Macro variables really gave strong messages over the last 18 months, and we’ll see that the timing of them were very different, so consumer markets and financial markets were affected at different times. I want you to think about how they affected these particular businesses.
Microsoft is mainly in the business of consumer software, Toyota Financial, in the dealer financing of cars, and Cisco Systems, in corporate communications to business. The last 18 months, if you had relied on the help of experts, you would have been totally lost.
This slide shows forecasted economic growth against actual economic growth, and the experts have really been no help in the last 18 months. If we look at residential housing, you can see that warnings came in 2006. This slide is showing the Case-Shiller index in the red and the supply of homes for sale in the blue. So the early warning signs were there, house prices were topping out in 2006 and the impact started to be felt in 2007 when the inventory of homes remained, topped out and stayed very high.
To the consumer market, 2006 or early 2007 is when warning signs started to appear. For housing debt and servicing ability, you can see the red line of household debt steadily increasing and the blue line, household debt growth, so debt accumulated to a point where people had to really stop taking on extra lending.
Consumer debt really didn’t start to decline until late 2008. So we have warning signs in the housing market, with consumer debt not being impacted, or starting to increase, until late 2008, 2009.
Home-linked delinquencies, not until mid-2007. So they are lagging after home prices topped out. It wasn’t until six months later that delinquencies started to increase.
Auto sales, which is of interest to Toyota, had a steady decline, and that was very early on. Back in 2006, the early warning signs were there. You can see that things steadily got worse and then had a benefit of Cash for Clunkers more recently.
For bank lending, you can see the blue line representing banks tightening credit and the red line demand for loans. Again, there are some early warning signs of banks tightening credit back in 2007, but it’s not until 2008 that it really starts to kick in.
On the corporate side, earnings and federal tax, the S&P earnings fell away heavily in 2008 and tax receipts started to fall. Commercial lending didn’t start to decline until 2008. On the business-to-businessside, some of the warning indicators came well after the consumer indicators.
Financial institutions are looking at Wall Street. You can see in 2007 credit spreads started to increase with the AA rated banks, but it wasn’t until March 2008 when things really got scary and you see the big spike in CDS spreads.
Keep that recap in mind as each of the submissions are presented and think about how the different indicators may have affected their businesses and how they responded to the credit crisis. In looking at the submissions, there’s really three common themes that come through:
All of the companies needed to get data, needed to be accurate and in some cases, they needed to reclassify their risks. They had multiple systems, so there was a need to gather them together. They needed to be timely, because management was asking for information, and management was changing their focus regularly about the type of information needed. And lastly, they were involved with technology. Some of the companies have multiple systems so they had to gather data from across systems and pull it together. They needed to be very flexible and talk to each other.
With that introduction, I would like to award the Bronze Medal to Cisco Systems and I’d like to invite Thomas Braida to receive the award. Thomas is the director of global credit within the Global Shared Services Organization of Cisco. He has strategic and technical responsibility for all open account trade, credit activities and an overseas trade credit portfolio of greater than $4.5 billion. He is responsible for implementing global credit policy and optimizing processes for speed, productivity and compliance. He has over 23 years experience in various credit and finance-related positions, with expert knowledge of all aspects of credit and financing solutions. Prior to Cisco, Thomas spent nine years at Comdisco. Thomas holds a bachelor’s degree from Northern Illinois University and an MBA from DePaul University. Congratulations to Cisco.
Credit Risk Management 2010 Transcript
Thomas Braida, director of global credit, Global Shared Services, Cisco Systems Inc.: Thank you. It’s an honor to accept this award on behalf of Cisco. Thanks to Treasury & Risk magazine. I’d also like to thank two people with me today from Cisco: Alex Dubankin, who was a project manager who really kept this project on track and on budget; and Warren Harber, who really made sure that this project stayed prioritized and stayed funding, even when the financial crisis almost knocked the project off the tracks.
A little about Cisco: Cisco is a relatively young company. It was founded in 1984, so it’s only 25 years old, and only 20 years old as a public company. We have $40 billion in annual revenues and about $4.5 billion in accounts receivable. We’ve grown very quickly through a combination of internal growth as well as acquisitions. In fact, just over the last month we announced two $3 billion acquisitions: one for Tandberg out of Europe and one for Starent Networks.
