Since the apex of the financial crisis back in 2008, the treasury function has been going through a period of accelerated evolution. “Treasury isn’t getting simpler,” observes Bob Stark, VP of strategy with Kyriba. When the treasury software specialist asked treasurers which trends they expected to have most impact on their treasury operations during 2013, 72% cited developments in the eurozone and counterparty risk. “Treasury professionals see a clear linkage between macroeconomic issues and risks in their daily operations,” he says, and one way for treasurers to tackle this is by better exploiting the information and the information systems at their disposal.
“The treasury function has evolved over the past few years,” says Stark, “and more complex information and analysis is expected of treasury teams.” But meeting these expectations can be a challenge, as appropriate software can be difficult to access – not least because of the growing range of counterparties that treasurers may need to consider. “Since the global financial crisis, treasurers have become much more focused on the risks associated with banking counterparties,” says David Stebbings, director, head of treasury advisory, at PwC, and the technology to support this is less well developed (see Analysing bank risk, right) than it is for other aspects of treasury management.
The treasury function has evolved over the past few years and more complex information and analysis is expected of treasury teams
There has also been a heightened focus on commercial counterparty risk, and a broadening of the relationships that this encompasses. “Until about four years ago, you rarely heard organisations talk about the counterparty risk associated with key vendors,” asserts CJ Wimley, COO for SunGard’s AvantGard Receivables, as the focus tended to be on understanding and managing the credit risks associated with customers. But organisations and their treasurers are becoming more risk averse. “As more accounts receivable people report into treasury, they are being asked to do the same sort of credit management on key vendors as they usually do on customers,” he says, so they can assess the associated risks and improve their analysis of them.
“With counterparty risk, treasurers want to know where the last domino is and what will happen if that falls,” says Wimley. How well this can be understood and managed depends on many factors. These include: corporate organisational structure; how centralised the treasury function is; the software it is using for cash, treasury and risk management; how sophisticated this software is; how well it supports the two-way exchange of data with other sources of significant information inside the enterprise (such as accounts receivable systems and financial accounting systems/enterprise resource planning systems); its capacity to pull in data from outside the enterprise; and how reliant it is on third-party evaluations of counterparty risk.
The way that commercial counterparty risk is assessed is changing. Take Edward Don & Company. The distributor of food-service equipment and supplies used to determine new credit lines and terms using generic bureau scores and an internal judgemental scorecard; while various scorecards were used for existing accounts, based on bureau data, financial reports and some internal data. But this wasn’t as accurate or available as quickly as the company required. Low-risk customers sometimes ended up in high-risk categories, and customers that should have been were not always being captured as high risk, which created all sorts of problems. So it decided to integrate statistical modelling into its credit and collections processes.
After assessing the possibilities, Edward Don & Company opted for AvantGard Predictive Metrics, a suite of statistical-based scoring models that can be used to determine a probability-based risk grade. It does this by leveraging a company’s internal data on accounts receivable, billing and collections, and then combining this with terabytes of behavioural trending information. This can then be used to predict the likelihood that an existing ‘good’ paying customer will become a ‘bad’ paying customer during the following six months, and calculate how much cash is at risk in the portfolio. “This helps us to focus on risk and be more proactive,” says John Fahey, director of credit at Edward Don & Company, “which has helped us to improve collection prioritisation and lower day sales outstanding by 5.3 days.”
When you are trying to mitigate the risks associated with cash exposure to counterparties and maintain liquidity, cash-forecasting tools remain invaluable, particularly if you are trying to manage exchange-rate volatility and establish the proper hedging policies to limit currency and counterparty losses. But, as Enrico Camerinelli, an analyst at research firm Aite Group, says: “The value of a treasury transaction is not in its execution, but in the information that accompanies it, and the intelligence this generates can be the foundation of more intelligent business decision making.” Not all treasury systems are equal, however, when it comes to delivering ‘treasury intelligence’ that unifies strong cash management facilities with forecasting and risk management.
When EADS, a global provider of aerospace and defence-related services, decided to upgrade its legacy treasury software and risk management at its central treasury department, it opted for a banking system (from Calypso). “As an outcome of the financial crisis, industrial corporates are taking a more cautious view on credit and liquidity risk management,” says Andreas Drabert, vice president, treasury controlling at EADS. “We were seeking a robust platform that would enable us to consolidate a range of financial instruments as well as the full scope of our processes onto one integrated platform.”
Volume-wise, EADS is like a small bank, and it has a strong order book that generates big currency exposures and risks. Calypso seemed like a good fit as it unifies cash management, collateral, accounting reporting, limits and liquidity risk, and delivers support for a wide range of FX and derivatives products. The new system also offers quantitative risk modelling and sophisticated risk reporting and metrics. This sort of detail may once have been the preserve of banks, but EADS wanted the added intelligence and capacity.
“The value of a treasury transaction is not in its execution, but in the information that accompanies it, and the intelligence this generates can be the foundation of more intelligent business decision making”
While many treasury functions are using treasury software to manage commercial counterparty risk, they may find that their relatively sophisticated methodology for evaluating counterparty risk does not fit easily into such solutions. “Software developers have done their best, but many companies use a variety of quantitative and qualitative measures that packages find difficult to model,” says Stebbings, so many organisations still rely on spreadsheet modelling combined within position information from treasury systems and risk measures, such as credit default swaps, ratings and tier 1 capital levels. “Positions may be calculated in treasury management systems, but the credit exposure and the limits still end up in spreadsheets,” he adds.
So the key to effective management of bank counterparty risk may have less to do with software than with your overall risk management process. “Key is understanding your global position on all instruments, not just your deposits, defining your criteria and measures for quantifying risk, and then calculating your exposure limits (value, duration, etc) based on your risk appetite,” says Stebbings. Limit measurement should ideally be on a real-time basis, though practicality often dictates this only for material positions, with daily or weekly monitoring more prevalent. “Once you have those objective measures, you can then wrap any subjectivity around them,” he suggests. After all, you may feel that your cash is safest in a UK bank, or suspect that the Italian government will help Italian organisations first if an Italian bank goes bust, but you wouldn’t want to base your analysis of counterparty risk on a hunch, would you?
Lesley Meall is a freelance journalist specialising in finance and technology