Counterparty risk is a top concern for treasury and finance professionals, according to a survey by Treasury & Risk. One major problem highlighted by the survey report is concentrated exposure to a small number of businesses.

Slow and non-payment are causing anxiety

The 2016 Credit Risk Management Survey found that 38 per cent of the treasury and finance professionals polled said that the “credit risk of a few large companies, to which we have a lot of concentrated exposure” was one of their top concerns with regard to credit risk. The risk of non-payment from customers also ranked as one of the top concerns, chosen by 43 per cent of the poll's respondents, while more than a quarter (26 per cent) said the related problem of slow customer payment was problematic.

The results, based on replies from 110 treasury, finance and risk professionals, also show that “supply chain disruption in the event of a supplier/distributor failure” is another major source of anxiety – particularly for companies in the manufacturing and energy sectors. This underlines the importance of managing both distributor and supplier credit risk.

Other findings from the survey include:

• 63 per cent said their company uses an external model from a rating agency as part of its credit-data analysis, while 51 per cent use an internal model driven by expert judgment, and 45 per cent use an internal quantitative model;
• 61 per cent said their company uses credit risk scores developed by a third party to estimate the probability of default;
• a centralised corporate treasury department has primary responsibility for credit management in 41 per cent of all companies, while 32 per cent have a corporate credit department;
• 16 per cent refresh their counterparty credit analysis once a month, and 8 per cent refresh even more frequently;
• 69 per cent said that “lack of access to private company information” was a significant impairment to the organisation's ability to effectively manage credit risk, while 44 per cent cited a “lack of standardised financial data” as a significant impairment.