The low interest rate environment has faced banks with structural changes in customer behavior and converging products such as savings and current accounts. ING, one of Europe’s largest players in the savings market and a long-term client of Zanders, has positioned itself as one of the frontrunners in this environment. We sat down with Tom Tschirner (head of market risk at ING Germany) and Maarten Hummel (financial risk officer at ING Group) to gather their view on modelling and balance sheet management after these structural shifts.
In some European countries, savings rates appear to have hit a limit where they have stayed at a low level for a few years, despite interest rates moving down. This would suggest a structural shift where the relation between interest rates and savings rates has broken down. How can banks model savings in this unprecedented situation?
Tom Tschirner: “The situation is different everywhere. Within the countries where we are active, the legal and regulatory frameworks are very different. For example, in countries like Italy or Belgium, the law prohibits further decreases in specific interest rates. In Germany, this regulatory restriction is not in place. From a modeling perspective, this introduces a very different dynamic.”
Maarten Hummel: “It seems all banks are struggling with the impact of these low interest rates on the behavior of their customers. There is no real history on these low rates to use in our modeling. To develop forward-looking scenarios and to know how to model these scenarios we therefore work even more closely with the business.”
How do you weigh these expert opinions in unprecedented scenarios versus historic observations?
Tschirner: “The political wind is towards using historic data. It is challenging to substantiate what you have based your expert opinion on with a regulator. Using data-based model decisions is more straightforward from that point of view, as the model is then objectively determined. However, there are situations like the one we have now, when you just have no or very limited data. And then you must use expert judgement.
The question is then: how good can the experts be? We neither have data nor experience with the current situation. What becomes important in that situation is not to do stupid things. It’s important to know what competitors are doing. For example, if you find out that on average their deposits are modeled for the duration of three years and your own model indicates you should use seven years, you should take a break and reconsider. Particularly when you don’t have enough data and experience.”
What is your role in this as a market risk manager?
Tschirner: “Our role is always to make sure that common sense is around the table and that everyone who is somehow affected by the model knows how much it depends on expert opinion, data, competition and common sense.”
Hummel: “We always have to be sure that we understand and can explain the dynamics in the forward-looking scenarios; how the bank reacts, how the clients react, what would happen in the wider savings market and other relevant factors. There needs to be a logic to explain the scenario outcomes, both on the savings portfolio and the overall balance sheet. We always look at what it means for the bank as a whole, for example: how would we manage the total bank in such a situation? It is not just a simple exercise of running a savings model based on historic data to get the answers – more important is that you assess the overall plausibility. Therefore, when calibrating our savings models, we now spend more time discussing the scenarios in-depth with the various stakeholders in the bank.”
Does that mean that both quantitative and qualitative elements are discussed?
Hummel: “The business strategy is leading. We use a global framework for our business strategy to look at how it would play out in a certain environment. Then you need to have discussions on whether that strategy will really hold in the more severe scenarios. We do take scenarios into account in a more qualitative strategy discussion. We have to look at the market, our own balance sheet and how we are positioned. It is an interesting discussion.”
To what extent do you look at the restrictions on the lending side in discussions on savings modeling?
Hummel: “The starting point is to look at the saving portfolio independently, but at some stage you cannot escape the rest of the balance sheet. For example, if I have a 50-year liability, where am I going to invest it and what is my funding value? There needs to be a check to see if the value attached to it exists.”
Tschirner: “At the end of the day, when it comes to modeling savings, the question that we are trying to answer is: how should we invest the money that we get from our clients? And can you do that totally independently of the asset situation? Most likely not. If the model tells you to invest the money for fifty years, but there are no such assets in the economy, the model is not very helpful. I would not say it is the individual situation of the bank that matters, but more the economy or the country. How easy is it to find long-term assets in Germany, Poland, or Belgium? That certainly plays an important role for the modeling of savings. One year ago, I may not have subscribed to this view, but now I’m quite convinced about that.”
Do the low savings rates impact the relation between the balances on payment accounts and the saving accounts?
Hummel: “Before, the idea was that these have different functions; one for the transactions and one to earn interest. The incentive on the savings side has now largely disappeared. Inevitably, we see many more funds staying on the current account. The question is then: how can you separate the two parts? The client does not bother to put it on the savings account, because the interest is the same. But since we have to be prepared for a scenario where interest rates will go up significantly again, we keep identifying that money as savings. You need data to identify the amount of transactional account money and separate that from the savings amount. Rates have been low for a long period already, so for a newly started bank estimating that will be very hard.”
A large portion of German ING clients is relatively new. Is it therefore harder to get the right data?
Tschirner: “There are different ways to look at it, but what we clearly observe is that the average balance of the current accounts is increasing quite significantly. You can relook at history and try to find a trend, to see what the average balances would be if it were not for the low-rate environment. Or you can look at intra-monthly patterns, driven for example by salaries and rents. If there is a threshold above which you do not find a pattern anymore, then it looks more like a savings account. These are two approaches to determine which part should be modeled as true current account money and which part as savings. There is no standard yet, but given the regulatory attention, we will find an industry standard in the coming year.”
Do you think it is a common blind spot that the segmentation between those two is often not explicitly modeled?
Tschirner: “It’s not the biggest issue that we have. But yes, you need a model. If you want a real good model though, you need all legs of the cycle; you would also need an observation from a point in time that rates increase – and you don’t have that.”
Hummel: “I agree, you need a full cycle. The challenge is that for each solution you put on this, you need an exit strategy, so once savings rates go up again and market rates are high, you gradually build down the savings on your current account. In the meantime, every client is different. We have different sets of clients and you need to have data on how your client composition is changing over time.”
Tschirner: “In Germany, ING is growing, and the number of accounts has been increasing a lot. We also know that the average age of our clients has gone up. You could argue that older clients intend to have higher balances on their accounts and that they do not shift it when rates are around zero. But if you look at data, you will not be able to tell the difference. And there is no data-based way of telling this apart. That makes it challenging to model.”