Despite the financial crisis, our long-term growth is still projected, or we still anticipate growing 12% to 17% every year. So although we’re a $40 billion company, we’re really planning on being an $80 billion company over the next five to seven years. And we have a relatively small global credit team. We have 16 credit professionals globally. So therein lies the problem.
Prior to the financial crisis, we were having difficulties scaling with the business. We had many inefficient and manual processes. All our credit reviews were done in spreadsheets outside of the RPspell out, please system. We had a manual control environment, so all of our Sarbanes-Oxley (SOX) controls were manual. They were “detect” controls rather than “prevent,” and it wasn’t integrated with any of our systems. We had a suboptimal customer set-up within the enterprise resource planning (ERP) system, which caused many of the manual processes; anything in terms of reporting was done entirely manual. And we had multiple ERP systems.
With the business accelerating, we had a very hard time keeping up with its demands. Something needed to change. So we undertook a comprehensive DMAIC [“Define, Measure, Analyze, Improve, Control” Six Sigma methodology] analysis and really tried to identify some of the core problems that were causing our inability to scale. And one of the things that we found out was because the way our customer set-up was in ERP, we just had many distributed credit limits.
We had no ability to roll up credit limits at the counterparty level, as well as exposures. And so about 29% of all our orders were hitting credit hold. A lot of these holds were unnecessary, if we could only fix our problem with the customer set-up.
We further found that about 60% of a credit analyst’s time was spent on what we considered non-value-added activities -- things like downloading financial statements, keying in financial data, rolling up exposures and releasing credit holds.
We also found that due to a lack of a formal integrated credit system, about 50% of our customers were really underreviewed from a credit risk standpoint and about 16% of our reviews were not completed on a timely basis.
So we embarked on a multiyear, multiproject credit capabilities road map. The first phase of this road map was a foundational project that we called Credit Line Management. The goal here was to reduce the amount of unnecessary credit holds by 25% as well as reduce the credit team’s non-valued-added activities.
Credit Risk Management 2010 Transcript
So this was really more of a foundational project, and we really needed to go into our ERP system and do some level of customization. We had to re-engineer the entire credit check process; 30% of your orders going on hold was just not acceptable.
Once we addressed the foundational issues within ERP, the next phase of the project was more transformational. What we really needed to do was get credit reviews out of the system and on spreadsheets and augment them with what we considered to be a best-in-class integrated credit platform.
We felt that a lot of our customers were either low risk or low-dollar amount, and those we could automate, with the goal of automating about 25% of our reviews. That would allow us the ability to focus, really, where the risk is: with our high-risk customers, emerging markets and very high credit concentration customers.
We also wanted to get away from this manual SOX control environment and find a way to embed our controls within the system and transform them from manual and detect controls into more automated embedded and prevent controls.
When we talk about the foundational Phase I of our project, one of the issues we had involved our many large global customers. We had credit limits assigned to multiple entities throughout the world. So, as an example, for one our large global customers, AT&T, we had probably 40 to 50 separate credit limits all over the world under various ERP systems. Although we felt very good about the customer, we would have situations where, for example, an order would come in for Italy. AT&T would not have sufficient credit limit in Italy. The order would go on hold. We would have to go in and manually rebalance credit limits to clear the order.
The solution was to go into the ERP system and totally re-engineer the process around credit checks. One of the things that we did was to create an entirely new entity within our ERP, called Economic Customer. This way, we can assign credit limits and monitor exposure at the counterparty level. So instead of doing credit checks at each ordering entity, we would just do one credit check at the ultimate counterparty level.
This was a significant customization within our ERP, but this also allowed us to roll up all exposures. At any given point in time, we can see what our global credit limits are to any counterparty as well as any amount of exposure.
One of the things this did was to reduce the number of credit limits assigned from over 10,000 individual entities to just under 2,000. This sounds real basic and simple, but in actuality, it was a very complex project. Whenever you’re customizing ERP, particularly around credit check, you run the risk of disrupting other parts of the business. So it required a lot of project management, a lot of IT support, and a lot of testing. But the project was highly successful.
Phase II of the project, which we call Integrated Credit Capabilities, really paved the way for us to do something more transformational.
