IFRS9

When IFRS9 came into force in January 2018, many in the credit risk world thought the hard part was over. After all, conventional wisdom suggested the new standard would cause a one-off shift in expected loss provisioning and life would return to normal.

However, as firms are now rapidly gaining experience with the first generation of models, a number of practical implications have sprung up with far reaching consequences on business models beyond the challenge of accounting for potential credit losses. One such challenge is the adequate pricing of the implied economic costs of credit under the new standard.

The IFRS9 Learning Curve

The most obvious way to exploit the insights generated by IFRS9 is to incorporate into pricing engines the forward-looking loss estimates it generates. After all, such estimates should be much more transparent and accurate than relying on Basel III or IAS39 estimates of loss. Any bank using risk-adjusted returns on capital (RAROC) should find it prices risk more accurately if it uses the IFRS9 expected loss curve as an input, instead of the flat estimates generated under Basel III.

Not all firms are that sophisticated. But even those which are can sometimes show an odd lack of co-ordination. The ‘right hand’ may know that the average loan within a credit portfolio front book has a certain chance of ‘significant increase in credit risk’ that would classify it as Stage 2 under IFRS9. That, in turn, would mean recognising a lifetime expected loss on the balance sheet against the loan and holding capital to absorb such a loss. However, the ‘left hand’ sometimes ignores that knowledge when deciding minimum acceptable returns to cover the risk.

There is, however, another economic cost that we think banks and building societies should consider to sharpen their pricing and attract the right customers at improved margins. It also pivots around the three-stage credit deterioration model for calculating impairment. But with a twist.

On Probation

The point to bear in mind is that credit assets will be moving back and forth between IFRS9’s 1st and 2nd Stages, as credit quality deteriorates or as accounts cure. Much will depend on the way in which firms define and put into practice their understanding of the term ‘significant increase in credit risk’. But regardless of the definition and frequency of calculation, there is a real chance that accounts could end up oscillating between Stages 1 and 2. And, depending on the staging threshold, economic scenario and product, the impairment at Stage 2 will be significantly higher than at Stage 1. This impairment volatility would be enough to cause material volatility in balance sheets, profit and loss statements and capital requirements. To mitigate this volatility, firms generally deploy a ‘probation period’ lasting between six and twelve months before restoring an account from Stage 2 back to Stage 1.

Consider the following example: For an account that moves from Stage 1 to 2 and back to 1, the capital consumed during the lifetime of the loan will look like the grey line in the chart below.

Fig1

The shaded grey area represents the capital cost borne by IFRS9 Stage migrations for accounts that move from Stage 1 to 2 and then back again. Note that this capital cost is in a sense of profit line effect, which by extension, temporally freezes parts of retained earnings (CET1). Its width represents the length of time such an account is in Stage 2 plus the probation period before it is returned to Stage 1. Its height represents the differential in impairment between Stages 1 and 2. And the slope of its top and bottom sides represents the rate of amortisation.

Weight for it…

Some loans in a portfolio will follow the grey line, meaning that overall, the portfolio will never follow the simple green path. Here’s where a Staging Value Adjustment (SVA) comes in.

The SVA combines the two expected loss curves (green and yellow) into a single, Stage-weighted EL curve (orange) that takes into account the expected CET1 capital cost and can be used to price risk more accurately.

Fig2

Remember that IFRS 9 effectively causes banks and building societies to set aside additional CET 1 capital, over a probation period minimum, to compensate for the fluctuation of lending credit worthiness. It is worth noting that this CET1 capital cost is at the cost of equity, not the cost of debt for the industry, hence an expensive source of burden. If this part of economic cost is not considered properly, certain products might be seriously mis-priced in the new accounting world.

