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. A replicating framework enables a bank to base its interest rate risk measurement and management on investments with a fixed maturity and price – while the deposits have no contractual maturity or price. In addition, a bank can use the framework to transfer the interest rate risk from the business lines to the central treasury, by turning the investments into contractual obligations. There are two commonly used methodologies for constructing the replicating portfolios: the marginal investment strategy and the portfolio investment strategy. These strategies have the same objective, but have different effects on margin and interest-rate term, given certain scenarios.

STRATEGIES DEFINED

An investment strategy determines the monthly allocation of the investable volume across various maturities. The investable volume in month t (It) consists of two parts:

The first part is equal to the decrease or increase in the volume of savings deposits compared to the previous month. The second part is equal to the total principal of all investments in the investment portfolio maturing in the current month (end date m = t ), ∑i,m=t vi,m. By investing or re-investing the volume of these two parts, the total principal of the investment portfolio will equal the savings volume outstanding at that moment. When an investment is generated, it receives the market interest rate relating to the maturity at that time. The portfolio investment return is determined as the principal weighted average interest rate. The difference between a marginal investment strategy and a portfolio investment strategy is that in a marginal investment strategy, the volume is invested with a fixed allocation across fixed maturities. In a portfolio strategy, these parameters are flexible, however investments are generated in such a way that the resulting portfolio each month has the same (target) proportional maturity profile. The maturity profile provides the total monthly principal of the currently outstanding investments that will mature in the future. In the savings modelling framework, the interest rate risk profile of the savings portfolio is estimated and defined as a (proportional) maturity profile. For the portfolio investment strategy, the target maturity profile is set equal to this estimated profile. For the marginal investment strategy, the ‘investment rule’ is derived from the estimated profile using a formula. Under long lasting constant or stable volume of savings deposits, the investment portfolio given the investment rule converges to the estimated profile.

STRATEGIES ILLUSTRATED

In Figure 1, the difference between the two strategies is graphically illustrated in an example. The example provides the development of replicating portfolios of the two strategies in two consecutive months upon increasing savings volume. The replicating portfolios initially consist of the same investments with original maturities of one month, 12 months and 36 months. To this end, the same investments and corresponding principals mature. The total maturing principal will be reinvested and the increase in savings volume will be invested.

Figure 1: Two replication portfolio strategies

Note that if the savings volume would have remained constant, both strategies would have generated the same investments. However, with changing savings volume, the strategies will generate different investments and a different number of investments (3 under the marginal strategy, and 36 under the portfolio strategy). The interest rate typical maturities and investment returns will therefore differ, even if market interest rates do not change. For the quantitative properties of the strategies, the decision will therefore focus mainly on margin stability and the interest rate typical maturity given changes in volume (and potential simultaneous movements in market interest rates).

SCENARIO ANALYSIS

The quantitative properties of the investment strategies are explained by means of a scenario analysis. The analysis compares the development of the duration, margin and margin stability of both strategies under various savings volume and market interest rate scenarios.

CLIENT INTEREST RATE

As part of the simulation of a margin, a client interest rate is modeled. The model consists of a set of sensitivities to market interest rates (M1,t) and moving averages of market interest rates (MA12,t). The sensitivities to the variables show the degree to which the bank has to reflect market movements in its client interest rate, given the profile of its savings clients. The model chosen for the interest rate for the point in time t (CRt ) is as follows:

Up to a certain degree, the model is representative of the savings interest rates offered by (retail) banks.

INVESTMENT STRATEGIES

The investment rules are formulated so that the target maturity profiles of the two strategies are identical. This maturity profile is then determined so that the same sensitivities to the variables apply as for the client rate model. An overview of the investment strategies is given in Table 1.

The replication process is simulated for 200 successive months in each scenario. The starting point for the investment portfolio under both strategies is the target maturity profile, whereby all investments are priced using a constant historical (normal) yield curve. In each scenario, upward and downward shocks lasting 12 months are applied to the savings volume and the yield curve after 24 months.

EXAMPLE SCENARIO

The results of an example scenario are presented in order to show the dynamics of both investment strategies. This example scenario is shown in Figure 2. The results in terms of duration and margin are shown in Figure 3.

Figure 2: Example scenario in volume and market interest rate development

Figure 3: Impact on duration and margin from interest up and down scenarios

As one would expect, the duration for the portfolio investment strategy remains the same over the entire simulation. For the marginal investment strategy, we see a sharp decline in the duration during the ‘shock period’ for volume, after which a double wave motion develops on the duration. In short, this is caused by the initial (marginal) allocation during the ‘stress’ and subsequent cycles of reinvesting it. With an upward volume shock, the margin for the portfolio strategy declines because the increase in savings volume is invested at downward shocked market interest rates. After the shock period, the declining investment return and client rate converge. For the marginal strategy this effect also applies and in addition the duration effects feed into the margin development.

SCENARIO SPECTRUM

In the scenario analysis the standard deviation of the margin series, also known as the margin volatility, serves as a proxy for margin stability. The results in terms of margin stability for the full range of market interest rate and volume scenarios are summarized in Figure 4. From the figures, it can be seen that the margin of the marginal investment strategy has greater sensitivity to volume and interest rate shocks. Under these scenarios the margin volatility is on average 2.3 times higher, with the factor ranging between 1.5 and 4.5. In general, for both strategies, the margin volatility is greatest under negative interest-rate shocks combined with upward or downward volume shocks.

