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Insolvency Series: Of Banks, SMEs and Boiled Frogs

15 December, 2017Keith Pond

If any one of us could accurately predict the future then we would either be very rich, very powerful or seen as very wise. 

Banks deal in the future – the key risks undertaken in lending relate to future revenue streams for repayment of loans; future movements of relevant interest and exchange rates and future asset values in respect of collateral security for loans.

In one aspect of their lending activity, banks are prepared to use predictive tools – typically only as accurate as the information fed into them – and with SMEs, often described as “informationally opaque”, that task becomes very difficult.

Studies abound that purport to predict corporate failure.  Some models even form part of the bank decision making process.  For example, the “Z Score” pioneered by US academic Altman (1968) is reasonably accurate 1 or 2 years from failure but cannot be readily applied to markets outside the USA.  Other “Z score” models relating to the UK exist (Agarwal and Taffler, 2007) and they do appear to foresee problems.  Such models rely, however, on accounts based information from the past, they are not “real-time” tools.

The point of this blog is, however, that few corporate or SME failures happen overnight.  For the majority there are signs and portents of disaster that are there for the observant lending officer to see.  Hence the “Boiled Frog” syndrome: Apparently, if you place a frog into a saucepan of boiling water it will jump out.  If you place the frog in cold water and slowly bring the water to the boil, the frog is likely to die.

Frog Keith Pond
PICTURE TAKEN BY KEITH POND IN HIS BACK GARDEN

So, what is the equivalent of slowly heated water for the SME “frog”?  Most likely, it is the relentless haemorrhaging of cash – still draining long after profits have evaporated.  And there are plenty of indicators of exsanguination for the lending officer to detect, that credit bureaux simply do not see:

  • Pressure on overdraft limits
  • Lower levels of credit turnover
  • Later payment of creditors
  • Diversion of management time to remedy problems, rather than to grow sales
  • Key managers leaving

The key is to recognise and understand issues as they occur.  That can rarely be done using computers or algorithms. 

 

NO FROGS WERE HARMED IN THE WRITING OF THIS BLOG

REFERENCES

Agarwal, V. and Taffler, R. J. (2007) Twenty-five years of the Taffler Z score model: does it really have predictive ability? Accounting and Business Research, 37(4), p285–300, EBSCOhost: Business Source Corporate Plus [online]. Available through KnowledgeBank

Altman EI, (1968), Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance, Vol 23 (4), pp 589 – 609.