, asset quality, and the soundness of lending frameworks and risk concentration practices. Collectively, these factors play a vital role in strengthening the efficiency and robustness of credit risk management systems.
Bank: Across Iraq
| Ratio | 2021 | 2022 | 2023 |
|---|---|---|---|
| Non-performing loans / Total loans | 16.7% | 45% | 31.5% |
| Non-performing loans / Core capital | 8.5% | 13% | 9.2% |
| Loan loss provisions / Non-performing loans | 193% | 95% | 108% |
Source: Credit Risk Management Department (modified by researcher)
Analysis:
The data in Table (3
There was a significant increase in non-performing loans (NPLs) in 2022, where the ratio reached 45%, compared to 16.7% in 2021. In 2023, this ratio dropped to 31.5%, which still represents a high level, negatively affecting the bank's credit policies.
The ratio of NPLs to core capital also rose in 2022 to 13%, up from 8.5% in 2021, then decreased to 9.2% in 2023. This indicates that a considerable portion of the bank’s core capital is exposed to credit risk due to problematic loans.
The loan loss provisions to NPLs ratio fell sharply in 2022 to 95%, down from a very high 193% in 2021. However, it improved slightly in 2023 to 108%, indicating an increased effort by the bank to strengthen its provisioning coverage, but still lower than in 2021.
These fluctuations reflect instability in the bank's risk management regarding non-performing loans, particularly in 2022. While there was some recovery in 2023, the high ratios still signal persistent credit quality issues that require more robust and proactive risk management policies.
| Ratios | 2021 | 2022 | 2023 |
|---|---|---|---|
| Non-Performing Loans to Total Loans | 47.6% | 62.5% | 91.4% |
| Non-Performing Loans to Core Capital | 33% | 7% | 6.4% |
| Loan Loss Provision to Non-Performing Loans | 15% | 22.8% | 67.6% |
Source: Credit Risk Management Department (Reformulated by the Researcher)
An analysis of the data presented in Table (4) reveals fluctuations in the proportion of non-performing loans at Sumer Commercial Bank over the study period. In 2022, the ratio stood at 5.62%, down from 6.47% in 2021, before rising again in 2023 to 4.91%, a relatively high level that reflects negatively on the bank’s credit risk posture.
The ratio of non-performing loans to core capital showed a marked improvement, declining significantly to 7% in 2022 from 33% in 2021, and further dropping to 4.6% in 2023. Additionally, the coverage ratio—measured as provisions for loan losses relative to non-performing loans—improved, rising from 15% in 2021 to 22.8% in 2022. However, it then declined to 6.67% in 2023, following a level of 8.22% in the prior year.
| Ratios | 2021 | 2022 | 2023 |
|---|---|---|---|
| Non-performing loans / Total loans | 6.26% | 5.32% | 61% |
| Non-performing loans / Core capital | 21.5% | 14% | 36.4% |
| Loan loss provisions / Non-performing loans | 59% | 66% | 40% |
Source: Credit Risk Management Department (Adapted and Analyzed by the Researcher)
The data in Table (5) indicate a downward trend in the proportion of non-performing loans during 2022, with the ratio declining to 5.32%, compared to 6.26% in 2021. However, a sharp and concerning rise was recorded in 2023, as the ratio surged to 61%, representing a critical level with potential adverse implications for the bank’s credit policy effectiveness.
Regarding the ratio of non-performing loans to core capital, there was a decline to 14% in 2022 from 21.5% in 2021, followed by a substantial increase to 36.4% in 2023. Similarly, the coverage ratio representing the proportion of loan loss provisions to non-performing loans improved in 2022 to 66%, up from 59% in the previous year. Nonetheless, it declined again in 2023 to reach 40%, suggesting a possible deterioration in risk coverage despite rising default exposure.
| Ratios | 2021 | 2022 | 2023 |
|---|---|---|---|
| Non-Performing Loans / Total Loans | 6% | 19.5% | 49.6% |
| Non-Performing Loans / Core Capital | 13% | 29.9% | 33.4% |
| Loan Loss Provisions / Non-Performing Loans | 29% | 15% | 30% |
Source: Credit Risk Management Department (Reformulated by the Researcher)
The indicators presented in Table (6) highlight a noticeable upward trend in the volume of non-performing loans at Middle East Iraqi Investment Bank. In 2022, these loans accounted for 19.5% of the total, a considerable increase from 6% in 2021, and the ratio further escalated to 33.4% in 2023. Such a high percentage reflects a growing risk exposure that poses challenges to the bank’s credit policy framework.
Moreover, the ratio of non-performing loans to core capital increased significantly rising from 13% in 2021 to 29.9% in 2022, and reaching 33.4% in 2023. As for the coverage ratio, represented by loan loss provisions relative to non-performing loans, it declined to 15% in 2022, compared to 29% in 2021. However, it recovered slightly in 2023, reaching 30%, indicating an effort by the bank to improve its risk coverage amid rising credit defaults.
