Since the introduction of the Pillar 1 capital charge for market risk, banks must hold capital for Foreign Exchange (FX) risk, irrespective of whether the open FX position was held on the trading or the banking book. An exception was made for Structural Foreign Exchange Positions, where supervisory authorities were free to allow banks to maintain an open FX position to protect their capital adequacy ratio in this way.

In our previous article, the reasons for a new reference rate (SARON) as an alternative to CHF LIBOR were explained and the differences between the two were assessed. One of the challenges in the transition to SARON, relates to the compounding technique that can be used in banking products and other financial instruments. In this article the challenges of compounding techniques will be assessed.

The MoRM division of ABN AMRO comprises around 45 people. What are the crucial conditions to run the department efficiently?

Habing: “Since the beginning of 2019, we have been divided into teams with clear responsibilities, enabling us to work more efficiently as a model risk management component. Previously, all questions from the ECB or other regulators were taken care of by the experts of credit risk, but now we have a separate team ready to focus on all non-quantitative matters. This reduces the workload on the experts who really need to deal with the mathematical models. The second thing we have done is to make a stronger distinction between the existing models and the new projects that we need to run. Major projects include the Definition of default and the introduction of IFRS 9. In the past, these kinds of projects were carried out by people who actually had to do the credit models. By having separate teams for this, we can scale more easily to the new projects – that works well.”What exactly is the definition of a model within your department? Are they only risk models, or are hedge accounting or pricing models in scope too?

“We aim to identify the widest range of models as possible, both in size and type. From an administrative point of view, we can easily do 600 to 700 models. But with such a number, we can't validate them all in the same degree of depth. We therefore try to get everything in picture, but this varies per model what we look at.”

To what extent does the business determine whether a validation model is presented?

“We want to have all models in view. Then the question is: how do you get a complete overview? How do you know what models there are if you don't see them all? We try to set this up in two ways. On the one hand, we do this by connecting to the change risk assessment process. We have an operational risk department that looks at the entire bank in cycles of approximately three years. We work with operational risk and explain to them what they need to look out for, what ‘a model’ is according to us and what risks it can contain. On the other hand, we take a top-down approach, setting the model owner at the highest possible level. For example, the director of mortgages must confirm for all processes in his business that the models have been well developed, and the documentation is in order and validated. So, we're trying to get a view on that from the top of the organization. We do have the vast majority of all models in the picture.”

Does this ever lead to discussion?

“Yes, that definitely happens. In the bank's policy, we’ve explained that we make the final judgment on whether something is a model. If we believe that a risk is being taken with a model, we indicate that something needs to be changed.”

Some of the models will likely be implemented through vendor systems. How do you deal with that in terms of validation?

“The regulations are clear about this: as a bank, you need to fully understand all your models. We have developed a vast majority of the models internally. In addition, we have market systems for which large platforms have been created by external parties. So, we are certainly also looking at these vendor systems, but they require a different approach. With these models you look at how you parametrize – which test should be done with it exactly? The control capabilities of these systems are very different. We're therefore looking at them, but they have other points of interest. For example, we perform shadow calculations to validate the results.”

How do you include the more qualitative elements in the validation of a risk model?

“There are models that include a large component from an expert who makes a certain assessment of his expertise based on one or more assumptions. That input comes from the business itself; we don't have it in the models and we can't control it mathematically. At MoRM, we try to capture which assumptions have been made by which experts. Since there is more risk in this, we are making more demands on the process by which the assumptions are made. In addition, the model outcome is generally input for the bank's decision. So, when the model concludes something, the risk associated with the assumptions will always be considered and assessed in a meeting to decide what we actually do as a bank. But there is still a risk in that.”

How do you ensure that the output from models is applied correctly?

“We try to overcome this by the obligation to include the use of the model in the documentation. For example, we have a model for IFRS 9 where we have to indicate that we also use it for stress testing. We know the internal route of the model in the decision-making of the bank. And that's a dynamic process; there are models that are developed and used for other purposes three years later. Validation is therefore much more than a mathematical exercise to see how the numbers fall apart.”

Typically, the approach is to develop first, then validate. Not every model will get a ‘validation stamp’. This can mean that a model is rejected after a large amount of work has been done. How can you prevent this?

