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How IFRS 9 can unite risk and accounting

Regulatory information brief part I – Modelling

How IFRS 9 can unite  risk and accounting

IFRS 9 will present the opportunity for organisations to greater align their risk and accounting functions. Bloomberg examines how ensuring effective communication and collaboration will be key to achieving this and for getting the best from the new standard 

International Financial Reporting Standard (IFRS) 9 presents a unique opportunity for an alignment between risk and accounting functions, which if grasped could usher in great economies of scale, streamlined reporting and a keener understanding of accounting, regulatory and capital impacts across business silos. 

The incoming accounting framework requires the definition of default used to estimate expected credit losses (ECLs) to be aligned with that used for internal credit risk management purposes. It therefore makes sense for risk and accounting to unify the processes and models used to generate these loss assessments. Doing so would eliminate inefficiencies around reconciling datasets and will allow firms to develop standardised key performance indicators, which in turn would foster the consistent measurement of credit risk across the entire organisation.

Harmonisation of processes would also help transform the credit calculation and review workflow from something that takes place within multiple siloed functions into a single centralised activity. A number of firms take a sequential approach to running internal credit assessments, producing them once for risk‑weighted asset purposes, again for stress testing and once more to satisfy accounting requirements. To optimise this process, firms could instead build a ‘golden data source’ and use this as the input for unified IFRS 9, risk‑weighted asset and Pillar III credit risk assessment purposes. 


Advantages of increased alignment

Integrating data sources and aligning credit risk models is not only prudent – the process could also produce spillover benefits that improve business performance and consequently shareholder returns. If the front-line business and second-line risk functions use the same financial methodologies as those for reporting financial statements, capital efficiencies will ensue as accounting numbers invariably drive business decisions, and these will now accurately reflect how the business is run. 

There is then the value firms can extract from these richer consolidated credit datasets. These could be put to work as inputs to advanced analytics programs – including machine‑learning algorithms – enabling firms to gather actionable insights that could benefit the front-line business. For example, such analysis could be used to predict client behaviour and improve early warning systems that flag when an obligor is likely to become delinquent. These could then be used to refine pricing, underwriting and product mix decisions.

In addition, IFRS 9 processes can be leveraged for stress testing. By consolidating existing stress-testing infrastructure with the ECL calculation engine, firms can produce detailed pictures of their businesses under base-case and downside scenario shocks. These pictures would be relayed to shareholders in financial statements if the scenarios were to occur in reality, and could then be used to tailor forward business planning and budget setting.

There is a series of transformations firms must undergo in pursuit of this alignment, which begins with underlying data. Finance, risk and regulatory functions may currently deploy similar, but not identical, datasets in their analysis of credit exposures. For instance, one probability of default (PD) dataset could be used to generate capital requirements, and another could be used to calculate provisions under the existing International Accounting Standard (IAS) 39. 

Establishing a ‘golden source’ of credit risk data encompassing PDs and loss given defaults over multiple time horizons, as well as ancillary information such as credit rating agency reports, would lay the foundation for the desired type of alignment. These could be established in-house, by consolidating and cleansing data that currently resides in multiple systems and rerouting the data feeds for the various credit models to this single source. Firms could alternatively turn to external data providers, trusting in their specialist data management and validation expertise to provide the requisite information to each function. 


The value of communication

Establishing coherent lines of communication between functions is also vital. Take, for example, the IFRS 9 business model test – this requires firms to distinguish between assets that are ‘held to collect’ and ‘held to collect and sell’, and to subject them to a ‘solely payments of principle and interest’ test. This two-part methodology determines which assets are then run through the ECL framework. 

Under IAS 39, the business model test only exercised the finance and accounting functions. With the alignment of risk and finance required under IFRS 9, however, the risk function will need to know which instruments belong to which business model for the purposes of running regulatory capital calculations. Just knowing which assets are accounted for at amortised cost and fair value will not be enough, as the business model test can allocate assets that currently reside in the latter category to the former, and vice versa, contrary to current practice. 

Then there’s the question of the models themselves – the parameterisation of credit models varies depending on the purpose for which they are deployed. Internal ratings-based (IRB) models for calculating regulatory capital use a through-the-cycle methodology to assess credit risk, measuring average losses over rolling 12-month periods. IFRS 9 ECL models, on the other hand, rely on point-in-time loss estimates based on the prevailing credit conditions at each quarter-end.

Yet as the underlying data inputs are the same, firms recognise the benefits of leveraging their existing IRB models to compute ECLs – policymakers also recognise this as a valid expediency. The European Banking Authority stated in November 2016 that use of existing model infrastructure would allow for greater consistency between prudential and accounting frameworks. 

Resources saved by not building a model infrastructure from scratch for IFRS 9 could instead be channelled towards tweaking front-office pricing and underwriting models to better reflect the new provisioning regime. The finance function could go beyond basic compliance and have data managers feed the ECL insights to all corners of the business. Product managers, for instance, could use ECL data related to retail portfolios to better tailor future consumer products to their clients’ risk profiles. 

Other benefits include streamlining credit risk reporting by cutting down on waste and duplication. As risk data will now be used for financial reporting, there will be no need for two separate credit risk reports for regulatory and accounting purposes. 

Retrofitting IRB models for IFRS 9 comes with its share of challenges, however. Wholesale changes to IT systems to assimilate the new credit models into existing calculation engines would need to be made. Firms would also have to ensure their current governance processes and quality controls are appropriate for IFRS 9 implementation, and that the changes made to existing models for calculating ECLs are properly scrutinised and documented. 


Technical advances

Technical adjustments would also need to take place, as IRB model PDs are calculated using a different set of assumptions than those appropriate for IFRS 9. The Global Public Policy Committee – which comprises representatives of the six largest accounting networks – lists some of the tweaks that would have to be made, including converting the PDs from a conservative to an unbiased estimate, aligning the definition of default used in the model with IFRS 9 standards and removing any bias towards historical data that does not reflect management’s current view of the future. 



Aligning credit risk management processes implies more than just a technical upgrade, however. It also demands the breaking down of operational silos and the improvement of communication across functions. Having access to uniform data is useful, but it is only made meaningful to a business when different desks are able to talk to each other and identify interdependencies that hinge on the use of certain data points.

Opening new avenues of communication and increasing collaboration across functions are vital to maximising the value of the ECL framework firm-wide. 


Bloomberg regulatory informaion briefs 

Part I of a two-part series of Regulatory Information Briefs covering IFRS 9. For more information on accounting, modelling and risk management under IFRS 9, please see Part II in this series – Unified data – Key to IFRS 9 implementation.

Bloomberg’s IFRS 9 datasets enable organisations to simplify and automate the complex challenges presented by IFRS 9 with robust and defensible data. Bloomberg’s ECL datasets and analytics utilise its models to calculate probability of default, loss given default, exposure at default and full 12-month and lifetime ECL amounts on a lot-by-lot basis.

Chartis named Bloomberg’s IFRS 9 ECL datasets and analytics best-of-breed, providing further confidence to clients looking for support in addressing the data, modelling and calculation challenges of the new impairment standard.

Bloomberg Regulatory & Accounting Products enable risk, compliance, asset managers, insurers, accountants and other financial organisations to navigate the increasingly complex liquidity, regulatory and accounting environment and comply with confidence.

To learn more about Bloomberg’s data solutions for IFRS 9 visit: www.bloomberg.com/IFRS9

Download the white paper, How IFRS 9 can unite risk and accounting

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