What tools do you need to get started with your IFRS 9 implementation?
6 mins read

IFRS 9 implementation has three primary areas of focus for entities that hold, own, owe or trade financial assets and liabilities through their balance sheet. The three areas are:

  1. The correct basis of carrying costs for an instrument based on its properties and the underlying business model of the entity. This is addressed under the recognition and measurement section of the standard.
  2. Estimates of impairments for assets that have the potential to experience a credit related loss in value or have already experienced it. This is addressed under the expected credit loss (ECL) section of the standard.
  3. Hedging of exposures linked to changes in market prices and the rate of financial benchmarks and commodities. 

We address the IFRS 9 implementation guidelines for the first two areas (a) and (b) in this note.  What does the implementation roadmap for IFRS 9 looks like for a bank? What is the break-down of the work effort required? What are the key components or steps required for a successful IFRS 9 implementation at a financial institution?


IFRS 9 is applicable on financial securities held on both sides of the balance sheet.


There are three carrying costs models supported by IFRS 9. They are

  1. Amortized cost. Assets and liabilities using this model are carried at amortized costs.  
  2. Fair value through other comprehensive income (FVOCI). Asset and liabilities under this model are carried at fair value and changes in fair value pass through other comprehensive income.  
  3. Fair value through P&L (FVPL).  Asset and liabilities under this model are carried at fair value and changes in fair value pass through P&L.

Pre and post IFRS 9 mapping

From an IFRS 9 implementation point of view the first step is to map the old chart of account to the new one. This is a good starting point for most implementations. Therefore, there is a requirement to provide a utility or tagging tool that would ideally classify and tag elements within the general ledger under the correct model. While the tool may suggest a default classification for a given asset or liability, it would also need a manual review and sign off using standard maker, checker functionality before the recommended classification is approved and used.

We build or use a rule based classification engine for this purpose. It walks through the client chart of account and designates nodes in the accounting tree in three categories in terms of carrying costs. Amortized cost, Fair value through Other Comprehensive Income (FVOCI) and Fair value through Profit and Loss (FVTPL).

When it comes to treasury and investment asset and liabilities the engine can simply look at prior account classifications and assign a default category. We review classification rules used for such an engine next.

IFRS 9 Classification rules

We start first with data. For IFRS 9 implementation, start with the primary chart of accounts and a list of financial securities held on both sides of the balance sheet by accounting classification. This is the first step. Under most cases, there is a clear map between existing accounting categorization from the pre-IFRS 9 world to the revised accounting classification under the post IFRS 9 world.

To implement the map properly we need to add two new data attributes for each asset and liability class. They are underlying business model and the post IFRS 9 classification.  The business model attribute is driven by business rules. The post IFRS 9 classification is driven by business rules and the original accounting treatment of the instrument in question.

For audit and quality assurance purposes it is important to also retain the old accounting classification (HTM, AFS and HFT) from the pre-IFRS 9 world.  

The business rules for determining business model are based on two drivers. The first is if the security will be held to just collect contractual cash flows or would it also be traded. The second deal with the nature of contractual cash flows.  

  • Business Model A – Hold to collect AND contractual cash flows are sole payments of principal and interest (SPPI test).
  • Business Model B – Hold to collect, sell or trade AND contractual cash flows are sole payments of principal and interest (SPPI test).
  • Business Model C – Neither A nor B.

Most loans and deposits on a bank balance sheet meet the sole payment of principal and interest test. Structured products, products with contingent cash flows and derivative instruments tend to fail the sole payment of principal and interest test. For conventional banks in the MENA and Far East region, most loans remain on the books of the bank till they mature or are refinanced. Loan sales, loan origination and repackaging of loans as a tradeable product is not as common as it is in North American and European capital markets.

All assets and liabilities that fall under Business Model A can be held under amortized cost.

All assets and liabilities that fall under Business Model B can be held under Fair Value under OCI (FVOCI)

All assets and liabilities that fall under Business Model C can be held under Fair Value under P&L (FVPL)

In addition to the business rule test, it is also possible to review classification from a product and pre-IFRS 9 classification point of view.

Collectively when we read the two set of rules together we can a summarize

  1. Most standard products on the asset side – short term loans, loans, advances and working finance products will get classified under amortized cost.
  2. Investments in treasury securities, sovereign debt and high quality liquid assets including corporate debt classified under the held to maturity category will also be classified under amortized cost.
  3. All securities currently held under Available for sale (AFS) would be classified under FVOCI
  4. All securities currently held under Held for Trading category and derivative instruments would be classified under FVPL
IFRS 9 - core components
IFRS 9 implementation modules

Module A – IFRS 9 – Security classification engine

The classification engine uses the rules above to classify all financial securities held on the client balance sheet under the appropriate IFRS 9 reporting categories.

Module B – Expected Credit Loss (ECL) Engine

The second engine used by the platform needs to calculate 12 month and lifetime Expected Credit Loss (ECL) for a given type or class of financial instruments.

The engine has three core components or sub models.

A Probability of Default (PD) module estimates both backward and forward looking projected PD and PD term structure using a number of selected leading economic indicators and historical PD estimates.

A Loss Given Default (LGD) module for estimating historical loss norms.

A limits and limits utilization trend calculator for estimating Exposure at Default (EAD)

There are two available models for calculating the Probability of default.

ECL ModelSimplified Model – Credit Rating Transition Model. Uses historical internal credit rating transitions to estimate Probability of Default for 12 month ECL. Usage of this model requires availability of historical credit rating data for all clients and products for the last 5 years. While current data may be available, for most bank still using paper or Excel based rating systems, availability of historical data maybe a challenge.

ECL ModelAdvance Model – Historical Payment Behavior. Uses historical payment behavior data to estimate Probability of Default for 12 month ECL. Usage of this model requires availability of historical interest and principal data for the last 5 years. Like the credit rating history above, extracting this data from the General Ledger would require a custom data extraction routine.

Model selection is driven by available historical data and client decision to use the approach that fits with their internal level of sophistication.

To estimate Loss Given Default historical loss norm data is collected and tabulated using 5 years of non-performing loans, provisions, write-offs and recoveries from the special asset and non performing unit of the bank. The system uses a loss norm engine to collect, classify and attribute loss norms based on business lines, products, client segments and sectors and economic cycle. The classification is important to track and refine adjustments to the forward looking ECL model required by IFRS 9.  Once again while this information may be available, for small banks with small client portfolios, there may not be sufficient default or loss norm data available to accurately fill in the LGD grid by all possible combinations and parameters.

The Exposure at Default estimate is based on tracking allocated client limits and their utilization over a three to five-year period. Similar to rating data for banks with inadequate or legacy systems, tracking client limit allocation and utilization history may be a challenge.

Module C – IFRS 9 – Disclosure and reporting package

Collects and collates outputs from all modules and sub modules and generate the appropriate carrying costs for each identified and tagged financial security in compliance with IFRS 9 guidelines.

Roll up ECL values, amortized cost and carrying cost across the chart of account into standard IFRS 9 reporting templates

Module D – Forward looking economic model

Forward looking company economic model that generates an adjustment factor for the PD term structure based on high level leading economic indicators. During the first year of client implementation, the forward looking model will use generic leading indicators to estimate the timing of the economic cycle. Using the timing data, it creates adjustment factors for the forward looking PD model. Over a period of time, as more data becomes available, the model begins to use client-specific data elements to create more customized adjustment factors.

A forward looking model could use economic growth, inflation rates, interest rates, currency exchange rates, consumer sentiment and loan loss provisions to link projected future credit performance to leading economic indicators.