“That is indeed a concrete problem. There are cases where a lot of work has been put into the development of a new model that was rejected at the last minute. That's a shame as a company. On the one hand, as a validation department, you have to remain independent. On the other hand, you have to be able to work efficiently in a chain. These points can be contradictory, so we try to live up to both by looking at the assumptions of modeling at an early stage. In our Model Life Cycle we have described that when developing models, the modeler or owner has to report to the committee that determines whether something can or can’t. They study both the technical and the business side. Validation can therefore play a purer role in determining whether or not something is technically good.”

To be able to better determine the impact of risks, models are becoming increasingly complex. Machine learning seems to be a solution to manage this, to what extent can it?

“As a human being, we can’t judge datasets of a certain size – you then need statistical models and summaries. We talk a lot about machine learning and its regulatory requirements, particularly with our operational risk department. We then also look at situations in which the algorithm decides. The requirements are clearly formulated, but implementation is more difficult – after all, a decision must always be explainable. So, in the end it is people who make the decisions and therefore control the buttons.”

To what extent does the use of machine learning models lead to validation issues?

“Seventy to eighty percent of what we model and validate within the bank is bound by regulation – you can't apply machine learning to that. The kind of machine learning that is emerging now is much more on the business side – how do you find better customers, how do you get cross-selling? You need a framework for that; if you have a new machine learning model, what risks do you see in it and what can you do about it? How do you make sure your model follows the rules? For example, there is a rule that you can't refuse mortgages based on someone's zip code, and in the traditional models that’s well in sight. However, with machine learning, you don't really see what's going on ‘under the hood’. That's a new risk type that we need to include in our frameworks. Another application is that we use our own machine learning models as challenger models for those we get delivered from modeling. This way we can see whether it results in the same or other drivers, or we get more information from the data than the modelers can extract.”

How important is documentation in this?

“Very important. From a validation point of view, it’s always action point number one for all models. It’s part of the checklist, even before a model can be validated by us at all. We have to check on it and be strict about it. But particularly with the bigger models and lending, the usefulness and need for documentation is permeated.”

Finally, what makes it so much fun to work in the field of model risk management?

“The role of data and models in the financial industry is increasing. It's not always rewarding; we need to point out where things go wrong – in that sense we are the dentist of the company. There is a risk that we’re driven too much by statistics and data. That's why we challenge our people to talk to the business and to think strategically. At the same time, many risks are still managed insufficiently – it requires more structure than we have now. For model risk management, I have a clear idea of what we need to do to make it stronger in the future. And that's a great challenge.”

According to Constant Thoolen, global head of financial risk at ING, the Accelerating Think Forward strategy, an updated version of the Think Forward strategy that they just call ATF, comprises several different elements.

"Standardization is a very important one. And from standardization comes scalability and comparability. To facilitate this standardization within the financial risk management team, and thus achieve the required level of efficiency, as a bank we first had to make substantial investments so we could reap greater cost savings further down the road."

And how exactly did ING translate this into financial risk management?

Thoolen: "Obviously, there are different facets to that risk, which permeates through all business lines. The interest rate risk in the banking book, or IRRBB, is a very important part of this. Alongside the interest rate risk in trading activities, the IRRBB represents an important risk for all business lines. Given the importance of this type of risk, and the changing regulatory complexion, we decided to start up an internal IRRBB program."

So the challenge facing the bank was how to develop a consistent framework in benchmarking and reporting the interest rate risk?

"The ATF strategy has set requirements for the consistency and standardization of tooling," explains Gilbert van Iersel, head of financial risk analysis. "On the one hand, our in-house QRM program ties in with this. We are currently rolling out a central system for our ALM activities, such as analyses and risk measurements—not only from a risk perspective but from a finance one too. Within the context of the IRRBB program, we also started to apply this level of standardization and consistency throughout the risk-management framework and the policy around it. We’re doing so by tackling standardization in terms of definitions, such as: what do we understand by interest rate risk, and what do benchmarks like earnings-at-risk or NII-at-risk actually mean? It’s all about how we measure and what assumptions we should make."

What role did international regulations play in all this?

Van Iersel: "An important one. The whole thing was strengthened by new IRRBB guidelines published by the EBA in 2015. It reconciled the ATF strategy with external guidelines, which prompted us to start up the IRRBB program."

So regulations served as a catalyst?