REPLICATION IN PRACTICE

The scenario analysis shows that the portfolio strategy has a number of advantages over the marginal strategy. First of all, the maturity profile remains constant at all times and equal to the modeled maturity of the savings deposits. Under the marginal strategy, the interest rate typical maturity can vary from it over long periods, even when there are no changes in market interest environment or behavior in the savings portfolio. Secondly, the development of the margin is more stable under volume and interest rate shocks. The margin volatility under the marginal investment strategy is actually at least one and a half times higher under the chosen scenarios.

AN INTUITIVE PROCESS

These benefits might, however, come at the expense of a number of qualitative aspects that may form an important consideration when it comes to implementation. Firstly, the advantage of a constant interest-rate profile for the portfolio strategy, comes at the expense of intuitive combinations of investments. This may be important if these investments form contractual obligations.

Figure 4: Margin volatility of marginal (left-hand) and portfolio strategy (right-hand) for upward (above) and downward shocks (below)

for the transfer of the interest rate risk. The strategy, namely, requires generating a large number of investments that can even have negative principals in case of a (small) decline of savings volume. Secondly, the shocks in the duration in a marginal strategy might actually be desirable and in line with savings portfolio developments. For example, if due to market or idiosyncratic circumstances there is high inflow of deposit volume, this additional volume may be relatively more interest rate sensitive justifying a shorter duration. Nevertheless, the example scenario shows that after such a temporary decline a temporary increase will follow for which this justification no longer applies.

THE CHOICE

A combination of the two strategies may also be chosen as a compromise solution. This involves the use of a marginal strategy whereby interventions trigger a portfolio strategy at certain times. An intervention policy could be established by means of limits or triggers in the risk governance. Limits can be set for (unjustifiable) deviations from the target duration, whereas interventions can be triggered by material developments in the market or the savings portfolio.

In its choice for the strategy, the bank is well-advised to identify the quantitative and qualitative effects of the strategies. Ultimately, the choice has to be in line with the character of the bank, its savings portfolio and the resulting objective of the process.

For all the criticism that rating models and credit rating agencies have had through the years, they are still the most pragmatic and realistic approach for assessing default risk for your counterparties. Of course, the quality of the assessment depends to a large extent on the quality of the model used to determine the credit rating, capturing both the quantitative and qualitative factors determining counterparty credit risk. A sound credit rating model strikes a balance between these two aspects. Relying too much on quantitative outcomes ignores valuable ‘unstructured’ information, whereas an expert judgement based approach ignores the value of empirical data, and their explanatory power.

In this white paper we will outline some best practice approaches to assessing default risk of a company through a credit rating. We will explore the ratios that are crucial factors in the model and provide guidance for the expert judgement aspects of the model.

Zanders has applied these best practices while designing several Credit Rating models for many years. These models are already being used at over 400 companies and have been tested both in practice and against empirical data. Do you want to know more about our Credit Rating models, click here.

Credit ratings and their applications

Credit ratings are widely used throughout the financial industry, for a variety of applications. This includes the corporate finance, risk and treasury domains and beyond. While it is hardly ever a sole factor driving management decisions, the availability of a point estimation to describe something as complex as counterparty credit risk has proven a very useful piece of information for making informed decisions, without the need for a full due diligence into the books of the counterparty.

Some of the specific use cases are:

• Internal assessment of the creditworthiness of counterparties
• Transparency of the creditworthiness of counterparties
• Monitoring trends in the quality of credit portfolios
• Monitoring concentration risk
• Performance measurement
• Determination of risk-adjusted credit approval levels and frequency of credit reviews
• Formulation of credit policies, risk appetite, collateral policies, etc.
• Loan pricing based on Risk Adjusted Return on Capital (RAROC) and Economic Profit (EP)
• Arm’s length pricing of intercompany transactions, in line with OECD guidelines
• Regulatory Capital (RC) and Economic Capital (EC) calculations
• Expected Credit Loss (ECL) IFRS 9 calculations
• Active Credit Portfolio Management on both portfolio and (individual) counterparty level

Credit rating philosophy

A fundamental starting point when applying credit ratings, is the credit rating philosophy that is followed. In general, two distinct approaches are recognized:

• Through-the-Cycle (TtC) rating systems measure default risk of a counterparty by taking permanent factors, like a full economic cycle, into account based on a worst-case scenario. TtC ratings change only if there is a fundamental change in the counterparty’s situation and outlook. The models employed for the public ratings published by e.g. S&P, Fitch and Moody’s are generally more TtC focused. They tend to assign more weight to qualitative features and incorporate longer trends in the financial ratios, both of which increase stability over time.
• Point-in-Time (PiT) rating systems measure default risk of a counterparty taking current, temporary factors into account. PiT ratings tend to adjust quickly to changes in the (financial) conditions of a counterparty and/or its economic environment. PiT models are more suited for shorter term risk assessments, like Expected Credit Losses. They are more focused on financial ratios, thereby capturing the more dynamic variables. Furthermore, they incorporate a shorter trend which adjusts faster over time. Most models incorporate a blend between the two approaches, acknowledging that both short term and long term effects may impact creditworthiness.

Rating methodology

Modelling credit ratings is very complex, due to the wide variety of events and exposures that companies are exposed to. Operational risk, liquidity risk, poor management, a perishing business model, an external negative event, failing governments and technological innovation can all have very significant influence on the creditworthiness of companies in the short and long run. Most credit rating models therefore distinguish a range of different factors that are modelled separately and then combined into a single credit rating. The exact factors will differ per rating model. The overview below presents the factors included the Corporate Rating Model, which is used in some of the cloud-based solutions of the Zanders applications.

The remainder of this article will detail the different factors, explaining the rationale behind including them.

