Treasury Benchmark: Battle Between Excel and TMS

After overseeing a study of more than 900 treasurers and other finance executives in Asia and decision-makers in the West whose remit covers the region, Brad Maclean can rightfully claim to know many things about treasury trends from Bangladesh to Vietnam.  
Maclean, Vice President of Business Development at business software developer SunGard, says the study found that a high proportion of companies still rely on manual processes and spreadsheets for cash flow forecasting. “And so the challenge there is: How accurate is it? How regular is your cash flow forecasting?”
He spoke to CFO Innovation’s Cesar Bacani about the trends and insights uncovered by the report, which is meant to establish baselines for future studies. Excerpts:
We did it in conjunction with Bank of America, working not only across our own clients, but beyond that. We interviewed 913 corporates [in the first quarter of 2013]. A lot of those were done online, but we also did a significant portion of well over a hundred face-to-face interviews to understand and delve into the details.
Our focus was Asia. But a lot of firms still have decision-makers based in Europe or the US and there were people outside of the region who were impacted by local coverage, so we also asked them questions.
How would you describe the sample size in terms of revenues?
We were looking for firms that had divorced finance and treasury, so we were basically looking at US$100 million and above. A lot of our clients would be in the US$1 billion and above in annual revenue; Bank of America they have clients all across the range.
We looked at four groups: US$100 million to US$1 billion, US$1 billion to US$5 billion, US$5 billion to US$10 billion, and US$10 billion and above. We found that they all work very, very differently – their attitudes towards technology, their attitudes towards bank account management.
What would some of those differences be?
For example, above US$10 billion, they generally work with three primary cash management banks. Below US$1 billion, they also work with three core cash management banks. But if they are ranged between US$1 billion to US$5 billion, they work with between four to 10 banks.
Our understanding is that the firms with less than US$1 billion in revenues may be too small to have that much complexity in bank relationships [and so they work with only three cash management banks]. Above US$10 billion, those companies have already done bank rationalisation schemes [and streamlined the number of primary cash management banks they used]. Interestingly enough, these firms are also very keen to keep focusing on rationalisation.
But the firms [with revenues of] between US$1 billion to US$5 billion, their focus was growth – and that adds a layer of complexity, new markets, new regulations. They’re also not on a scale where they can necessarily negotiate with local authorities.
For example, in local markets across China, every single city where you have operations, there are regulations on which banks you have to use. When you’re the right size and scale, you can talk to the government and say, we have a preference for this bank and negotiate that a little bit more.
For me, one of the more interesting findings in the report is the fact that 66% of companies say they do not use a cash forecasting tool.
This was an interesting fact. When you look across the region, some markets are even higher. In the Philippines, for example, 85% don’t use a cash flow forecasting tool . . . That means they are doing it manually by using Excel sheets. They do not have a specialised system that’s essentially grabbing all the information and doing the reporting for them.
We’re not saying 66% of firms aren’t cash forecasting. They do, but they’re doing it manually. And so the challenge there is: How accurate is it? How regular is your cash flow forecasting?
Why aren’t they using a cash forecasting tool? You would think that forecasting is such a crucial task that companies would install the technology no matter the cost.
When we asked the question, the vast majority said it wasn’t technology and it wasn’t their banking partners. It’s actually their own internal processes and policies. For example, systems integration, their working systems, their own sales projections, the way they put them together, their own policies on a lot of things – they just don’t talk to each other;
So the first thing to do, before you purchase and implement a cash forecasting tool, is to fix your internal systems first.
That’s going to be the first step, really. The quickest win, where technology will be a long-term enabler, will really be about critically reviewing those internal processes and policies, standardising what you do. Then you start using technology on the longer term, to make sure that [forecasting is accurate and done frequently as appropriate].
But the use of a cash flow forecasting tool does not necessarily mean you will get the desired results, right? Your internal policies and everything else might work against that.
Possibly. You’re looking at specialist technologies to basically [implement] straight-through processing. The idea is, with fewer manual touch-points, there’s less potential for errors. And more importantly, if it’s a specialist system, you should be able to do forecasting more regularly. In fact it could even be automatic; you get a report whenever you need it.
Is there any indication from the survey that those companies that adopted a cash flow forecasting tool are more satisfied with treasury and cash management than those who are stuck with manual processing?
This is outside the survey, but I heard you’re looking at this area so I spent some time [cross-tabbing the data indicating satisfaction]. We actually found some things . . . The biggest impact we saw was in the revenue range of between US$100 million to US$5 billion. The impact of technology was significant in terms of satisfaction.
Above US$10 billion, you see a slight drop off, but still significantly higher than just using spreadsheets. We think the drop off is simply because of the complexities in the business [of a bigger company]. Regardless of the technologies you’re using you need a lot of things you need to put together, a lot of integration of different systems.
Is there an indication in the study that people are actually getting tired of spreadsheets or that they think it’s not really fit for the purpose? I’ve been speaking to a lot of CFOs and they seem happy enough with Excel.  
Actually, the indication that we got was spreadsheet is still being used as glue. One of the biggest challenges is lack of connectivity between systems, so firms are still using spreadsheets simply because it’s ad hoc. When you need something you can build it and then you can integrate the systems. Other systems [require] a lot of planning, a lot of process.
However, there is risk around that . . . So for example, one significant issue for some treasurers [in the US and UK]  was cutting and pasting the wrong numbers – and sending the company bankrupt. That’s why it was a focus of the regulator or of the board to say we need to move away from this.