Thoolen: "Yes indeed. But in addition to serving as a foothold, the regulations, along with many changes and additional requirements in this area, also posed a challenge. Above all, it remains in a state of flux, thanks to Basel, the EBA, and supervision by the ECB. On the one hand, it’s true that we had expected the changes, because IRRBB discussions had been going on for some time. On the other hand, developments in the regulatory landscape surrounding IRRBB followed one another quite quickly. This is also different from the implementation of Basel II or III, which typically require a preparation and phasing-in period of a few years. That doesn’t apply here because we have to quickly comply with the new guidelines."

Did the European regulations help deliver the standardization that ING sought as an international bank?

Thoolen: "The shift from local to European supervision probably increased our need for standardization and consistency. We had national supervisors in the relevant countries, each supervising in their own way, with their own requirements and methodologies. The ECB checked out all these methodologies and created best practices on what they found. Now we have to deal with regulations that take in all Eurozone countries, which are also countries in which ING is active. Consequently, we are perfectly capable of making comparisons between the implementation of the ALM policy in the different countries. Above all, the associated risks are high on the agenda of policymakers and supervisors."

Van Iersel: "We have also used these standards in setting up a central treasury organization, for example, which is also complementary to the consistency and standardization process."

Thoolen: "But we’d already set the further integration of the various business units in motion, before the new regulations came into force. What’s more, we still have to deal with local legislation in the countries in which we operate outside Europe, such as Australia, Singapore, and the US. Our ideal world would be one in which we have one standard for our calculations everywhere."

What changed in the bank’s risk appetite as a result of this changing environment and the new strategy?

Van Iersel: "Based on newly defined benchmarks, we’ve redefined and shaped our risk appetite as a component part of the strategic program. In the risk appetite process we’ve clarified the difference between how ING wants to manage the IRRBB internally and how the regulator views the type of risk. As a bank, you have to comply with the so-called standard outlier test when it comes to the IRRBB. The benchmark commonly employed for this is the economic value of equity, which is value-based. Within the IRRBB, you can look at the interest rate risk from a value or an income perspective. Both are important, but they occasionally work against one another too. As a bank, we’ve made a choice between them. For us, a constant stream of income was the most important benchmark in defining our interest rate risk strategy, because that’s what is translated to the bottom line of the results that we post. Alongside our internal decision to focus more closely on income and stabilize it, the regulator opted to take a mainly value-based approach. We have explicitly incorporated this distinction in our risk appetite statements. It’s all based on our new strategy; in other words, what we are striving for as a bank and what will be the repercussions for our interest rate risk management. It’s from there that we define the different risk benchmarks."

Which other types of risk does the bank look at and how do they relate to the interest rate risk?

Van Iersel: “From the financial risk perspective, you also have to take into account aspects like credit spreads, changes in the creditworthiness of counterparties, as well as market-related risks in share prices and foreign exchange rates. Given that all these collectively influence our profitability and solvency position, they are also reflected in the Core Tier I ratio. There is a clear link to be seen there between the risk appetite for IRRBB and the overall risk appetite that we as a bank have defined. IRRBB is a component part of the whole, so there’s a certain amount of interaction between them to be considered; in other words, how does the interest rate risk measure up to the credit risk? On top of that, you have to decide where to deploy your valuable capacity. All this has been made clearer in this program.”

Does this mean that every change in the market can be accommodated by adjusting the risk appetite?

Thoolen: “Changing behavior can indeed influence risks and change the risk appetite, although not necessarily. But it can certainly lead to a different use of risk. Moreover, IFRS 9 has changed the accounting standards. Because the Core Tier 1 ratio is based on the accounting standard, these IFRS 9 changes determine the available capital too. If IFRS 9 changes the playing field, it also exerts an influence on certain risk benchmarks.”

In addition to setting up a consistent framework, the standardization of the models used by the different parts of ING was also important. How does ING approach the selection and development of these models?

Thoolen: “With this in mind, we’ve set up a structure with the various business units that we collaborate with from a financial risk perspective. We pay close attention to whether a model is applicable in the environment in which it’s used. In other words, is it a good fit with what’s happening in the market, does it cover all the risks as you see them, and does it have the necessary harmony with the ALM system? In this way, we want to establish optimum modeling for savings or the repayment risk of mortgages, for example.”

But does that also work for an international bank with substantial portfolios in very different countries?