Quantitative factors

Quantitative risk factors are crucial to credit rating models, as they are ‘objective’ and therefore generate a large degree of comparability between different companies. Their objective nature also makes them easier to incorporate in a model on a large scale. While financials alone do not tell the whole story about a company, accounting standards have developed over time to provide a more and more comparable view of the financial state of a company, making them a more and more thrustworthy source for determining creditworthiness. To better enable comparisons of companies with different sizes, financials are often represented as ratios.

Financial Ratios

Financial ratios are being used for credit risk analyses throughout the financial industry and present the basic characteristics of companies. A number of these ratios represent (directly or indirectly) creditworthiness. Zanders’ Corporate Credit Rating model uses the most common of these financial ratios, which can be categorised in five pillars:

Pillar 1 - Operations

The Operations pillar consists of variables that consider the profitability and ability of a company to influence its profitability. Earnings power is a main determinant of the success or failure of a company. It measures the ability of a company to create economic value and the ability to give risk protection to its creditors. Recurrent profitability is a main line of defense against debtor-, market-, operational- and business risk losses. 

Turnover Growth

Turnover growth is defined as the annual percentage change in Turnover, expressed as a percentage. It indicates the growth rate of a company. Both very low and very high values tend to indicate low credit quality. For low turnover growth this is clear. High turnover growth can be an indication for a risky business strategy or a start-up company with a business model that has not been tested over time.

Gross Margin

Gross margin is defined as Gross profit divided by Turnover, expressed as a percentage. The gross margin indicates the profitability of a company. It measures how much a company earns, taking into consideration the costs that it incurs for producing its products and/or services. A higher Gross margin implies a lower default probability.

Operating Margin

Operating margin is defined as Earnings before Interest and Taxes (EBIT) divided by Turnover, expressed as a percentage. This ratio indicates the profitability of the company. Operating margin is a measurement of what proportion of a company's revenue is left over after paying for variable costs of production such as wages, raw materials, etc. A healthy Operating margin is required for a company to be able to pay for its fixed costs, such as interest on debt. A higher Operating margin implies a lower default probability.

Return on Sales

Return on sales is defined as P/L for period (Net income) divided by Turnover, expressed as a percentage. Return on sales = P/L for period (Net income) / Turnover x 100%. Return on sales indicates how much profit, net of all expenses, is being produced per pound of sales. Return on sales is also known as net profit margin. A higher Return on sales implies a lower default probability.

Return on Capital Employed

Return on capital employed (ROCE) is defined as Earnings before Interest and Taxes (EBIT) divided by Total assets minus Current liabilities, expressed as a percentage. This ratio indicates how successful management has been in generating profits (before Financing costs) with all of the cash resources provided to them which carry a cost, i.e. equity plus debt. It is a basic measure of the overall performance, combining margins and efficiency in asset utilization. A higher ROCE implies a lower default probability.

Pillar 2 - Liquidity

The Liquidity pillar assesses the ability of a company to become liquid in the short-term. Illiquidity is almost always a direct cause of a failure, while a strong liquidity helps a company to remain sufficiently funded in times of distress. The liquidity pillar consists of variables that consider the ability of a company to convert an asset into cash quickly and without any price discount to meet its obligations. 

Current Ratio

Current ratio is defined as Current assets, including Cash and Cash equivalents, divided by Current liabilities, expressed as a number. This ratio is a rough indication of a firm's ability to service its current obligations. Generally, the higher the Current ratio, the greater the cushion between current obligations and a firm's ability to pay them. A stronger ratio reflects a numerical superiority of Current assets over Current liabilities. However, the composition and quality of Current assets are a critical factor in the analysis of an individual firm's liquidity, which is why the current ratio assessment should be considered in conjunction with the overall liquidity assessment. A higher Current ratio implies a lower default probability. 

Quick Ratio

The Quick ratio (also known as the Acid test ratio) is defined as Current assets, including Cash and Cash equivalents, minus Stock divided by Current liabilities, expressed as a number. The ratio indicates the degree to which a company's Current liabilities are covered by the most liquid Current assets. It is a refinement of the Current ratio and is a more conservative measure of liquidity. Generally, any value of less than 1 to 1 implies a reciprocal dependency on inventory or other current assets to liquidate short-term debt. A higher Quick ratio implies a lower default probability.

Stock Days 

Stock days is defined as the average Stock during the year times the number of days in a year divided by the Cost of goods sold, expressed as a number. This ratio indicates the average length of time that units are in stock. A low ratio is a sign of good liquidity or superior merchandising. A high ratio can be a sign of poor liquidity, possible overstocking, obsolescence, or, in contrast to these negative interpretations, a planned stock build-up in the case of material shortages. A higher Stock days ratio implies a higher default probability.

Debtor Days

Debtor days is defined as the average Debtors during the year times the number of days in a year divided by Turnover. Debtor days indicates the average number of days that trade debtors are outstanding. Generally, the greater number of days outstanding, the greater the probability of delinquencies in trade debtors and the more cash resources are absorbed. If a company's debtors appear to be turning slower than the industry, further research is needed and the quality of the debtors should be examined closely. A higher Debtors days ratio implies a higher default probability.

Creditor Days

Creditor days is defined as the average Creditors during the year as a fraction of the Cost of goods sold times the number of days in a year. It indicates the average length of time the company's trade debt is outstanding. If a company's Creditors days appear to be turning more slowly than the industry, then the company may be experiencing cash shortages, disputing invoices with suppliers, enjoying extended terms, or deliberately expanding its trade credit. The ratio comparison of company to industry suggests the existence of these or other causes. A higher Creditors days ratio implies a higher default probability.

