From a macro-economic perspective 2020 was in many ways “the year of the great deferral.” Despite record level of unemployment in some parts of Europe surpassing the crisis of 2008, governments across the world successfully managed to push out the crystallisation of credit risk.
The big questions now are what will be the economic effects of the easing of lockdown and the accompanying withdrawal of the fiscal support adopted by governments?
Our macro-economic forecasting team provide forecasts for multiple countries across Europe, Middle East and Africa region and specifically assess the impacts of macro-economic change on credit and how will changes in GDP and other measures impact credit origination and delinquency risk.
What sets out this crisis from those that have happened before is the lack of any form of precedent that can provide meaningful comparison with todays’ hyper connected world. This is resulting in economic uncertainty and as a result attempting to predict when credit risk will begin to be crystalised requires more than an assessment of just GDP.
In this context demographics and sector level analysis provides a different lens for more local level forecasting. Clearly bureau data is particularly useful, but to achieve focussed local area forecasting, overlaying macro- analysis with segmented market and demographic data such as that provided by Experian’s Mosaic, enables market segmentation right down to household level to create a very granular view of the local area impacts.
By assessing GDP impacts on unemployment and then combining this with analysis of a combination of this demographic data plus credit bureau data enables a detailed exploration of local level impact of unemployment on different age groups and sectors. This in turn enables the construction of different macro-economic scenarios analysis to assess risk concentrations within and across your portfolio.
National and sector comparisons
In terms of total economic impact, economies in Europe had real winners and losers in 2020.
The sectors hit hardest in terms of unemployment have been those requiring face to face consumer interaction – hospitality / restaurants / travel / accommodation – all sectors which account for huge numbers of workers.
Unemployment has always been the key variable within the credit lifecycle but economies with stressed unemployment rates have not seen the level of delinquencies as would normally be expected. A year ago, macro-economic forecasts would have predicted unemployment levels over far higher than what eventually transpired. This reflects the success of national governments’ deferral of risk through measures such as emergency payment holidays and other repayment concessions. However, the debt problem has only been pushed forward – in effect kicked further down the road.
Credit institution responses
When this support is taken away, not only we will see very different sector level impacts, but we will see very different responses between credit institutions. Each will have their own risk appetites, scores policies, cut offs. Sector level exposures and potential risk concentrations therefore vary greatly not just between institutions operating in different countries but also vary greatly within the same country. All of this means that there will be significant variations in the timing and level of exposure to the progressive crystallisation of unemployment.
The more fortunate members of the society (one hesitates to use the term “winners” given that personal tragedy has affected so many) to emerge from the pandemic are those who have remained in employment and who rapidly transitioned to home working early on. For these workers, the combination of income stability plus a restriction on outgoings on entertainment, recreation and travel has meant that they collectively have significant levels of pent up disposable income that can drive growth as economies gradually re-open.
The prospects for growth may have the effect of cushioning the overall impact on delinquencies through unemployment. If delinquencies increase within a growing economy will mean that overall non-performing loans (NPL) rates will look relatively stable as there will be a stark increase in new lending. This is one of the reasons why in countries such as UK, high street banks have announced a repayment of provisions whilst their equivalents in other countries have been far more cautious.
So how do you reflect divergent recovery this in your risk forecasting?
The divergent nature of the current recovery, with its complex patchwork of those who have gained or lost from a financial perspective, has been described as “K shaped”. Many slightly older, more affluent consumers now appear a better risk than ever before.
In contrast, younger people that are less skilled and in consumer facing sectors, are not in a good position. However, given the deferred nature of fiscal policy, this view of risk won’t be evident from traditional credit models which will indicate that their risk levels are still acceptable. Some undoubtedly will be but there will be pockets that hit badly.
We believe that in order to reflect the uneven spread of risk and opportunity within risk forecasting, credit institutions should look closely at their approach to segmentation.
Many organisations will already have put brakes on their automated decisioning tools. These are probably best reopened on a progressive basis sector by sector
To build a forward macro view to address the emerging vulnerability, models should be built based on shorter periods – say 15 to 50 days – and these should be broken down to sector and geography to generate triggers and flags. The short-term time horizons are needed because the picture could change quickly.
Concerns over Inflation
The pandemic has disrupted global supply chains with both factory production and global shipping channels particularly affected. This has resulted in both restricted availability of consumer goods but also to shortages amongst local manufacturers of individual components such as microchips resulting in production shortages which in turn has hit domestic manufacturing levels.
Whilst the pandemic has seen no inflation, the combination of pent up consumer demand plus shortages of supply is beginning to translate into inflationary pressure.
This is a big concern. It will reduce real wealth plus undermine wealth creation and hit the associated potential for job creation. The responses of the Central Banks to increased inflationary pressure need to be closely assessed. For now, they are saying that inflation will be transitory and will wash out over the next year and so there is no real action that they will take. Reducing levels of quantitative easing is another tool that might be used in controlling inflation and would potentially be used before increasing interest rates.
Increased inflation will result in both winners and losers and create a differential amongst consumers, with the less affluent more exposed. However, combatting inflation through increasing interest rates, if handled incorrectly, could derail business recovery. Close monitoring of businesses’ revenue and profit levels are good indicators, but benchmarking is crucial given that we are in unknown territory and there is strong potential for localised pressures.
Conclusion
The combination of lack of historical precedent, K-shaped divergent recovery, supply chain disruption and the varying speed and adoption of vaccinations across different countries contribute to massive uncertainty. Managing the effects on credit risk decisioning, only strengthens the need to ensure that credit providers are as agile and responsive as possible. We believe that in this context it will be important that organisations have access to the following foundational capabilities.
- Firstly, the ability to combine macro-economic analysis with granular data not only at an industrial segment level but at a geographical and even a household level. This will enable credit providers to identify more accurately any potential changes in exposure and vulnerability.
- A segmented approach within their Risk Appetite Frameworks, risk tolerance thresholds and supporting KPIs that reflects business sectors and geo-location. This will help ensure maximum agility and responsiveness to be able to reflect different speeds and overall timescales on the road to recovery.
- Thirdly, access to a centralised macro-economic forecasting capability that can ensure a consistent approach is adopted within a range of forward-looking scenarios that can simultaneously drive originations strategy, default and non-performing loans management, and IFRS9 monitoring
The combination of these three factors should ensure credit providers are best able to leverage localised areas of opportunity whilst simultaneously ensuring maximum responsiveness to the prospect of default.