Thoolen: “While there is model standardization, there is no market standardization. Different countries have their own product combinations and, outside the context of IRRBB, have to comply with regulations that differ from other countries. A savings product in the Netherlands will differ from a savings product in Belgium, for example. It’s difficult to define a one-size-fits-all model because the working of one market can be much more specific than another—particularly when it comes to regulations governing retail and wholesale. This sometimes makes standardization more difficult to apply. The challenge lies in the fact that every country and every market is specific, and the differences have to be reconciled in the model.”

Van Iersel: “The model was designed to measure risks as well as possible and to support the business to make good decisions. Having a consistent risk appetite framework can also make certain differences between countries or activities more visible. In Australia, for example, many more floating-rate mortgages are sold than here in the Netherlands, and this alters the sensitivity of the bank’s net interest income when the interest rate changes. Risk appetite statements must facilitate such differences.”

To what extent does the use of machine learning models lead to validation issues?

“Seventy to eighty percent of what we model and validate within the bank is bound by regulation – you can't apply machine learning to that. The kind of machine learning that is emerging now is much more on the business side – how do you find better customers, how do you get cross-selling? You need a framework for that; if you have a new machine learning model, what risks do you see in it and what can you do about it? How do you make sure your model follows the rules? For example, there is a rule that you can't refuse mortgages based on someone's zip code, and in the traditional models that’s well in sight. However, with machine learning, you don't really see what's going on ‘under the hood’. That's a new risk type that we need to include in our frameworks. Another application is that we use our own machine learning models as challenger models for those we get delivered from modeling. This way we can see whether it results in the same or other drivers, or we get more information from the data than the modelers can extract.”

Thoolen: “But opting for a single ALM system imposes this model standardization on you and ensures that, once it’s integrated, it will immediately comply with many conditions. The process is still ongoing, but it’s a good fit with the standardization and consistency that we’re aiming for.”

In conjunction with the changing regulatory environment, the Accelerating Think Forward Strategy formed the backdrop for a major collaboration with Zanders: the IRRBB project. In the context of this project, Zanders researched the extent to which the bank’s interest rate risk framework complied with the changing regulations. The framework also assessed ING’s new interest rate risk benchmarks and best practices. Based on the choices made by the bank, Zanders helped improve and implement the new framework and standardized models in a central risk management system.

BNG Bank was founded more than 100 years ago – firstly under the name Gemeentelijke Credietbank – as a purchasing association with the main task of bundling the financing requirements of Dutch local authorities so that purchasing benefits could be obtained on capital markets. In 1922, the name was changed to Bank voor Nederlandsche Gemeenten and even today the main aim is, in essence, the same. What has changed is the role of local authorities, says John Reichardt, a member of the Board of BNG Bank. He explains: “Over the past few years they have diversified. Many of their responsibilities are now independent or even privatized. Hospitals, electricity boards and housing companies, for example, were in the hands of local authorities but now operate independently. They are, however, still our clients because they provide public services.”

Different to Other Banks

To satisfy the financing requirements of its clients, BNG Bank collects money on the international capital markets to realize ‘bundled’ purchasing benefits. “And we pass these benefits on to our customers,” says Reichardt. “While our customers have become more diverse over time, our product portfolio has widened. Some thirty years ago we became a bank, with a comprehensive banking license, and this meant we could take up short-term loans, make investments, and handle our customers’ payments. We try to be a full-service bank, but then only for services our customers need.”

The state holds half of the shares and the remainder belongs to local authorities and provinces/counties. “Because of this we always have the dilemma: should we go for more profit and more dividend, or should our strong purchasing position be reflected immediately in our prices by means of a moderate pricing strategy? Our goal is to be big in our market – we think we should keep 35 to 50 percent of the total outstanding debt on our balance sheet. We are not striving for maximum profit, and that differentiates us from many other banks. Although we are a private company, we do also feel we are a part of the government,” says Reichardt.

Changed Worlds

BNG Bank has only one branch in The Hague, with 300 employees. The bank has grown considerably, mainly over the past few years. As of the start of the financial crisis, a number of services from other parties have disappeared, so BNG Bank was often called upon to step in. Now, partly as a result of this, it has become one of the systematically important Dutch banks. “From a character point of view, we are more of a middle-sized company, but as far as the balance sheet is concerned, we are a large bank. We earn our money by buying cheaply, but also by trying to pass this on as cheaply as possible to our customers – with a small commission. This brings with it a strong focus on risk management, including managing our own assets and the associated risks. These are partly credit risks, but we have fewer risks than other banks – because, thanks to the government, our customers are usually very creditworthy.”