Pillar 3 - Capital Structure

The Capital pillar considers how a company is financed. Capital should be sufficient to cover expected and unexpected losses. Strong capital levels provide management with financial flexibility to take advantage of certain acquisition opportunities or allow discontinuation of business lines with associated write offs.  

Gearing

Gearing is defined as Total debt divided by Tangible net worth, expressed as a percentage. It indicates the company’s reliance on (often expensive) interest bearing debt. In smaller companies, it also highlights the owners' stake in the business relative to the banks. A higher Gearing ratio implies a higher default probability.

Solvency

Solvency is defined as Tangible net worth (Shareholder funds – Intangibles) divided by Total assets – Intangibles, expressed as a percentage. It indicates the financial leverage of a company, i.e. it measures how much a company is relying on creditors to fund assets. The lower the ratio, the greater the financial risk. The amount of risk considered acceptable for a company depends on the nature of the business and the skills of its management, the liquidity of the assets and speed of the asset conversion cycle, and the stability of revenues and cash flows. A higher Solvency ratio implies a lower default probability.

Pillar 4 - Debt Service

The debt service pillar considers the capability of a company to meet its financial obligations in the form of debt. It ties the debt obligation a company has to its earnings potential. 

Total Debt / EBITDA

The debt service pillar considers the capability of a company to meet its financial obligations. This ratio is defined as Total debt divided by Earnings before Interest, Taxes, Depreciation, and Amortization (EBITDA). Total debt comprises Loans + Noncurrent liabilities. It indicates the total debt run-off period by showing the number of years it would take to repay all of the company's interest-bearing debt from operating profit adjusted for Depreciation and Amortization. However, EBITDA should not, of course, be considered as cash available to pay off debt. A higher Debt service ratio implies a higher default probability. 

Interest Coverage Ratio

Interest coverage ratio is defined as Earnings before interest and taxes (EBIT) divided by interest expenses (Gross and Capitalized). It indicates the firm's ability to meet interest payments from earnings. A high ratio indicates that the borrower should have little difficulty in meeting the interest obligations of loans. This ratio also serves as an indicator of a firm's ability to service current debt and its capacity for taking on additional debt. A higher Interest coverage ratio implies a lower default probability.

Pillar 5 - Size

In general, the larger a company is, the less vulnerable the company is, as there is, usually, more diversification in turnover. Turnover is considered the best indicator of size. In general, turnover is related to vulnerability. The higher the turnover, the less vulnerable a company (generally) is.

Ratio Scoring and Mapping

While these financial ratios provide some very useful information regarding the current state of a company, it is difficult to assess them on a stand-alone basis. They are only useful in a credit rating determination if we can compare them to the same ratios for a group of peers. Ratio scoring deals with the process of translating the financials to a score that gives an indication of the relative creditworthiness of a company against its peers.

The ratios are assessed against a peer group of companies. This provides more discriminatory power during the calibration process and hence a better estimation of the risk that a company will default. Research has shown that there are two factors that are most fundamental when determining a comparable peer group. These two factors are industry type and size. The financial ratios tend to behave ‘most alike’ within these segmentations. The industry type is a good way to separate, for example, companies with a lot of tangible assets on their balance sheet (e.g. retail) versus companies with very few tangible assets (e.g. service based industries). The size reflects that larger companies are generally more robust and less likely to default in the short to medium term, as compared to smaller, less mature companies.

Since ratios tend to behave differently over different industries and sizes, the ratio value score has to be calibrated for each peer group segment.

When scoring a ratio, both the latest value and the long-term trend should be taken into account. The trend reflects whether a company’s financials are improving or deteriorating over time, which may be an indication of their long-term perspective. Hence, trends are also taken into account as a separate factor in the scoring function.

To arrive to a total score, a set of weights needs to be determined, which indicates the relative importance of the different components. This total score is then mapped to a ordinal rating scale, which usually runs from AAA (excellent creditworthiness) to D (defaulted) to indicate the creditworthiness. Note that at this stage, the rating only incorporates the quantitative factors. It will serve as a starting point to include the qualitative factors and the overrides.

"A sound credit rating model strikes a balance between quantitative and qualitative aspects. Relying too much on quantitative outcomes ignores valuable ‘unstructured’ information, whereas an expert judgement based approach ignores the value of empirical data, and their explanatory power."

Qualitative Factors

Qualitative factors are crucial to include in the model. They capture the ‘softer’ criteria underlying creditworthiness. They relate, among others, to the track record, management capabilities, accounting standards and access to capital of a company. These can be hard to capture in concrete criteria, and they will differ between different credit rating models.

Note that due to their qualitative nature, these factors will rely more on expert opinion and industry insights. Furthermore, some of these factors will affect larger companies more than smaller companies and vice versa. In larger companies, management structures are far more complex, track records will tend to be more extensive and access to capital is a more prominent consideration.

All factors are generally assigned an ordinal scoring scale and relative weights, to arrive at a total score for the qualitative part of the assessment.

A categorisation can be made between business analysis and behavioural analysis.

Business Analysis

Business analysis deals with all aspects of a company that relate to the way they operate in the markets. Some of the factors that can be included in a credit rating model are the following:

Years in Same Business

Companies that have operated in the same line of business for a prolonged period of time have increased credibility of staying around for the foreseeable future. Their business model is sound enough to generate stable financials.

Customer Risk

Customer risk is an assessment to what extent a company is dependent on one or a small group of customers for their turnover. A large customer taking its business to a competitor can have a significant impact on such a company.

Accounting Risk

The companies internal accounting standards are generally a good indicator of the quality of management and internal controls. Recent or frequent incidents, delayed annual reports and a lack of detail are clear red flags.