Prior to this initiative, if you can see on the slide, we only had a very small amount of automated processes within the whole credit review process. We got notification when someone was on credit hold, and we had an automated credit check process, which we just improved through Phase I. But everything else was done outside the ERP. We downloaded financial statements, we keyed in a lot of financial data, we would run rating agency reports. We would have to manually key it in to a spreadsheet, and we’d have to route that for approval. A lot of our time was spent on things that really weren’t adding value. And as I said earlier, we felt that more than 60% of our analysts’ time was spend on these non-value-added activities, instead of really managing risk, which is what we’re paid for.
Credit Risk Management 2010 Transcript
This process involved a detailed and lengthy RFP process where we selected what we thought were three best-in-class vendors in this area. We also did a very high level of interviewing the vendors and demonstrations. Because what you find out when you do an RFP, when you look at what your needs are, everyone comes and says, “Oh, that’s out-of-box functionality. We can do that.” But once you get them in a conference room and they’re going through, and you say, “OK, show us how you do this,” you find out that there’s certainly a lot of gaps in the process.
We ultimately picked eCredit by Cortera as our application. We thought it had the most robust credit analytics and automation capabilities.
We moved almost all of the credit management processes from manual to more automated and embedded processes, and now all our activities take place in a system. They’re not outside the system on spreadsheets. When we kick off a credit review, if it’s a public company, we are able to automatically download all their financials right into our credit tool. We’re able to automatically load in and populate Moody’s ratings, default probabilities, D&B data.
So everything basically takes place in eCredit, which we consider our single source of truth. It’s a flexible rules-based system, where we’re able to program automatic approvals based on certain types of rules and really take a lot of the low-risk and low-dollar-amount transactions out of the work queue and automate those as much as possible. Now we can really focus on where the risk is and managing the risk in the portfolio. The end state was really a fully integrated credit system, and we went live in May 2009.
In terms of the results, both projects were highly successful. We had a 43% reduction in manual credit holds. These were credit holds that we felt were unnecessary and were caused by the inefficient set-up we had previously. The quarter we went live, which was our fourth quarter, we saw a 183% increase in the amount of credit reviews that were completed, and almost a quarter of these reviews were approved using pre-defined automation rules. Cost per credit review declined by 70% quarter over quarter, and our SOX controls were all now automated and pro-active. Our credit system is all work queue driven and a lot of our SOX controls reside in the work queues. The non-value-added credit activities were reduced by about half.
In terms of savings, we calculated conservatively about $1 million in annual savings. We think that number is probably closer to $2 million. But when your whole department budget is only $2.5 million to $3 million, that’s pretty significant. More importantly, what this gave us was really more of an ability to scale, so as the economy picks up our business starts growing again. Thank you.
Moderator Peter Seward: Thanks, Tom. I’d like now to present the Silver Award to Microsoft Corp. Representing Microsoft is Bertan Akin, senior quantitative manager with Microsoft. Bertan is responsible for Microsoft’s investment portfolio’s daily risk and analysis reporting. Prior to joining Microsoft, he was a credit and risk management information systems analyst at Duke Energy Corp., where he was responsible for implementing an energy trading system and developing functional requirements for a new credit risk management reporting system. Bertan has an MBA and a B.S. in computer science from the University of North Carolina. Congratulations, Microsoft and Bertan.
Bertan Akin, senior quantitative manager, Microsoft Corp.: Good morning. Before I begin, I want to thank Treasury & Risk for giving us the opportunity to come out and present our 360-degree exposure reporting and process solution. I also want to do a couple of thank-yous:, My boss, who’s over there, who provided me the opportunity to work on this project and really inspires us to do more than we think we can do. And I also want to recognize Anita Prasad, who’s our general manager, and she actually was a winner yesterday in the Liquidity Risk Management category.
Credit Risk Management 2010 Transcript
We also had a win in the Financial Risk Management category. I think it’s almost a clean sweep. I also want to recognize George Zinn (, head of treasury, who also inspires us to do more than we think we can do and also has introduced the concept of the life cycle of the dollar. This places us in terms of understanding how we fit into the treasury group as a whole, and how this reporting process fits.