In Asia, we haven’t necessarily seen that yet and that’s why the evolution [away from spreadsheets] is slightly slower in this region. But if you suddenly saw there were some big event or critical challenge to the firms [from wrong spreadsheet data], you’d suddenly see an adoption that would be quite steep. The learning curve would be very quick to adapting new technologies.
Is there a case in Asia where a company that was using spreadsheets was forced to change because of serious errors?
I’m thinking of 2007-2008 when a firm in Hong Kong had issues with hedging. It forced the parent company to take the biggest stake [in the Hong Kong company] and the chairman of the company was forced to lose all of his power. That was because of the manual issue to do with spreadsheets and the fact that he didn’t have visibility into what was happening. After the fact, they changed to a technology.
While the research we have done wasn’t just focused on technology – we were covering a very broad baseline to set up a benchmark – you can already see there is a significant difference between people who are using spreadsheets in terms of satisfaction and people who are using specialist systems. And it’s a significant gap right now. The people who have optimal visibility using technology are far higher than the people who have optimal technology using spreadsheets.
As we do more and more research, we’re going to understand that gap more and more and understand the key drivers. Technology enables you to access and understand everything about your treasury when you need to, versus having to say: I need to do it and then spending a period of time putting it together from disparate sources [using spreadsheets].
At the moment, it seems it just doesn’t make sense for a lot of treasurers to actually chuck the spreadsheet and go automatic because there doesn’t seem to be a lot of upside to doing that.
I wouldn’t say that. I would say there is a place [for spreadsheets]. We’re seeing that the persistence of use of spreadsheets is that glue, the ad hoc nature of it.
But one of the challenges you’ll have with spreadsheets, and this applies to anything that’s ad hoc, is that different people develop different reports. Once they leave your business, someone else has to go through a process of understanding what was built [by the departing staff] or build up a new one.
Now a treasury system is beyond just creating reports or cash visibility. It’s also executing payments, reconciling bank accounts and getting statements, it’s also giving you an understanding of work flow, who has approvals and allowing you to execute that work flow. So if you look through straight-through processing, the whole idea is the less manual interference, then the less error and the less chance for fraud.
From a spreadsheet’s perspective, it’s more about creating a report or creating something from a management decision making processes. That’s why it’s generally very, very ad hoc. You can manage treasury from a spreadsheet. But your treasury should be a little bit more pro-active versus reactive.
That’s the biggest difference between an automated system [and spreadsheets]. With an automated system, it’s all about: I need to look at it now, I need to understand our cash position so we can proactively make a decision. If I’m reporting to a spreadsheet, it’s only going to be historical and it takes time.
But people can do what-if scenarios, forecasting and so on using a spreadsheet.
That’s all built into technology as well. The way technologies are built, every firm approaches it from a different perspective. But it’s really built around: ‘This is the best practice internationally.’ And then it’s customised into your industry, in your own work flows.
Every firm works slightly different depending on the industry they’re in. For example, receivables and DSO and things like that, everyone has a different industries and different industry benchmarks, and they have their own internal ways of dealing with things as well. So that would be built in into the structure of the system.
With both spreadsheet and automation, you need to make sure the raw data is accurate. Because it’s manual, isn’t it easier to detect and fix inaccurate data in a spreadsheet? Whereas if it’s automated . . . .
If we look at just the whole issue of data, regardless of what reporting you’re doing on top of that, the integrity of the data would be one of the biggest issues.
Just because you’re doing it in spreadsheets and you have the ability to look into the details, that doesn’t mean the quality of the data is any better. It comes down to your own processes in putting it together. And remember, every subsidiary is doing it, and they may be potentially doing it slightly differently.
Just look at sales targets. One person has a certain attitude: I’m very optimistic, I’m going to raise my sales target a little bit more. The other person is like: If I put it too high, I prefer to actually put it a little bit lower, because I’ll overperform. That’s a data integrity issue. You need to understand the seasonality, the attitude of people putting that data together.
Spreadsheets and the treasury management system are fundamentally putting the same data together. But with the treasury management system, you’re giving access to the various people who needs to and how they need to. With spreadsheets, it’s a person opening up a spreadsheet, putting the data that they need, emailing it across to somebody else, who then may put it into another thing.
These manual touch points become an issue to data integrity, when you look at who’s manipulating data along the way.
Is there a case to be made that, if turnover is US100 million or lower, you can actually get along well with the manual spreadsheet. As you go higher up that scale and complexity then you would probably need a treasury management system?
Firms under certain size are less complex in terms of their businesses. If it’s less complex, do they really need to have specialist systems? I’m not necessarily sure [what the answer is], but if your business is managed like that, and the risks are lower, then potentially that is the case.
It comes down to the individual business and the complexity of your business. Some of the complexities could be the number of countries you’re doing business with and the regulations within those countries. Do you have, for example, trapped cash? Are you dealing with FX? Are you dealing with thousands of payments?
When you’re having more and more variables, you’re more likely to see that divorce between finance and treasury. But if you’re dealing only with a couple of payments, you’re dealing only with one country, you’re dealing only with one bank, you might find the treasury role sitting within finance.
You asked the respondents about their priorities for the next 12 months. The answers are what you would expect, right? Visibility, yield enhancement, rationalisation of banking relationships . . .

The thing that isn’t there that I thought would be is talent management. I thought that would be higher [in the list of priorities]. Yield enhancement is higher than I thought it would be. Cash concentration is higher in emerging markets, which makes sense; it is also high for developed markets, sitting just behind yield. Bank rationalisation is more prominent within emerging markets again and that’s because of the regulatory complications.  


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