Track record with Corporate

This is mostly relevant for counterparties with whom a standing relationship exists. The track record of previous years is useful first hand experience to take into account when assessing the creditworthiness.

Continuity of Management

A company that has been under the same management for an extended period of time tends to reflect a stable company, with few internal struggles. Furthermore, this reflects a positive assessment of management by the shareholders.

Operating Activities Area

Companies operating on a global scale are generally more diversified and therefore less affected by most political and regulatory risks. This reflects well in their credit rating. Additionally, companies that serve a large market have a solid base that provides some security against adverse events.

Access to Capital

Access to capital is a crucial element of the qualitative assessment. Companies with a good access to the capital markets can raise debt and equity as needed. An actively traded stock, a public rating and frequent and recent debt issuances are all signals that a company has access to capital.

Behavioral Analysis

Behavioural analysis aims to incorporate prior behaviour of a company in the credit rating. A separation can be made between external and internal indicators

External indicators

External indicators are all information that can be acquired from external parties, relating to the behaviour of a company where it comes to honouring obligations. This could be a credit rapport from a credit rating agency, payment details from a bank, public news items, etcetera.

Internal Indicators

Internal indicators concern all prior interactions you have had with a company. This includes payment delay, litigation, breaches of financial covenants etcetera.

Override Framework

Many models allow for an override of the credit rating resulting from the prior analysis. This is a more discretionary step, which should be properly substantiated and documented. Overrides generally only allow for adjusting the credit rating with one notch upward, while downward adjustment can be more sizable.

Overrides can be made due to a variety of reasons, which is generally carefully separated in the model. Reasons for overrides generally include adjusting for country risk, industry adjustments, company specific risk and group support.

It should be noted that some overrides are mandated by governing bodies. As an example, the OECD prescribes the overrides to be applied based on a country risk mapping table, for the purpose of arm’s length pricing of intercompany contracts.

Combining all the factors and considerations mentioned in this article, applying weights and scoring functions and applying overrides, a final credit rating results.

Model Quality and Fit

The model quality determines whether the model is appropriate to be used in a practical setting. From a statistical modelling perspective, a lot of considerations can be made with regard to model quality, which are outside of the scope of this article, so we will stick to a high level consideration here.

The AUC (area under the ROC curve) metric is one of the most popular metrics to quantify the model fit (note this is not necessarily the same as the model quality, just as correlation does not equal causation). The AUC metric indicates, very simply put, the number of correct and incorrect predictions and plots them in a graph. The area under that graph then indicates the explanatory power of the model. A more extensive guide to the AUC metric can be found here.

Alternative Modelling Approaches

The model structure described above is one specific way to model credit ratings. While models may widely vary, most of these components would typically be included. During recent years, there has been an increase in the use of payment data, which is disclosed through the PSD2 regulation. This can provide a more up-to-date overview of the state of the company and can definitely be considered as an additional factor in the analysis. However, the main disadvantage of this approach is that it requires explicit approval from the counterparty to use the data, which makes it more challenging to apply on a portfolio basis.

Another approach is a purely machine learning based modelling approach. If applied well, this will give the best model in terms of the AUC (area under the curve) metric, which measures the explanatory power of the model. One major disadvantage of this approach, however, is that the interpretability of the resulting model is very limited. This is something that is generally not preferred by auditors and regulatory bodies as the primary model for creditworthiness. In practice, we see these models most often as challenger models, to benchmark the explanatory power of models based on economic rationale. They can serve to spot deficiencies in the explanatory power of existing models and trigger a re-assessment of the factors included in these models. In some cases, they may also be used to create additional comfort regarding the inclusion of some factors.

Furthermore, the degree to which the model depends on expert opinion is to a large extent dependent on the data available to the model developer. Most notably, the financials and historical default data of a representative group of companies is needed to properly fit the model to the empirical data. Since this data can be hard to come by, many credit rating models are based more on expert opinion than actual quantitative data. Our Corporate Credit Rating model was calibrated on a database containing the financials and default data of an extensive set of companies. This provides a solid quantitative basis for the model outcomes.

Closing Remarks

Model credit risk and credit ratings is a complex affair. Zanders provides advice, standardized and customizable models and software solutions to tackle these challenges. Do you want to learn more about credit rating modelling? Reach out for a free consultation. Looking for a tailor made and flexible solution to become IFRS 9 compliant, find out about our Condor Credit Risk Suite, the IFRS9 compliance solution.

The European Central Bank (ECB) recently completed another important step in the supervisory process to assess the management of climate-related and environmental (C&E) risks by European banks. On 2 November, they published the results of their thematic review on C&E risks performed earlier this year.

Do you have difficulties setting out your treasury technology requirements (both from a functional as well as IT perspective), or matching those to the latest capabilities of a modern Treasury Systems? Are you wishing for a technology selection process that is less cumbersome and time consuming? If you said ‘yes’ to any of these, then we might have the solution for you!

Introducing Zanders’ Treasury Technology Selection Service powered by Tech-Select, our proprietary cloud-based Treasury Technology Selection tool designed to give you an easier, quicker way to select a new treasury system that best supportis your specific needs, or to evaluate your current system against other alternatives. For over 30 years, Zanders has been helping organizations globally to select treasury management systems and other related treasury technology. As an independent and unbiased expert, we have built extensive experience into how best to assist our clients to select, implement and deploy the most appropriate solution for their technology needs – it is this insight that drove us to develop a system to modernize the commonly used RfP process.