I also want to thank a previous representative and speaker, Jennifer MacKethan of RTI International for using Excel and sexy in the same sentence. That’s a great ad for us, and I also want to say if you use SharePoint, also, then maybe the solution you guys provide could be even further automated.
I’m a financial risk manager at Microsoft and I’ve been there for six years. When we talk about risk in general, they say that when risk managers do their job, nothing happens. The last couple of years, it hasn’t been like that. As you all know, during this financial crisis everybody had to get together and try to figure out exposures and where the risks were in the company.
At Microsoft, we have a concept of the life cycles of the dollar. We talk about how when we sell a product, the dollar comes in, and it goes through credit and collections, and then finally it gets in our bank balance and then it gets invested. This project is really about preserving that dollar by making sure that we have an understanding of where that dollar sits, where we have exposures to companies, and understanding how to manage them.
The 360 exposure to (inaudible, 0:35) and process was something we put together in a very short time last year during the financial crisis. The purpose of it is to give a holistic view of Microsoft’s financial exposures to individual companies. In addition, we want to make sure we understand what the financial health of these companies is and provide management the ability to uncover and mitigate these exposures before they become problematic. Because we know that once a company, for example, declares bankruptcy, you’re in line with everybody else. So if you can figure out what’s going on with the companies and exposures, then you can start tightening your credit policies or you could start unwinding some of your positions.
This is not just a report. It has actually driven some business decisions and discussions in the organization. We have used this report to review some of our exposures to risky counterparties during the financial crisis. In addition, it gives us incentive to diversify some of our banking relationships with additional banking partners. And the reporting process, itself, while it was driven internally by the financial risk management team, was through a collaborative effort because what we’re trying to do is gather exposures from a variety of sources.
We are looking at accounts receivable exposures, so that involves the credit and collections team. We’re looking at bank balances with the banks, and that involves case operations teams. The treasury controller’s group, and the risk management are there. And finally, we have the capital markets risk team, which is responsible for our investment portfolio exposures.
So in the course of a few months last year, we identified key people from these organizations, and worked to understand where these exposures sat in terms of what systems, and what could actually pull these things together. We did this all through Microsoft’s software because we really didn’t have time to set up a big project and find vendors and do RFPs. And we had the necessary tools; use of Sequel Server, Excel, gave us a way to automate all of these data pools and report on them.
Now, I just want to briefly illustrate what our weekly 360-degree exposure reporting looks like. We deliver it through Outlook and SharePoint, and the notable highlights section is usually our own internal analysis of the week’s events. We look at what companies we have exposure to and what’s going on the wires. Are there any rating changes? And, basically, it’s our own analysis. We then include various cuts of our exposure data, including looking in at the top 25 delinquent accounts receivable. In terms of dollars, the top 25 are not that significant individually, but we like to see what companies go in and out, and we work with the credit and collections team to understand what’s changing over there.
Credit Risk Management 2010 Transcript
Next, we take the exposures that I mentioned before -- including AR, bank balances, counterparty exposures, stocks and bonds in these companies -- and then we look at it, for example, by country. As you know, during the financial crisis, it’s not just individual companies and sectors that got caught up but the developed countries such as Iceland, U.K. and Ireland. So we want to be able to look at, for example, the CDS spreads and see where the market is saying the risks of these countries are, and understand our exposures to them in general.
We also look at our high and very high risk exposures, and it’s hard to see the tables here, but we have some financial health ratings that we use to look at the historical financial health of some of these companies that are considered high and very high risk exposure.
Now this is the infamous bubble chart at Microsoft, and I think a lot of people have seen it. I did this presentation before at the Risk USA Conference, and folks were saying, “Where is this coming out of?” And I said, “Excel.” And they said, “What, bubble charts in Excel?” Actually, this is a very useful view of exposures. What it shows is select companies such as autos or retailers or, in our case, top AR, and these numbers are illustrative. Each of these represent a company.
When you look at the Y-axis, what you’re seeing is the dollar AR exposure we have to them. You look at the X-axis, what you’re seeing is the health of the AR. So now, very quickly, you can look at these companies and have a sense of where are, and what the size of the AR exposure is. And looking at the X-axis, you can actually have a pretty good idea that most of these ARs are pretty healthy. They are 70%, 80% or 90% current. But now, in addition, look at the size of the circles we use to represent our portfolio exposures to them. Now you get a very interesting perspective. You know, for example, that Company C at the top has the highest AR exposure, but we have a lot of portfolio exposure to Company F, relatively speaking, to the other companies. That may be because that company happens to be in some of the indices that we benchmark in our portfolio.
