The following articles were authored by The Axway Blog Team
What larger banks do to minimize the difficulty of automating stress testing

This is an excerpt from a transcript of The Axway Podcast, “What larger banks do to minimize the difficulty of automating stress testing.”

ANNOUNCER: Forbes writer Tom Groenfeldt recently published a piece titled, “Compliance Efforts Can Bring Business Benefits for Banks,” and he quoted a risk consultant in it who noted that “For the larger banks, over $50 billion, it is more difficult to automate the stress testing because of the sheer volume of data and the number of systems they have in place. Big banks this year identified data integration as their big challenge. That was not the top challenge for banks with $10 to $50 billion.”

PETER BENESH: A lot of the challenges around data integration are the ability to access data that especially resides on mainframes and legacy systems. Or in this case, it may be in the cloud. Or it may not even be in a database. It could be in Excel spreadsheets. It could be unstructured data that doesn’t even reside in a formatted database.

ANNOUNCER: That’s Peter Benesh, Axway’s director of solution marketing for the Financial Services industry. We asked him to describe what those larger banks do to minimize the difficulty of automating stress testing.

PETER BENESH:  The bigger the bank is, the more likely it is that it has a greater variety of both hardware and software technologies. Probably the bigger they are, probably the more mainframes they might have. The more in-house developed reporting systems they may have. The data integration challenge really becomes “Does the technology that they have provide all of the various connectors that are required to enable their data integration platform to extract information from every possible data source?” The next challenge in that is once you extract it, depending on what the source is, the data formats are most likely going to be in various structures. In order to get all of that information into an analytic server, you’ve got to translate all of that into one common data structure. The greater variety of sources you have — not only do you need to have more connectors, but you also have to have a greater library of data transformation capabilities. Such that you can translate or transform data formats that are coming from Oracle, that are coming from a mainframe, that are coming from flat files. Because all of that has to be put into a common format that an analytic server like Hadoop, for example, can digest or ingest for analytics. That whole process is traditionally called ETL — extract, transform, load. That’s what that acronym means. We aren’t really in the business of ETL, per se, but to the extent that any of this data that they need to integrate also needs to come from outside their organization… Let’s say they have information in the cloud, or maybe they have information for partners that they want to integrate into these exercises. Anything that they would need to do more of a B2B-type integration with, bringing information through their firewalls… And, of course, our gateway technologies could help them with that. It’s really a function of how broadly do they want to scope the integration exercise. Do they want to restrict it simply to information that resides in their internal systems? If that’s the case, then pretty much a very robust ETL tool is what they need. If they want to expand the scope to include both internal and external data, then they need strong ETL and they need strong MFT.

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One concerned about profitability, the other concerned about growth

This is an excerpt from a transcript of The Axway Podcast, “One concerned about profitability, the other concerned about growth.

ANNOUNCER: Forbes writer Tom Groenfeldt recently published a piece titled “Compliance Efforts Can Bring Business Benefits for Banks,” and he quoted a risk consultant in it who noted that bankers he recently surveyed were “split into two groups — one concerned about profitability and the other more concerned about achieving growth. Those focused on profitability were concentrating on regulatory stress testing, model review and more operational issues that were a little more backward looking. The growth-oriented bankers were looking at capital planning and allocation.”

PETER BENESH: First of all, we need a clear understanding of what these two groups are. Best as I can interpret, one is a group of banks that actually have completed the stress tests that are required by the regulators. This is all part of what’s called the Basel III regulations. Some banks have gone ahead and actually done the stress testing, other banks are trying to figure out how they’re going about actually complying.

ANNOUNCER: That’s Peter Benesh, Axway’s director of solution marketing for the Financial Services industry. We asked him to explain to us what sort of technologies he would recommend to each group, what sort of actions he’d recommend those groups take to achieve their goals, and why?

PETER BENESH:  There’s a period of time that the regulators have given banks within which they can comply, so not everybody has to do it immediately. Some are being more proactive about it than others. The stress testing itself is a hypothetical exercise and essentially involves a scenario of very adverse economic circumstances such as very high unemployment, very high interest rates, such that people aren’t taking loans out as much. So a bank’s interest income might be declining. Concerns about stock market crashes. And people might be pulling their money out of the banks. It’s essentially a series of hypothetical scenarios that the banks need to quantify and see what happens to their balance sheet under such a situation. Meaning, if their income drops significantly due to less loans or if depositors pull a bunch of money out so that they have less money to loan, do they have enough capital in the form of stock that they’ve issued or income that they retained as reserves, to survive a crisis like that? Can they still pay all their operating expenses by pulling money out of their reserves?  Essentially the same idea as a household looking at how much savings they have, and could they survive for a year if the people making income for the household get unemployed, for example. So that’s the essence of what we’re talking about. The technologies that are required to do the stress testing — it’s a big data integration exercise and really integration of data that’s internal to the organization. They have to be able to pull information from all kinds of databases, both databases that track revenue from loans, databases that track how much investment money they’re keeping. They have to essentially create a hypothetical repository of all that integrated information out of which they will then create hypothetical balance sheets and income statements running those statements using the assumptions of the tests. So it’s data integration in more of the classic sense of pulling data from mainframes, servers, Excel spreadsheets, many, many different sources, internal to the bank. Transforming those and consolidating them into a data warehouse and then running financial analytics against that. The ones who are concentrated on profitability, those are the ones that haven’t actually done the stress tests yet. They’re trying to figure out “How are we going to build models? How are we going to quantify these adverse economic assumptions? How are we going to create the integration flows to create the database of information we need and then create the hypothetical financial statements?” They don’t know yet what the results of those stress tests are going to be. They don’t know if the outcome is going to show that in a real bad economic situation, perhaps they don’t have enough capital to survive. Naturally, they’re going to be more focused on “We got to remain profitable today” whereas the ones that have already completed the stress tests, if the test results show that they would be in danger of going out of business, then they are taking steps to increase their reserves, increase the amount of capital that they are keeping. That may involve they decide to issue more stock. They may decide that they can cut back a little bit on how much of their case reserves they’re actually loaning out. That’s why they’re able to look more forward, because they already know if their capital reserves are adequate or not. Technology is, again, classic data integration, ETL type of tools. You need a very powerful analytic server. There’s a lot of companies that offer that. Hadoop is a very popular tool for that now because you can store massive amounts of data in Hadoop and then do analytics on it. I think our accounting integration suite could also help these companies because we don’t actually do the financial reporting but we could help them create rules by which they could create a hypothetical G.O. under these types of scenarios. First, you have to collect all the information, all the financial information. Put it into a hypothetical general ledger with all the accounts, your income statement accounts, your balance sheet accounts. Then run your reports against that. It’s almost like creating a whole separate accounting and reporting system that’s all based on hypothetical assumptions.

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To listen to the podcast on YouTube (audio only), please click here.