Zanders Tech-Select is the driving force behind our Treasury Technology Selection Service and accelerates your treasury system selection project by addressing the traditional challenges faced by organizations when choosing treasury technology solutions and introduces several other benefits as follows:

• Provides a broad overview of the systems market, giving you access to a wider range of potential vendors that can provide systems supporting your needs, and the most up-to-date insights on their current capabilities and new product developments
• Allows you to easily identify and define your functional needs, short-list suitable vendors, and automatically create and issue your RfP, by providing access to our comprehensive library of functional and technical requirements alongside our curated database of vendors, system functionality and capabilities
• Facilitates and accelerates multi-user assessment and evaluation of vendor responses, by utilizing workflows for scoring, the ability to assess user scoring differences, and the opportunity to determine final scores for each vendor
• Helps you to understand key differentiators between services and functionality on offer from each vendor, using our pre-defined results analytics and drilldowns
• Helps to streamline and accelerate the RfP process from end-to-end within a single cloud-based platform, removing the need for multiple emails, spreadsheets or complex scoring models, and
• Reduces the resource and time investment required to engage with multiple vendors, freeing you to focus on key analysis and value-add activities

Let’s take a deeper look at how Zanders Tech-Select achieves this:

• Once you’ve chosen from Zanders Tech-Select’s comprehensive library of functional and technical requirements, these are matched to our curated database of system functionality and capabilities in order to present you with a shortlist of systems and vendors that are suited to your specific needs. You can then select which of those vendors you would like to invite for your RfP process, utilise our templates to create cover letters and other attachments, and specify deadlines for acknowledgement, Q&As and final responses.

• When you are ready to launch your RfP, you simply click the ‘Issue’ button to automatically generate RfP invite emails quickly and easily to all chosen vendors. Once they receive the invite, vendors can use Zanders Tech-Select to access all document attachments, view your requirements, ask any questions, and respond to the RfP. 

• Once vendors have completed submitting their RfP responses, multiple users from your treasury, finance, IT and procurement teams can use Zanders Tech-Select to independently compare each vendor’s responses to each requirement in a consolidated view, and can assign scores to each. When all users have completed their scoring, the system automatically consolidates their scores and provides you with the opportunity to review and moderate the final scores before moving forwards to analyze the results. 

• At the final stage, Zanders Tech-Select’s interactive results analytics provide you with several ways to view and drill down into your results. Simply hover over data points to see more detailed information, check or uncheck to include or exclude vendors, sections and subsections, or most importantly click on specific scores to drilldown and quickly understand the reasons behind any scoring differences between vendors.

Based on experience from our clients, Zanders’ Treasury Technology Selection Service powered by Tech-Select drastically improves the current RfP processes many corporates are using today.

Zanders is unique in offering a system selection solution that includes an extensive library of the most common technology requirements alongside pre-populated and curated responses from over 90% of the leading treasury systems currently on the market. The structured, end-to-end process helps to streamline and automate your selection project and is applicable to a wide variety of use cases, enabling a consistent approach to vendor selection based on objective and rigorous evaluation.

Zanders Tech-Select uniquely provides the most efficient, thorough and cost-effective digital selection service within the treasury space, with Zanders experts to guide you throughout. If you would like to find out more about how Zanders can help to streamline your next technology or service selection process, get in touch now.

Article updated in May 2024

With year-end approaching quickly it is a natural moment to look back, zoom-out a bit and look forward to the coming year(s). Last year was, for most treasurers, a bumpy road. We witnessed another year with uncertainty and volatility in the financial markets due to the residual effects of the pandemic; the impact of the War in Ukraine; and rising inflation, interest rates and commodity prices. In this context, Corporate Treasuries are continuously facing internal and external challenges, and therefore need to operate in an increasingly complex and uncertain environment. Based on these challenges, we have identified four main Treasury themes for the year 2023 which are interlinked with each other and will be addressed in more detail in this article:

On one hand, the trend towards ERP system consolidation has progressed, most companies have centralized all their global entities and processes in a single environment with unique master data. At the same time, the trend towards cloud solutions has brought innovation and increased competition also to the TMS area. In this context, we are often asked: should the treasury function follow into the centralized ERP solution as well, or does a separate TMS instance have its justification?

Nowadays, all established TMS solutions are also available in the cloud or have disappeared from the market. The cloud technology brings disruption to the TMS market – high scalability of the cloud solutions has brought a number of new players to the global market – originally regional leaders seek to expand their footprint in other regions, some of them challenging the traditional market leaders. Established cloud TMS providers expand their functional covering in a very high pace and at relatively low marginal costs, which leads to increased competition in both functional capabilities and pricing.

In the segment of companies with more than USD 1 billion revenue, SAP occupies a special position among both ERP and TMS vendors (see numbers here) – it offers both a leading ERP system and a TMS with very broad functional coverage.

Benefit areas

In what areas can you find the benefits from integrating the TMS into ERP? We list them for you.

1. Process integration

Treasury is usually involved closely in the following three cross-functional processes, with the following implications:

(a) Cash management

Short- and mid-term cash forecast reflects accounts payable and accounts receivable, but purchase and sales orders can also be used to enrich the report. Even when commercial accounts are cash-pooled to treasury accounts, expected movements on these accounts can be used to plan cash pool movements on treasury accounts.

(b) Payments

Various levels of integration can be found among the companies. While some leave execution of commercial payments locally, others implement payments factories to warehouse all types of payments centrally. Screening against payment frauds and sanction breach is facilitated with centralized payment flow. Centralized, well-maintained bank master used at payment origination helps to avoid the risk of declined payments.

(c) Exposure hedging

FX and commodity hedging activities are usually centralized with the treasury team.

In case FX or commodity exposure arises from individual purchase and sales orders or contracts, the treasury team needs tools to be directly involved in the exposure identification and hedge effectiveness controlling.