In light of the preceding discussion regarding the accounting treatment of financial asset impairment under IFRS 9, several points emerge that warrant a critical review of certain aspects of the guidance issued by the Central Bank of Iraq. These include, but
First: The methodology adopted in calculating loan loss provisions based solely on fixed percentages of total credit exposures—without taking into account the existence of tangible collateral such as mortgage-backed guarantees—can significantly impact both the value and timing of reported profits. This impact is reflected in the following aspects:
Provisioning against performing credit (not yet due): Applying provisions to the full value of such credit may result in the recognition of unrealized losses, thereby artificially reducing reported profits in a manner that does not reflect actual credit risk.
Provisioning against medium to loss-classified credit up to 100%: When provisions are calculated without considering real collateral provided by borrowers (e.g., mortgaged assets), it creates a potential avenue for management to exercise discretion in adjusting loan loss provisions. This flexibility enables manipulation in the timing and valuation of credit loss recognition, ultimately distorting the financial statements by affecting both profit levels and their timing of disclosure.
Second:The regulatory guidance failed to adequately address the necessary disclosures within the financial statements concerning credit risk management to which the bank may be exposed. For enhanced transparency and alignment with international standards, banks should be required to disclose the following elements:
Risk management frameworks and procedures adopted to monitor and mitigate credit risk exposures.
Comprehensive quantitative and qualitative data regarding credit losses recognized during the reporting period.
Detailed information on credit risk exposure, including sectoral or geographic concentrations where applicable.
Nature and value of collateral and other credit enhancements obtained to secure lending operations and mitigate expected credit losses.
The researchers believe that the expected credit losses in private commercial banks (research sample) during the period 2021–2023 are largely due to the increasing cases of loan repayment defaults, which is a fundamental factor affecting net profit. The reasons for this default vary between internal and external factors. Internally, it relates to the inefficiency of credit granting procedures within banks, such as the absence of an objective study of the credit decision and ignoring the assessment of potential risks (management risks, market risks, capital risks, and real estate guarantees). It is noted in some cases that credit facilities are disbursed in lump sums without effective supervisory follow-up. On the other hand, customer practices contribute to deepening the volume of losses through providing misleading or incomplete information about their financial status or due to their lack of technical and administrative competence in using the loan for financing purposes suitable to the nature of the funded activity, which leads to their failure to meet their obligations to the bank.
Externally, the unstable economic conditions experienced by Iraq and the region during the mentioned period, particularly the repercussions of the Russia-Ukraine war and sharp fluctuations in the US dollar exchange rate against the Iraqi dinar, negatively affected the creditworthiness of clients. These changes also impacted the banks’ credit policies themselves and weakened their ability to hedge against expected credit losses.
First: Conclusions
Expected credit losses represent one of the most significant negative factors affecting banks’ net profit, due to the continuous rise in default cases and the weak credit management efficiency in some banks under study.
The analysis results show that Iraqi banks (the sample) rely on traditional procedures in loan classification, which weakens the ability to predict losses and limits the effectiveness of early hedging tools.
An imbalanced relationship was observed between the increase in loan loss provisions and net profit, as some banks form high provisions as a reaction to past defaults rather than as a result of proactive credit analysis, which directly affects their financial results.
There is variability in the level of compliance with IFRS 9, especially regarding the measurement of expected credit losses and updating predictive models based on client information and credit behavior.
Weakness in accounting disclosure is noticed concerning expected credit loss data, including the nature of collateral, loan classifications, and restructured asset types, which reduces the transparency of financial statements.
External economic and political variables, such as fluctuations in the Iraqi dinar exchange rate and regional events like the Russia-Ukraine war, impacted the rise in expected loss ratios and the decline in profitability of some banks.
Some banks still lack dynamic analytical tools for loan portfolios, which complicates ongoing risk assessment and its impact on profitability and financial stability.
Second: Recommendations
Enhance commitment to fully applying IFRS 9, especially in measuring credit losses based on future expectations and linking them to net profit analysis, alongside training qualified personnel for effective implementation.
Reconsider the Central Bank of Iraq’s instructions on provisioning calculations, with necessary consideration of real estate and commercial collateral when estimating actual losses, to help reduce the negative impact on profits.
Adopt flexible credit classification systems based on real-time and behavioral indicators, updated monthly to keep pace with changes in customer behavior and economic conditions, reducing the estimation gap between actual and expected losses.
Improve accounting disclosure in financial statements by requiring banks to disclose credit exposure levels, types of collateral, and risk management procedures, ensuring enhanced user confidence and understanding of the impact of these losses on profit.
Strengthen qualitative analysis tools for clients within credit policies, not relying solely on historical financial data, which increases the accuracy of loss prediction before occurrence.
Deepen coordination between risk, credit, and internal audit units to ensure accurate and updated information exchange affecting loan granting decisions and their impact on profitability.
Expand the use of advanced statistical models to analyze non-performing loans, linking their results directly to IFRS 9 provisions, enhancing management’s ability to absorb losses without materially harming net profit.
Diversify loan portfolios by sector and geographic area to reduce credit concentration and strengthen protection against losses resulting from volatility in a specific sector or region.
Review banking and regulatory laws to ensure their alignment with international standards, providing legal protection for banks in recovering non-performing loans, thus supporting their financial position and reducing pressure on profits.