The economy matters
We have seen the deepest recession on record. The UK economy shrunk by more than a fifth over the first half of 2020; and whilst we have seen record levels of GDP growth since, the latest data confirms that the economy is still some 10% smaller than it ended 2019. To help understand what these headline trends mean for your borrowers, we have developed a sectoral model which provides us with a granular view of growth trends by industry. This allows us to differentiate between regions and local areas in terms of growth. 
For example, the latest data shows that industries such as accommodation and food services continue to be hit hard by the pandemic. In contrast, the manufacturing sector has recovered most of the ground it lost since 2019. We can then track back these industries to local areas, understanding your exposure to parts of the country where they have the most prevalence.
This local data feeds directly into our unemployment forecasts. We have seen unemployment creep up to nearly 5%. However, we still believe that this is underestimating the true nature of unemployment, and in fact, there are countless more individuals who have suffered job losses. This masking of the real data makes it imperative that you break away from the average unemployment rate and understand the unemployment risk of your portfolio.
Unemployment risk
Beneath the UK headline unemployment rate there are varying trends amongst the regions with northern regions seeing higher averages than in southern regions. Sectoral mixes and population dynamics likely influence this. Looking across the sectors, job losses have primarily been in the service sector. The 18-24 year old age group is overrepresented in this part of the economy, meaning the unemployment rate rose by the most for this group.
Our credit bureau gives us access to account level data, so we can help you understand where your borrowers are in the country and to which age group they belong. The Collections Foresight Tool uses unemployment forecasts at this exact level of granularity to give you a view of unemployment risk at account level.
Building in Bureau Insights
To understand the impact of the economy on the credit markets, we have supplemented the unemployment risk data with our latest Bureau Insights centred around Emergency Payment Holidays (EPH) and CATO Income Shock.
Borrowers have been able to take out an EPH since March of this year, with millions taking them out across a range of credit products. The regulator has asked lenders not to report these to credit bureaus, meaning many lenders cannot accurately assess if their borrowers have taken out EPHs with another lender. Our modelled EPH indicator shows the number of EPHs we estimate a borrower has taken out and against which credit product.
On its own, understanding whether your borrower has taken an EPH is useful but the Bank of England estimate that 30% of all the emergency payment holidays have been taken out as a precautionary measure*. Our own estimates suggest that nearly 50% of those people who’ve taken those emergency payment holidays have not suffered an income shock; many have seen income increased. Therefore, to accurately assess potential arrears emergence, lenders must identify those borrowers who have taken out EPHs as a result of a negative income shock rather than simply a precautionary measure.
Methodology
The methodology we have developed to forecast potential arrears emergence is simple, transparent and resilient to the changes in the economy. Our foresight tool brings together unemployment forecasts, the latest Bureau Insights, as well as traditional credit risk indicators in the form of Delphi for Customer Management (DCM) scores. The tool first segments each of these data fields into three categories and assign each a Red/Amber/Green flag. For example, suppose a borrower has a low risk of unemployment. In that case, they are assigned a Green unemployment flag, a medium risk is Amber, and finally, a high risk of unemployment is Red.
With the segments now defined we can model a lender’s exposure to the riskiest parts of the credit population, i.e. Red – Unemployment Risk, Red – Income Shock, Red – EPH and Red – DCM score (4 reds). The matrix analysis is carried out in Tableau, and a full account level file is returned. We have then assumed a probability of default for each segment; for example, the probability of default for the riskiest population is assumed to be 50%. With the probability of default assumptions in place, Tableau can now calculate both the volume and value of arrears predicted for the given portfolio.
Conclusion
The impacts of the pandemic and associated economic shock are yet to feed through to credit markets. As the economy continues to evolve and unemployment starts to feed through, there is likely to be an arrears challenge for the industry. Experian’s Collections Foresight Tool can help lenders prepare by providing a robust estimate at portfolio level of both the volume and value of future arrears.