2. System integration

Streamlined processes described above have implications for the required system integration. Depending on frequency and criticality of the data flows, different integration approaches can be considered.

All TMS on the market support file integration with the ERP systems in a reasonable way. They are also often very strong in supporting API integration with trading platforms and bank connectivity providers, but usually lag behind in API integration with the ERPs. Claims of available API integration need to be always critically reviewed on their maturity. API integration is preferred.

What are the benefits of API-based integration?

• It offers real-time process integration between the systems and empowers treasury to be tightly integrated in the cross-functional processes. It can be important when intraday cash positions are followed, payments are executed daily or exposure needs to be hedged continuously.
• It keeps the data flow responsibility with the users. Interfaces often fail because of master data are not aligned between the participating systems. If message cannot be delivered because of this reason, user receives the error message immediately and can initiate corrective actions. On the other hand, file-based interfaces are usually monitored by IT and errors resolution needs to be coordinated with multiple parties, requiring more effort and time.

Figure 1 Usual data flow between ERP and TMS

3. Parallel maintenance of master data

Integration flows require harmonization decisions in the area of master data. Financial master data (G/L accounts, cost centers, cost elements) are involved as well as bank master data and payment beneficiaries (vendors). TMS systems rarely offer an interface to automatically synchronize the master data with the ERP system – dual manual maintenance is assumed. Using one of the systems as leading system for the respective master data (e.g. bank masters) is usually not an available option.

4. Redundant capabilities

Both the ERP and TMS systems need to cover certain functionalities for different purpose. ERP to support payments and cash management in purchase-to-pay and order-to-cash, while TMS supports the same in treasury process. Both systems need to cover creation of payment formats, reconciliation of electronic bank statement, update of cash position for their area. These functionalities need to be maintained in parallel, or specific solution defined, to avoid the redundancy.

Conclusion

So, should the treasury function follow into the centralized ERP solution as well, or does a separate TMS instance have its justification? The answer to this question depends strongly on your business model and the degree of functional centralization. If functional centralization and streamlined business operations across your global entities is important in your business, you are probably already very advanced in ERP centralization as well. You can expect benefits from integrating the treasury function into such system setup.

On the other hand, if the above does not apply, you may benefit from the rich choice of various TMS solutions available at competitive prices to fit your specific needs.

Every business runs differently. We are happy to support you in your specific situation.

Payment fraud originating from within or outside an organization must be guarded against. Read on to learn how SAP Advanced Payment Management (APM) and Business Integrity Screening (BIS) can help.

Compliance requirements, audit needs and external factors like embargos, sanctions imposed by governments, and so on are additional imperatives for corporates and financial institutions (FIs) that want to secure their end-to-end payment lifecycle. Protection must be provided from the time of triggering a payment – or even before – until payment reaches the intended recipient.

In an increasingly digitized world with multiple cybersecurity threats, it is becoming even more important that the payment process is robust and ably supported by a strong technology infrastructure that provides security, speed, and efficiency.

Challenge for Corporates

The reality for many corporates is that they have multiple enterprise resource planning (ERP) systems – SAP or from other vendors – implemented over a period of time, or they have multiple systems due to merger and acquisition (M&A) activity in the past. Such corporates end up having lots of multi-banking relationships with different processes across the entire company, and different systems or banking portals for making payments. In an ideal world, moving to a single system with focused banking relationships, centralized treasury management and harmonized global processes is the end game that every company wants to achieve. But the process to get there is long.

SAP S/4 HANA

Companies using, or moving, to SAP as their primary ERP often aim for a single instance of the S/4 HANA landscape to get a single version of truth, but they may approach it in different ways. The journey most often is long and complex. But payment risk has to be mitigated sooner, rather than later.

Companies can adopt different strategies like ‘Central Finance’, ‘Treasury First’ or centralization of payments through a Payment Factory (PF) solution to enable certain quicker wins and security for the treasury and finance organization.

Advanced Payment Management Functionality

SAP introduced APM in 2019 to help payment centralization, visibility and oversight for those using its systems. APM alongside In-House Bank is its payment factory solution. SAP has continuously upgraded it since, with appropriate functionalities including anti-fraud measures available to users now.

APM allows for centralization of payments originating from any system – be it SAP or non-SAP – and facilitates:

• Data enrichment,
• Data validations,
• Conversions to bank specific file formats where needed,
• Batching, along with adding an approval mechanism by integrating with SAP’s Bank
• Communication Management option and by using a secured single channel of communication to all banks like SAP’s Multi-Bank Connectivity.

These measures enable treasury to have central and near-real time visibility of all payments going out, allowing corporate treasurers to put controls and checks in place through a robust payment approval mechanism.

Having a strong and auditable payment approval process governed by a unified system will enable reductions in payment fraud. However, payment approval alone is somewhat of a reactive mechanism and relies on a human touch that can sometimes become time consuming, labor-intensive and prone to errors, which can potentially miss some transactions when done on a large scale. A more advanced way of managing payment risk efficiently is through an exception-based procedure, where only absolutely required payments go through a human touch, with low-risk transactions filtered through an automated rules engine that allows for targeted attention on high-risk payments.

The need for Business Integrity Screening

SAP Business Integrity Screening (BIS) is a solution that complements the payment engine of S/4 HANA, including the advanced payment management (APM) function. BIS is a SAP solution that can be enabled on S/4 HANA. At a high level, it is a rules-based engine designed to detect anomalies and third-party risk. It uses data to predict and prevent future occurrences of fraud risk.