Not only that, but we have integrated this reporting process with financial health ratings. As a result, when you look at the colors of these bubbles, you can actually get a sense of what the health of the individual companies are. For example, we can see that the green and blue companies tend to be fairly healthy in terms of their financial health rating, whereas there’s only one company there that looks high risk, and the rest are all moderate to low risk.
This was a very user-friendly way to present our exposures in different industries as well as our top AR to senior management and initiate a lot of discussion. As I said, in a very short period of time, we were able to put this process together and, using Microsoft’s internal tools, aggregate our exposures and present them to senior management, so it can be actionable.
While the results have been pretty good so far, we know that we still have challenges ahead, and we have some additional visions that we want to integrate into this tool. That includes the integration of accounts payable exposures. We also want to have more transparency in exposures that we inherit through alternative investments, private equity, and include financial health ratings on private companies, which is always a significant challenge. We are working with some vendors and also looking at some internal tools to do that. Finally, we want to further automate this reporting process to include additional industries and sectors.
In conclusion, I want to thank Treasury & Risk for recognizing us with this award, and I also want to let you know that all this can be done using Microsoft tools. Thank you.
Credit Risk Management 2010 Transcript
Moderator Peter Seward: Thank you, Bertan. Finally, I’d like to award the Gold Medal to Toyota Financial Services. Representing Toyota is Reddy Pakanati, the chief risk officer for the Americas region. Reddy has been with Toyota since 1997. Since July 2006, he has held the position of chief risk officer. He is responsible for risk management, including market risk of consumer credit, commercial credit and collections for the Americas region.
Since being at Toyota, he has held positions such as corporate manager of banking products for TFS Bank, where he was responsible for bank strategy, dealer-banking products, risk management, channel management, credit card portfolio and new product development. He has also set up risk management functions at TFS China start-ups. He’s the founding chairman of TFS’s Credit Risk Management Functional Committee. Prior to joining Toyota, Reddy was with American Express, where he was director of risk management. Reddy has done graduate studies in agricultural economics from Oklahoma State University. Congratulations, Toyota, and in particular, Reddy.
Reddy Pakanati, chief risk officer for the Americas region, Toyota Financial Services: Good morning. I want to start off by thanking Treasury & Risk magazine for the recognition of TFS. I also want to thank a few people who were an integral part of the solution and the success that we have had. Adem Yilmazhad planned on being here, but he busted up his knee on Sunday playing soccer, so he couldn’t be here. He helped make this vision and my strategy a success. And iIt’s very key to have someone who can take that from a high level and implement a solution. I also want to thank Frank Churchill, a business partner from the sales side, and also Theresa Gamble from our technology group and also part of the solution. Finally, I’m sure you’re intimately familiar with Toyota Financial Services over the years, and I want to recognize our treasury group for blazing the trail here for TFS to be successfully recognized to these lengths.
In case you didn’t recognize this car, this is our third generation Prius. It’s a beautiful car, and maybe should have been an entry in the green theme yesterday.
You heard yesterday from Cindy Wang about the Toyota Financial Services business. We have 3.8 million loans and leases, and I do want to take a minute to point out what’s unique about our business model. We are an indirect consumer lender, meaning we source all business through the dealers. Consumers shop for a car, they apply for a loan, and that’s how we get introduced to the consumer, the applicant.
We’re also a captive finance company. We’re here to support sales and growth of Toyota vehicles. And we are also very relationship and brand focused. I have had the good fortune of working for two of the greatest brands, American Express and Toyota.
Finally, one thing that I want to reiterate is the wisdom of Toyota in taking a long-term perspective and demonstrating to the markets, through our consumers and dealers, that we are here for the long run.