BNG Bank also runs certain interest rate risks that have to be controlled on a day-to-day basis. “We have done this in a certain way for a long time, but in the meantime the world has changed,” says Hans Noordam, head of risk management at BNG Bank. “So we thought it was time to give the method a face-lift to test whether we are doing it right, with the right instruments and whether we are looking at the right things? We also wanted someone else to take a good look at it.”

So BNG Bank concluded that the interest rate risk framework had to be revised. “Our approach once was state of the art but, as always with the dialectics of progress, we didn’t do enough ourselves to keep up with changes in that respect,” Reichardt explains. “When we looked at the whole management of interest rate risk, on the one hand it was about the departments involved, and on the other hand the measurement system – the instruments we used and everything associated with them used to produce information which enabled decision-making on our position strategy. That is a big project.

Project Harry

Over the past few years various developments have taken place in the area of market risk. When BNG Bank changed its products and methods, various changes also took place in the areas of risk management and valuation, including extra requirements from the regulator. “So we started a preliminary investigation and formed one unit within risk management,” says Reichardt. At the end of 2012, BNG Bank appointed Petra Danisevska as head of risk management/ALM (RM/ALM). “We agreed not to reinvent the wheel ourselves, but mainly to look closely at best market practices,” she says.

Zanders helped us with this. In May 2013 we started an investigation to find out which interest rate risks were present in the bank and where improvement levels could be made.

Petra Danisevska, Head of risk management/ALM (RM/ALM) at BNG Bank

Noordam explains that they agreed on suggested steps with the Asset Liability Committee (ALCO), which also provided input and expressed preferences. A plan was then made and the outlines sketched. To convert that into concrete actions, Noordam says that a project was initiated at the beginning of 2014: Project Harry. “This gets its name from BNG Bank’s location, also the home of a Dutch cartoon character, called Haagse Harry. He was the symbol of the whirlwind which was to whip through the bank,” says Noordam.

Within ALCO Limits

“During the (economic) crisis, all sorts of things happened which influenced the valuation of our balance sheet,” Reichardt explains. “They also had many effects on the measurement of our interest rate risk. We had to apply totally different curves – sometimes with very strange results. Our company is set up in a way that with our economic hedging and our hedge accounting, we can buy for X and pass it on to our customers for X plus a couple of basis points, which during the period of the loan reverts to us. We retain a small amount and on the basis of this pay out a dividend – our model is that simple. However, since the valuations were influenced by market changes, we were more or less obliged to take measures in order to stay within our ALCO limits. These measures, with respect to managing our interest position, would not have been realizable under our current philosophy; simply because they weren’t necessary. We knew we had to find a solution for that phenomenon in the project. After much discussion we were able to find a solution: to be more reliable within the technical framework of anticipating market movements which strongly influence valuation of financial instruments. In other words: the spread risk and the rate risk had to be separately measured and managed from one another. The world had changed and our interest rate risk management, as well as reporting and calculations based upon it, had to as well.”

After revision of the interest rate risk framework, as of the second half of 2015, all interest-rate risk measurements, their drivers and reporting were changed. The market risks as a result of the changes in interest rate curves, were then measured and reported on a daily basis by the RM/ALM department. “There is definitely better management of the interest rate risk; we generate more background data and create more possibilities to carry out analyses,” Danisevska explains. “We now have detailed figures that we couldn’t get before, with which we can show ALCO the risk and the accompanying, assumed return.”

More proactive

Noordam knew that Project Harry would involve a considerable effort. “The risk framework would inevitably suffer quite a lot. It had to be innovated on the basis of calculated conditions, while the implementation required a lot of internal resources and specific knowledge. Technical points had to be solved, while relationships had to be safeguarded; many elements with all sorts of expertise had to be integrated. The European Central Bank was stringent – that took up a lot of time and work. We had an asset quality review (AQR) and a stress test – that was completely new to us. Sometimes we were tempted to stay on known ground, but even during those periods we were able to carry on with the project. We rolled up our shirtsleeves and together we gained from the experience.”

