Prepare for IFRS 9 convergence with better IT and data practices

by Chrysostomos Kridiotis, SAS

One of the biggest changes in the financial industry in the last decade emerged from the financial crisis of 2007-2008: the International Financial Reporting Standards 9 (IFRS 9).

After the breadth and depth of the economic downturn was known, the International Accounting Standards Board (IASB) published the IFRS 9 Financial Instruments in July 2014, which replaced the IAS 39 Financial Instruments: Recognition and Measurement. IFRS 9 covers three areas:

  1. Classification and measurement: how to account for financial assets and liabilities in financial statements and their ongoing measurement.
  2. Hedge accounting: a reformed model for hedge accounting, with enhanced disclosures about risk management activity.
  3. Impairment: a new expected loss impairment model that will require more timely recognition of expected credit losses (ECL), as opposed to the current “incurred loss” model.

The IFRS convergence is changing the way that firms account for their activities, manage risk and how they manage their data. For example, according to a Deloitte industry survey, the change in the impairment standard – from an incurred approach to a forward-looking approach – will significantly affect the majority of large banks.

Source: Fifth Global IFRS Banking Survey: Finding your way, Deloitte, 2015.

Many banks initially approached IFRS convergence as an accounting challenge first, then as a risk challenge. They did not consider the significant role played by IT systems, processes and infrastructure. In general, the impact on IT from IFRS conversions creates a need for additional data, revised calculation tables and a new governance framework.

To facilitate these challenges, IT will need to modify, remap, reconfigure or even start anew with different systems. Here are the four major IT challenges facing banks as they prepare for IFRS 9.

New “big” data and the IFRS convergence

Banks must collect and aggregate data from disparate sources within the organization when classifying their portfolio and determining ECL. Some of this data was not being collected in the past. An example would be macroeconomic data, such as GDP, house price index, interest rates, etc.

Firms will also need more internal data than before. For example, to calculate the ECL, they need historical information, dating back a number of years. Often, some of this data predates the information collected by the bank. This includes individual account data, which must be stored, managed and reconciled with the bank’s general ledger at an aggregated level.

A proper governance structure is necessary around these information streams for traceability and auditability purposes. It is imperative for banks to revise their current infrastructure to manage the vast amount of granular data collected as well as the complex calculations performed.

New models

To address the forward-looking nature of IFRS 9, banks also need to create new models to cover macroeconomic parameters, prepayments, collateral values and other factors. This adds overhead for banks, as it increases the number of models to design, develop and deploy.

Here, banks need a robust framework, both from a business and technological point of view, to support the model development, validation and deployment processes. In addition, due to the large number of models, there needs to be some automation when it comes to managing these models. This could include the status of each model and the deployment level within the organization.

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Integration of risk and finance

IFRS 9 is a great opportunity to improve the risk and finance integration process, which is also discussed in BCBS 239: Risk Data Aggregation and Reporting. This integration is only achievable if banks set up the right IT architecture which to support the integration between the risk model outputs with the accounting stream. Banks need to apply appropriate automation and controls, with clear data lineage and management oversight.

The first requirement is a data reconciliation between finance and risk systems at every step of the ECL measurement process. This reconciliation provides the confidence that there is consistent information flowing between the two functions. As the Deloitte survey showed, this is a prevalent concern for financial reporting.

Source: Fifth Global IFRS Banking Survey: Finding your way, Deloitte, 2015.


The technological infrastructure required to support IFRS 9 compliance needs to be fully auditable and flexible enough to adapt to any amendments to the current regulation. The internal processes designed and implemented for compliance with the new standard will be under severe scrutiny by the regulatory authorities and the auditors. Excel-based calculations and processes will no longer be acceptable without following a controlled process with a proper, documented justification.

As a result, banks will need to create a technological environment to provide efficient documentation, change controls, model management, traceability, workflow and audit trails to avoid manual processes.

Banks should see this challenge as a part of a larger set of regulatory requirements pressing them to re-engineer their technological and data infrastructures. As part of the IFRS convergence, banks can effectively address other regulatory requirements that include stress testing, BCBS 239, regulatory capital calculations and capital planning for ICAAP purposes. In the Deloitte survey, banks responded that the forecasting of economic conditions for IFRS 9 can be covered through existing stress testing models.

Source: Fifth Global IFRS Banking Survey: Finding your way, Deloitte, 2015.

Once banks create a more robust environment to meet their regulatory challenges, there’s also then opportunity to upgrade finance and risk infrastructures along the way. This can provide even more integration and efficiency within the organization – based on a more comprehensive technical and business framework.

Chrysostomos Kridiotis is a SAS sales manager, managing the office of SAS in Cyprus and the banking industry clients of SAS in Bulgaria. He works closely with organizations, mostly in the financial sector, to identify business problems and needs and propose solutions that have to do with data management, analytics and business intelligence.


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