Peter talked about economic backdrop. This has been the learning of a lifetime for me, for sure. And I want to quickly cover this. I won’t go into much detail. Back in 2007, we started noticing that our more recent vintages weren’t performing the way we thought they were. But at that time we didn’t know the extent or the depth of what was about to unfold. It started off primarily as a subprime housing issue, primarily in California, and there were some false alarms as well. One in particular was the adjustable rate mortgage reset. There’s a lot of talk about the ARMs’ resetting being a ticking time bomb. It was a major issue, but at the end of the day, I don’t think it was the crisis that many had thought it would be.
So as the outlook and the events unfolded, we started looking at the types of business we were acquiring, which had been performing reasonably well up until this crisis. But external events were driving a phenomenal change in performance, driven by the unfolding of the housing home equity boom. The household balance sheets were extremely leveraged, a very high leverage, consumers that had taken on debt. Their real income had not kept pace, but there was the illusion of wealth created by the availability of liquidity, which we now know, is the availability of debt. And there was also the inflated home prices and the values.
Credit Risk Management 2010 Transcript
These were some of the expected and unexpected actions, and by the time we got to 2008, soon after we launched our solution, the unexpected events clearly were the credit crisis and, very notably for the automotive industry, the spike in oil, when it reached almost $150 a barrel and gas was almost $5 at some gas stations in California. We also saw the liquidity crisis in when dealers were very hard pressed to get financing, and we were successful in staying with our dealers and providing the financing that they needed through the crisis.
So this is the framework of our objective. Like I said, we take a long-term view of things at Toyota and we are very dealer and consumer focused. Our objective was to improve profitability, improve our risk profile and continue dealer support. This was very important because, being part of a captive finance company, one of our primary functions is to support our dealers. Every change we made had to be cost-effective from both the tangible and intangible costs, because we wanted to show that we weren’t just reacting, that this was not a knee-jerk reaction to the situation.
We also saw this as an opportunity to build brand loyalty by sticking with our dealers and demonstrating to consumers that TFS is not just another finance company, or just there to provide a loan, but to build a relationship through good and bad times.
So the strategy that we came up with was multi-faceted and cross-functional in nature. We wanted to act fast, be decisive but not over-reach. In the industry, we were seeing where a lot of our competition had taken a hatchet approach to the problem in becoming extremely conservative and not considering credit in its entirety, with the different dimensions that make up the performance of a loan. So we took a much deeper view than that.
Let’s start with legal. We can’t do a whole lot without legal involvement, so we had to get their blessing in the changes and the strategy that we were about to implement.
As for technology, our systems had some challenges in being able to implement some of the changes, but we had to partner with them and come up with a solution that was easy to implement from a technology perspective, and they came through for us.
With communication to the field, we had to be crystal clear in the action we were taking. So communication to our field and credit teams, as well as to the dealers, was very critical. And our internal communication at corporate and to our senior management to get them on board was also critical.
The solution had to be seamless and sustainable just by its simplicity, and it had to resonate with the people that were about to implement or be impacted by this solution.
Because we wanted to be very surgical in our approach, if you looked at it purely from a credit risk perspective, looking at either FICO scores or credit scores, it was clear that there were some high-risk segments that were performing reasonably. Or they were strategic to our business model. So we wanted to leave those segments alone.
We focused instead on an extremely low-volume, low-profitability segment. By targeting a small percentage of the portfolio, which had a disproportionate impact on our performance from a loss of profitability perspective, we came up with this thing called an X Grade, which truly ended up being the X factor through the economic crisis for the consumer business at TFS.
This is a simulation that we made based on the changes we were about to make. That’s the time line of how losses go up over time and then, over the life cycle of the loan, start declining. Our simulation showed that we could have this level of impact on the new business we started booking early last year in 2008.
We have continued to monitor the results. True to what we had simulated, I am very confident we are beginning to see that we will see a benefit of at least $100 million in Year 1 from reduced losses.
Credit Risk Management 2010 Transcript
Generally when you take drastic action, it does have an adverse impact on your market share. So with the targeted action that we took and some other actions the company took as part of our business strategy, we also saw that our market share went from almost 50 per cent to 58 per cent, which is quite an accomplishment in this downturn. End of the day, we ended up impacting less than 2% of the origination volume with the X Grade implementation. The decision was not automated, but the criteria that went into this was.