Reichardt says: “It was a tough project for us, with complex subject matter and lots of different opinions. In total it took us seven quarters to complete. However, I think we have accomplished more than we expected at the beginning. With a combination of our own people and external expertise, we have managed to make up for lost ground. We have exchanged the rags for riches and we have been successful. Where do we stand now? As well as the required numbers, we have a clear view of what our thoughts are on ‘what is interest rate risk and what isn’t’. The only thing we still have to do is to fine-tune the roles: what can you expect from risk managers and risk takers, and how will they react to this? We will continue to monitor it. RM/ALM as a department is in any case a lot more proactive – that was an important goal for us. We can be more successful, but the department is really earning its spurs within the bank and that means profit for everyone.”

A replicating investment portfolio is a collection of fixed-income investments based on an investment strategy that aims to reflect the typical interest rate maturity of the savings deposits (also referred to as ‘non-maturing deposits’). The investment strategy is formulated so that the margin between the portfolio return and the savings interest rate is as stable as possible, given various scenarios.

In the 150 years of its existence, Swiss Re has grown to be one of the world’s largest providers of reinsurance and insurance-based forms of risk transfer. Reinsurers are mostly associated with insurance for extreme loss events, such as natural catastrophes. However, Swiss Re’s services cover the entire insurance spectrum: Swiss Re is the counterparty to risks which primary insurance companies and large corporates decide to mitigate.

Liquidity risk

Usually, liquidity is not the first topic that comes to mind as a key risk for reinsurance companies. “The general view was, and kind of still is, that reinsurance companies do not run a lot of liquidity risk, like a bank,” Martin Ramseyer says. For banks, the main driver of liquidity risk is a sudden customer run on deposits. The risk for reinsurers is rather that claims can reach the order of billions, sometimes to be paid out at short notice relative to the magnitude. If sufficient assets cannot be liquidated at a reasonable price within the required time frame, the company not only puts its reputation at stake but also risks bankruptcy – regardless of its solvency or profitability.

From a capital perspective, expanding services across businesses yields a risk diversification benefit. But that benefit does not extend to liquidity, Ramseyer clarifies: “There are many legal limitations imposed by different jurisdictions that limit our abilities to move assets between subsidiaries within the group.” A joint effort of risk and treasury was initiated several years ago to create a framework to measure and manage funding liquidity risk. Initially, the primary objective was to identify potential liquidity constraints for the major legal entities. Calculations gradually grew more extensive, and the framework evolved into an important scenario analysis mechanism used to support executive management decisions. Its execution had become time-consuming, and the operational risk inherent in manual calculations increasingly relevant. The time was ripe to streamline and automate liquidity risk analysis and the reporting process. Andreas Tonn became the business project manager for the system selection and implementation.

Implementation

The choice was made for a vendor solution. “The core advantage is that they provide a framework, which reduces implementation time and facilitates the translation of needs into requirements,” Tonn says. “But as you will never find the perfect tool, it is important to have a clear focus.” Liquidity risk measurement models for the insurance business in vendor systems are still in evolution phase. Flexibility was therefore a key priority for Swiss Re, as the majority of the logic needed to be implemented from scratch. Swiss Re embarked on an intensive proof-of-concept phase, and asked vendors to provide a working demonstration that addressed all aspects of its liquidity risk framework. They chose Wolters Kluwer’s RiskPro, as it proved both mature and sufficiently flexible at the same time.

A phased implementation approach was chosen to gradually introduce the solution into the reporting cycle. However, after the first release, it became apparent the project team would need additional business support if it was to cover all the aspects thoroughly within the stated time frame. “Our internal resources were too committed to other tasks and could not provide support to the extent an intensive project requires,” Tonn says, “but external resources are actually only beneficial to a project if they bring the right expertise to the table.” There were very positive experiences with Zanders on other treasury projects so a request was made for support.

Jeroen van der Heide from Zanders was asked to join the project team: “His ample experience with functional design of various systems across risk domains convinced us that he would indeed accelerate our project.”

Andreas Tonn, business project manager for the system selection and implementation

Challenges

As with many system implementations, getting the data delivered in a systematic fashion was a challenge: different departments have different priorities and downstream reporting is often not on top of their list. Afterwards, the interpretation for modeling was not always clear either as it was complicated by the global reach of the company in which data ownership spans across time zones. For example, intra-group funding was previously measured using a net aggregate approach. With the implementation of RiskPro, the choice made sense to model each debt contract based on its characteristics as booked in the system. Understanding how to calculate the impact of all implicit options automatically for different scenarios required detailed discussions across teams and continents.

