Oct 2021 | PowerCurve Users Club | Credit Risk, Simrisk
By Posted by Richard Topham

Richard Topham, Director of Strategic Sales in EMEA at Experian, explains how companies can identify the businesses being kept afloat by government stimulus

Governments across EMEA stepped in to provide support for businesses once it became clear the Covid-19 pandemic would change trading conditions beyond recognition.

Every country is different, but many of these initiatives are now being unwound as societies make the journey back to normal.

There are unknowns for us all as we tread a careful path, and lenders are certainly included in that number. One of the key challenges that they face is how to identify the ‘zombie’ organisations which were being kept alive only by government intervention.

In more normal times, lenders would use the annual accounts, which show the cashflow, profit and loss a company has made, and tried and tested ratios to get a snapshot of how the business has performed over the previous financial period.

The drawback has always been that these accounts reflect a past financial year and they are often not published until well after the financial year end.  This could therefore be reflective of pre-pandemic performance or certainly not reflective of a post pandemic business environment.

During conversations I’ve had with the C-suite over the summer, I know some lenders are now looking to alternative data to provide a more up-to-date perspective.

Open Banking has created an environment where transactional data is more readily available and may organisations are using this for their consumer base, but there is equal, if not more, value in using this to assess the performance of SMEs in order to understand their viability today, based on current data.

Macro economic segmentation is another new tool that can be used – once you understand what sector the business is operating in, it’s possible to use economic insight to understand the macro performance of the SME population in that sector when making lending decisions on businesses in that sector.

This has never been more valuable than today when we know that some sectors are perfoming well below their historical level, and some well above.

More than one lender is casting the net even wider in search of new ways to understand their customers.

Web data insights allow lenders to consider factors including unstructured data such as social media reviews, status comments and news articles to see if a business is trading, popular or maligned.

It mirrors the logic we apply as humans when scanning reviews. If a café closes, its reviews grind to a halt, or alternatively, a downturn in reviews may show the new trading conditions now challenge them.

All of these factors can be predictive of risk and when combined with traditional data, such as bureau data it can provide an uplift in the performance of the scorecard.

Processing such high volumes of unstructured data is only possible by using AI and Machine Learning but the results can be surprisingly predictive.

The lenders who will best identify the zombies among their client base will use alternative data to understand the macroeconomic situation and combine new data sources with traditional insights to get the best view of their customers and avoid the zombie apocalypse.