We looked at many segments -- we have hundreds of thousands of segments -- and we came up with a handful of segments. The X-grade implementation enabled the automation of the display of that information for the decision maker, not the decision itself. We are a very high-touch business model and we wanted to make sure that we have a dialogue with the dealers in helping them understand why we are not booking these contracts or approving these contracts anymore.
So that freed up time to restructure some of the more marginal deals more effectively that needed to be restructured. The beauty of this, end of the day, was an implementation cost of less than $300,000.
In conclusion, what we learned is that you have to balance both the analytics and judgment and common sense. That plays a very critical part. We couldn’t rely exclusively on the models. We had to come up with a solution that made sense to business, because their acceptance and their selling of the solution to the dealers and to keep our business relationship was very critical. Thank you for your time.
Questions and Answers:
Moderator Peter Seward: Thank you, Reddy. I’d like to please congratulate all the award winners on their success this year. We’re now ready to take questions from the floor.
Q: This question is for Tom. With the concept of the economic customer groups, I’m curious how you evaluated and treated certain customers where you may have had a customer who had either subsidiaries or joint ventures that may not necessarily have qualified as having the same credit status. How did you go about splitting those out?
Thomas Braida, Cisco: Just like that. We could split out and just make a certain subsidiary its own economic customer. That’s one way to do it. In certain cases, with more of our high-risk systems integrators and partners, a lot of times at the contractual level, we’ll get guarantees, and in that case, we’ll just have one economic customer. But in cases where we have groups where the ownership isn’t over 50%, or if we have concerns, we would just make it its own economic customer and review them as a stand-alone.
Q: This is to Bertan. The Microsoft Excel applications that you guys used, how do you guys do Sarbanes-Oxley compliance? Microsoft Excel gives you a time and point to do things, and then the model changes or the inputs change, and then you’ve got all this data or you’ve got reports. How do you know what those models are, or how do you go back and actually have traceability in your models versus the output?
Bertan Akin, Microsoft: Specific to this reporting process, we didn’t necessarily build a model. What we’ve done is really aggregate the exposures from various data sources and sliced them and diced them in industries and sectors, and integrate them with financial health ratings. Specific to this solution, it was more about finding a way of automating the process for bringing in these exposures and mapping them across the counterparties’ companies and rolling them up. With respect to specific Excel models, the models that the capital markets team uses as a risk manager, for example, my team is responsible for actually reviewing any new financial model that’s used for trading purposes. For example, let’s say the FX team is going to be trading certain options, and they’re going to structure it using Excel, they have to go through a model review process that includes my team.
Q: It’s not easy to go back and do regression testing, right? You could have hundreds of models, right?
Bertan Akin, Microsoft: For example, in the risk world, we manage our risk using, actually, a risk system. In that case, the models are already built in, and we do back testing of those models, where we actually look at the historical gains and losses and make sure that we are actually seeing the same amount of confidence levels in terms of outliers as the models suggest.
Q: This question is for Tom and Bertan. How quickly can you pull together your aggregate exposure to any given counterparty on an off-reporting cycle period? If the CFO were to say, “What’s our exposure to Citibank at any given day?”, how quickly can you pull that together?
Bertan Akin, Microsoft: If you wanted to pull a specific company’s exposures, it’s actually pretty quick. For example, we’ve built the whole report so we can run it in about an hour, an hour and a half, and then the rest of the time is spent doing the analysis so that before we send it out, we know what the questions are going to be. If somebody was to ask what the specific exposure roll-up for Citibank is, we could do that fairly quickly because we have a Sequel server solution that pulls in data from all these different sources, and it’s fairly automated. Now, there could be an issue with the timing of the pulls. For example, portfolio exposures are updated at the end of the day. So if you were to update as of Monday, then maybe you would have to wait until Tuesday morning to update that report. So there is that timeliness, but the actual running of the report is pretty quick.
Thomas Braida, Cisco: With ours, we just go into our eCredit system and it’s real-time reporting. So we’d just key in Citibank, look at the economic customer level and we can, with the touch of a button, immediately have all the outstanding exposure that relates up to that credit limit, whether it’s AR or any orders in backlog. Another thing we do is, every morning a report goes out to everyone in the credit and collections team showing at that particular day what all the global credit limits and exposures are. And that report can be run very quickly, as well.