The project team has worked relentlessly during the past year in close cooperation with business and IT colleagues. A total of six minor and major releases were accomplished, during which the necessary data and calculations with respect to investments, collateral, reinsurance portfolios, debt, internal cash flows, and contingent funding requirements were added to the system. The RiskPro results were embedded in existing reporting templates, and the change analysis process between reporting dates was partly automated. They were very satisfied with the Zanders support: “Of great benefit was Jeroen’s talent to quickly gain insight out of a huge amount of information, and present the newly created results in such a way to make them understandable to the business user and fit right into existing business processes. That has been a very valuable business contribution.”

Looking towards the future

With the system up and running, the team is able to provide reporting and analytics for the major legal entities within the group and across business segments and branches, rather than only for those with the largest impact from a risk perspective. “It allows us to understand and represent the liquidity dynamics in a more systematic way across the group,” Ramseyer says. The next step is to increase the quantity and quality of the information flow between local business units and treasury. “It will really enhance the risk awareness of local boards and empower them in their active steering efforts. With their feedback they will help improve the framework in return.”

Developments within Treasury Business Services don’t stop there. The system contains the vast majority of Swiss Re’s economic balance sheet down to the transaction and cash flow level. “The vendor software is designed to be an integrated risk system. With the market data and contract data available in full detail, we have a suitable basis to extend the scope of the solution to other domains,” Tonn says. The plan for the next two years, therefore, is to gradually support other analyses, for example with respect to currency risk, funding cost, liquidity planning, and ALM. The consistency between and efficiency of the analyses will improve, enabling the treasury teams to dedicate more time to proactive analysis and steering. Zanders will continue to support these efforts: “Given his successful contribution to the project and his interest to continue to support Swiss Re, we asked Jeroen to manage next year’s project,” Tonn says, “But first things first: we are very much looking forward to the daily use of the implemented solution.”

As a bank providing financial services to business enterprises and financial institutions located in North and Sub-Saharan Africa, the Middle East, and the Indian Subcontinent, Banca UBAE relies on accurate and customized credit ratings for its counterparties. While its previous credit rating process didn’t allow for a tailor-made approach, Banca UBAE has recently begun using the EAGLE internal ratings methodology, which gives its credit analysts the transparency and flexibility they need.

The ongoing changes in its markets make counterparty risk assessment more important than ever for Banca UBAE. This Italian bank, headquartered in Rome, has been operating in countries around the Mediterranean and the Middle East since 1972. Trade finance is Banca UBAE’s single most important line of business, and its main products and services include letters of credit, letters of guarantee, discounting, and forfaiting.

In April 2012, the bank implemented the EAGLE corporate rating model, developed by Zanders and available through the FACT web-based platform, developed by Bureau van Dijk.

Customized Ratings

While an external ratings agency such as Fitch Ratings or Standard & Poor’s has a standard and fixed methodology for calculating a rating, Banca UBAE needed a customizable approach, with a tailor-made and transparent credit rating model for each of its counterparties. This is essential considering the specialized and risk-sensitive nature of the bank’s business.

Fabrizia Calvello, a senior credit analyst at Banca UBAE, explains how the bank’s credit analysts customize the counterparty ratings: “In an internal rating, we evaluate qualitative and quantitative information. The analyst is very well acquainted with the client’s core business and balance sheet, as well as the market within which the client operates, so they can insert qualitative data into the internal ratings model.” An important factor is that data is automatically uploaded from the database into the credit rating model. Calvello adds: “For us as a small bank, it is very important to customize the service to our needs.”

Evert de Vries, one of the two Zanders consultants dedicated to the implementation of EAGLE at Banca UBAE, acknowledges that the need for the customized ratings methodology lies in the nature of the bank’s core business. He says: “The bank is working in a challenging environment, so of course it’s very important for them to be able to calculate specific risks.”

Partnership

Considering the specialized nature of Banca UBAE’s business, there was a need for a customizable ratings methodology. The bank had a long-standing working relationship with Bureau van Dijk, a leading publisher of company information and provider of credit risk management solutions. This relationship developed into a regular and established collaboration when, in 2011, Zanders and Bureau van Dijk joined forces to offer a specialized and flexible credit rating product. The EAGLE ratings methodology is based on Bureau van Dijk’s credit risk management platform, FACT, which integrates information from financial databases such as Amadeus.