As is readily apparent, SVA will be sensitive to:

  • Product Type and Maturity: the SVA yields different results for revolving or amortising loans;
  • Risk Volatility: the more the underlying risk indicators tend to fluctuate, the longer the probation period is likely to be; and
  • Economic Conditions: if the long-term economic trajectory improves, this will be reflected in lifetime expected losses before they show up in 12-month expected losses. The SVA picks up this change and can feed it into pricing.

Those who can cascade the loss insights properly through the customer lending rates, will be able to ensure that the margins accurately reflect the risk and capital consumption and enjoy a competitive advantage on a risk-adjusted basis. Firms who are not able to transparently price the SVA will find risk adjusted returns diminished when adverse conditions increase the capital consumption of the credit portfolio.

The SVA applied to Retail Mortgages

To see how these factors affect a real portfolio, we compare the outputs of two pricing engines on a retail mortgage book: Both pricing approaches used RAROC simulations but one featured standard flat-lining IRB expected losses, whilst the second used IFRS9 expected credit losses that factored in a SVA.

With the assumption of same RAROC hurdle, we found that in our IFRSR9-SVA model, the pricing of higher-risk lending was significantly more sensitive to the trajectory of economic outlook, introduced by the new standard, than that of low-risk lending. This was largely caused by the higher proportion of risk cost relative to interest and fee income for these products. 

Depending on the level of conservatism that banks have already priced in and the position within the economic cycle, our results suggest that aligning IFRS 9 expected losses within pricing can cause sizeable variations in customer rates (and up to 60bp higher for high-LTV mortgages). 

For our sample portfolio, layering in the additional SVA, the customer rates grew by up to 30 bps for the higher-LTV lending, depending on staging weights and the anchoring base price. 

The pricing impacts for unsecured lending are larger, reflecting the nature of utilisation uplift of the undrawn commitments as the economy deteriorates.

What next?

Accounting under IFRS 9 has started to lead to capital markets and investment managers considering their loan asset valuations.

Cascading the loss insights properly through the customer lending rates, will enable margins to accurately reflect the risk and capital consumption.

Through strategic lenses, we see that incorporating IFRS9-SVA is an initiative to start a wider price-optimisation program across functions and stakeholders.

 

Authors :

Raymond


Raymond Zhu – Senior Manager, Risk Advisory

Raymond is a Senior Manager in Deloitte’s Risk Advisory Practice in London, he specialises in financial risk measurement and management across all principle risk topics faced in global Tier 1 banks and leading asset managers.

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Jan-Hinnerk Fahrenkamp


Jan-Hinnerk Fahrenkamp – Director, Risk Advisory

Jan-Hinnerk has over 20 years of experience in Financial Services, both as a practitioner, including leadership roles at Barclays and Lloyds Bank in the UK, and as a management consultant across Europe with a focus on capital quantification and risk management.

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Alexandra

 Alexandra Savelyeva – Manager, Risk Advisory

Alexandra is a Manager in the Risk Modelling Team of Deloitte’s Financial Services Risk Advisory Practice. Alexandra specialises in credit risk model development and advice across the Financial services industry, to include asset managers and investment banks.

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 Contact Partners : 

Ian


Ian Wilson – Partner, Risk Advisory

Ian is a partner within Deloitte’s financial services risk advisory group specialising in quantitative credit risk measurement, for retail and investment banks. Ian Joined the firm as a Director in 2010 and has over 23 years’ experience in the financial services sector in UK, Europe, Africa and Asia Pacific.

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Damian


Damian Hales – Partner, UK IFRS 9 Target Operating Model Lead

Damian is a Partner within the Risk and Regulation practise with over 18 years’ experience in the financial sector. He specialises in IFRS 9 Operating Models, Credit Risk Management across the full credit lifecycle (from origination to collections and recoveries) and Capital Management.

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Leif Boegelein

Leif Boegelein – Partner, Risk Advisory

Leif Boegelein joined as a Partner in our Audit and Advisory practice. He has more than 15 years of experience working with financial institutions on the design and implementation of risk methodologies in the context of risk management, capital management and valuation.

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