Implementing the IFRS 9’s Expected Loss Impairment Model: Challenges and Opportunities

The IASB published the IFRS 9 Financial Instruments in July 2014, completing its response to the financial crisis by improving the accounting and reporting of financial assets and liabilities. It replaced the IAS 39 Financial Instruments: Recognition and Measurement with a unified standard that covers three areas:

• Classification and measurement: determines how to account for financial assets and liabilities in financial statements and their ongoing measurement.
• Hedge accounting: launches a reformed model for hedge accounting, with enhanced disclosures about risk management activity.
• Impairment: introduces a new expected loss impairment model that will require more timely recognition of expected credit losses.

Impairment is the biggest change for banks moving from IAS 39 to IFRS 9. Forecasting expected credit losses instead of accounting for them when they occur will require institutions to greatly enhance their data infrastructure and calculation engines. The timeline given by regulators – compliance by 2018 – presents a considerable challenge, especially given the complexity of the new systems and workflows to be put in place.

Understanding the new impairment model

Under IAS 39 accounting standards, credit losses were taken into account when the loss occurred; hence the term “incurred loss.” With the new IFRS 9 standards, impairment recognition will follow a forward-looking “expected credit loss” model.

According to the new model, credit exposures will be categorized into one of three stages, depending on the increase in credit risk since initial recognition (Figure 1). IFRS 9 requires that when there is a significant increase in credit risk, institutions must move an instrument from a 12-month expected loss to a lifetime expected loss. In making the evaluation, the institution will compare the initial credit risk of a financial instrument with its current credit risk, taking into consideration its remaining life.

In stages one and two, the interest revenue will be the effective interest on gross carrying amount; in stage three it will be the effective interest on amortized cost.

Figure 1. Increase in credit risk since initial recognition: three stages

Source: Moody's Analytics

Determining expected losses

In order to calculate 12-month and lifetime expected losses, banks should apply models on credit risk (PD, LGD), balance sheet forecast (prepayments, facility withdraws) and interest rates (discount factors).

On the credit risk side, PD and LGD models are needed to satisfy the new impairment model.

PD models: IFRS 9 standards require an estimate of probability of default (PD) that is consistent with the following principles:

• Considers all relevant information
• Reflects current economic circumstances (i.e., it is a best estimate rather than a conservative estimate)
• Provides the likelihood of a default occurring within the next 12 months or during the lifetime of the instrument
• Includes forward-looking economic forecasts
• Existing internal ratings-based (IRB) Basel models can be reused but particular attention should be paid to point-in-time versus through-the-cycle models

LGD models: IFRS 9 requires an estimate of loss percentage that is consistent with the following principles:

• Considers all relevant information and includes a forward-looking element
• Reflects current economic circumstances (i.e., is a best estimate rather than an economic downturn estimate)
• Considers only costs directly attributable to the collection of recoveries

Complying with IFRS 9 requirements

Financial Institutions will face some challenges to fulfilling these IFRS 9 requirements, including:

• Retrieval of old portfolio data, especially for the transactions that originated before the advanced internal ratings-based (A-IRB) models were introduced.
• Classification of the transactions at origination. Products will need to be categorized a priori (contractual cash flow test) or create a workflow to capture the classification and initial credit worthiness. An additional effort could be required to identify those products that can be considered out of scope (e.g., short-term cash facilities and/or covenant-like facilities).
• Management of standardized approach portfolios (if no model is available and/or data is not available).
• Flexibility of implementations (e.g., on models and thresholds) according to asset classes and model availability. For instance, a granular approach may be needed for one part of a portfolio (e.g. wholesale portfolio), while another portfolio (e.g., retail) may require provisioning.
• Historization of data for the new transactions.

To overcome those challenges, banks should set up a dedicated group of subject matter experts and facilitate close collaboration between the architecture team (to ensure availability of data and infrastructure) and the modeling team (to ensure models are accurate and can rely on available data).

Banks may either enhance existing solutions or use brand new products to achieve compliance. In either case, they should plan and execute an implementation project in the next two years.

Implementing a rigorous workflow

Financial institutions should ensure that their systems can handle such granularity of data while maintaining high quality standards. They should use a rigorous workflow to produce these outputs consistently (Figure 2).

Figure 2. Calculation process workflow

Source: Moody's Analytics

Figure 2 illustrates how banks should gather data on:

• Exposures
• Counterparties
• Credit risk mitigants

From this data, banks can implement models on PD, LGD, and exposure at default (EAD) profiles, using market data and macroeconomic forecasts to get 12-month and lifetime expected loss forecasts (discounted at current interest rates).

Then, based on exposure and counterparty characteristics, allocation between stages 1, 2, and 3 sends the final EL provision to accounting systems.

An example of such a calculation process would include:

• The interest rate of each loan is used to calculate the discount rate.
• EAD is calculated monthly for the next 360 months, based on the amortization of the contractual balance of the loan, plus up to six months of arrear payments.
• The PD is derived from a default curve calibrated for the portfolio. The age of the loan will give the starting point on the default curve. This PD is then scaled to the loan, using the Basel point-in-time PD.
• The LGD is derived from the loan-to-value (LTV) using a lookup table. The LTV uses the value of the property covering the loan and takes into account EAD from all other loans eventually covered by this property.
• The expected loss for each of the next 360 months is the product EAD*PD*LGD divided by the discount rate.h
• The EL is then summed up for the first 12 months and for the full life of the loan. These two figures can then be used by accounting systems.

Conclusion

IFRS 9 is the next regulatory “tsunami.” Like Basel II and Basel III, it requires banks to make huge investments in models, data, and infrastructure for long-term implementation.

The output of IFRS 9 will be a more resilient financial system, capable of forecasting losses instead of accounting them after they occur, which will give the investor community greater confidence and add transparency to credit losses forecasts. Furthermore, banks will leverage such an implementation to manage, in a more accurate manner, their risks and forecast their capital and profit and loss.