Thomas Van der Ghinst, business development manager EMEA at Bureau van Dijk, explains how the project was structured and how the elements led to its success: “Zanders was able to customize the standard EAGLE ratings model and calibrate the model according to specific industry sectors – this is one of their strengths. The combination of the Amadeus database, the FACT platform, and the EAGLE credit ratings model makes this a very competitive solution.” He adds: “For me, EAGLE was a perfect fit for Banca UBAE – it met all the requirements of the project goals.”

Bureau van Dijk’s Van der Ghinst also explains that banks often require customizable credit ratings to be more independent from rating agencies. He says: “Working with EAGLE has helped Banca UBAE to better reflect their risk appetite. Internal ratings also help the bank to provide an instant assessment of new clients – this is the key benefit for Banca UBAE. The added value is that you can rate and evaluate companies that don’t have a rating from a rating agency. You can rate any company in the Amadeus database.”

The combination of the Amadeus database, the FACT platform and the EAGLE credit ratings model makes this a very competitive solution.

Jacopo Ribichini, head of the credit department at Banca UBAE.

Smooth Implementation

Banca UBAE implemented EAGLE in April 2012, and the implementation process took about three weeks. So what benefits has Banca UBAE seen since moving to the EAGLE customizable ratings methodology? Jacopo Ribichini, head of the credit department at Banca UBAE, explains: “The product facilitated and sped up the analysis process. At the same time, it became more transparent and precise. The functionality that allows us to adapt the product score from EAGLE with the specific knowledge Banca UBAE has for each customer allows the correct assessment for the commercial relationship our bank has with each counterparty.”

“Furthermore, the product complies with requirements imposed by EU legislation related to risk analysis. The result is a final assessment that is extremely clear, concise, and exhaustive, offering the best conditions for our deliberating bodies to make business decisions,” he adds.

Overall, Ribichini reports that EAGLE had increased the professionalism and efficiency of his department. The implementation was smooth, and Ribichini sums up his thoughts: “Lastly, thanks to the efficient support offered by the Zanders team, I managed the migration to EAGLE without affecting the regular activities carried out by my department.”

Flexibility and Transparency

Reinoud Lyppens, a consultant at Zanders, works with Evert de Vries on the project and adds that other than providing some independence from ratings agencies, EAGLE has two other main advantages: “It is one single platform that enables you to calculate a credit rating for many different industry sectors and counterparties – and, moreover, it is customizable. These two factors were our prime advantages over our competition. We are very open – the model is well documented and validated every year, so I think that transparency is what really makes EAGLE stand out. The client should always understand the process.”

De Vries adds: “Zanders and Bureau van Dijk worked with Banca UBAE throughout the project, not just during implementation but also at later stages, providing advice and support. This post-implementation service is very important in a project like this – when our customer has a question, we are there to support them. We also did some fine-tuning for the oil & gas and commodities sectors for the model. I think the project went well.”

Van der Ghinst adds: “To date, the project has been a real success. The flexibility and professionalism of the Zanders team have resulted in a very positive outcome, which has been appreciated by the client.”

As Banca UBAE is currently expanding and establishing its presence further afield, in Vietnam and Mozambique (as a result of oil & gas exploration), the flexible and transparent internal ratings methodology will be increasingly important to its business.

About Banca UBAE

Banca UBAE, established in 1972 as ‘Unione delle Banche Arabe ed Europee’, is a banking corporation funded by Italian and Arab capital. Shareholders include major banks such as Libyan Foreign Bank, Banque Centrale Populaire, Banque Marocaine du Commerce Extérieur, UniCredit, Intesa Sanpaolo, and large Italian corporate groups like Eni Group, Sansedoni, and Telecom Italia.

Since 1972, Banca UBAE has acted as a trusted consultant and privileged partner for companies and financial institutions wishing to establish or develop commercial, industrial, financial, and economic relations between Europe and countries in North and Sub-Saharan Africa, the Middle East, and the Indian Subcontinent.

Banca UBAE offers a wide range of services and boasts unique expertise in every form of banking relevant to clients engaged in business on Arab markets, from export financing, letters of credit, and documents for collection to finance, syndications of loans and risks, and on-site professional assistance.