By virtue of being on S/4 HANA, BIS handles large volumes of payments, processing through real-time simulations. SAP BIS also integrates with different process areas like master data management, invoice processing, payment execution (payment runs), and with APM for payments originating from other systems. This helps fraud prevention at a much earlier stage.

The below Figure 1 picture depicts a few of the features of BIS where a set of rules can be defined for different scenarios with certain SAP provided out-of-box rules – for example, identified risk factors might include:

• Supplier invoice and payment execution stages, like vendor invoices or banks accounts in high-risk countries,
• One-time vendors,
• Payments made too early,
• Changes to vendor banking details just before a payment cycle,
• Duplicate invoices,
• Manual payments, and so on.

Figure 1: SAP’s Business Integrity Screening (BIS) Key Features

Source: SAP.

BIS has a highly flexible detection and screening strategy for business partners where new rules can be added and it can make composite rule scenarios, resulting in an overall risk score being awarded. For example, a weighted score may be determined based on individual Rules like:

• Payment value banding.
• Consecutive payments to the same beneficiary.
• Beneficiary address in an ‘at risk’ country.

Using the power of S/4 HANA, every payment is processed through all the rules and strategies defined to detect anomalies as early as possible, with real-time alert mechanisms providing further security. Implementations can leverage out-of-the-box rules and create new rules based on internal knowledge to refine anti-fraud measures going forward. BIS also has powerful analytics through the SAP Analytics Cloud solution for evaluating the performance of each strategy and rule, enabling refinements to be made.

BIS & APM Integration

For customers operating a single system environment, BIS was previously integrated with Payment Run functionality. With a multi-ERP Payment Factory landscape, BIS now integrates directly with APM. This means payments across the enterprise can be routed through screening for exception-based handling.

BIS combined with APM has two possibilities (as of writing this article):

• online screening for individual items,
• or batch screening for larger volumes of payments.

Rules can be set based on the size of payments as well – for example, Low value payments can be set for batch screening, while high value transactions can be set for online screening.

In the current release BIS 1.5 (FPS00), there are pre-defined scenarios specifically for APM. These check recipient bank accounts – for example, in high-risk countries and so on – and business partner (payee) bona fides for sanction screening/embargo checks at the payment order/payment item level. Custom scenarios can be created, and further custom code enhancements built within SAP-provided enhancement points.

While screening online, APM payment orders are validated through BIS detection rules. Payments without any anomalies or risk scores below threshold are automatically approved and processed for further normal processing through APM outbound processing. Payments which are suspicious will be ‘parked’ in BIS for user intervention to either release the payment – remembering, it could be a false positive scenario – or for blocking.

Any blocked payment in BIS automatically moves the APM payment order to the Exception Handling queue within Advanced Payment Management for further processing – for example, taking corrective actions in source systems, validating internal processes, contacting the vendor, cancelling/reversing a payment, and so on.

End-to-End Payment Fraud Prevention

There are different solutions available to cater to the specific needs of corporates across the payment lifecycle. A key first step is to centralize payments where Advanced Payment Management can help. A key benefit of Payment Centralization in a corporate landscape is the opportunity to initiate centralized payment screening and fraud prevention using BIS.

The integration between BIS and the APM Payment Factory enables effective payment fraud and sanction screening detection across the whole payment landscape. Adding Bank Communication Management for further approval control on an exceptions-basis will ensure a robust and automated payment process mechanism, with a strong focus on automated payment fraud prevention.

Once the payment process is secured, the next step is having secure connectivity to banks. This is where solutions like the SAP Multi-Bank Connectivity option can help.

If you are interested in any of the topics mentioned, or Sanction Screening & Fraud Detection more generally, we at Zanders encourage you to reach out to us via the ‘Get In Touch’ button. You can read more about Bank Connectivity Solutions & Advanced Payment Management: APM in our earlier articles.

Having to do this as part of a transaction where part of an existing business is being sold adds a major challenging element: time.

Time pressure together with the uncertainty of who will be the ultimate buyer (Financial Investor vs. Strategic Investor with an existing treasury function) requires many key decisions to be made under uncertainty. One of these key decisions is the one about the Treasury Management System (TMS) and the corresponding functionalities that have to be available on the closing day of the transaction. This decision has two components: costs and time to go-live.

Considerations

Technical developments in recent years have led to a situation where basic functionalities can be provided in-time by deploying a standard TMS at a cost not higher than any individual spreadsheet-based solution provided by consultants. Therefore, it has become a standard that an integrated TMS is a must-have for Day-1. It provides cash visibility and forecasting, captures both internal and external financial transactions and valuates them for period-end closing, therefore a TMS is the main Treasury Enabler.

With the time remaining until closing usually being quite short (often not more than 3-6 months) the question regarding the long-term strategic fit however has been one of minor relevance. This was and still is the case when the ERP-System is SAP-based. Until now, SAP treasury and risk management and cash and liquidity management components have not been part of the shortlist for transaction related TMS requirements. Three major (valid) arguments have been the reason for this:

• Cost,
• Complexity and therefore the longer duration time for an SAP implementation
• & Usability, especially by treasury staff that are not necessarily familiar with SAP logic.

But every major technical development requires us to question existing narratives and ask whether certain arguments are still valid.

Making the Right Strategic Choice
Usually, SAP has been dropped from the discussion when an implementation time constraint of between 3-6 months exists. The same applies if the future ERP landscape hasn’t been decided upon. This uncertainty often leads to the decision to opt against SAP. Let’s start with this element. A feasible solution to overcome this uncertainty is that of a Treasury Sidecar, i.e., implement a separate SAP instance with the SAP Treasury components. This allows connection to the existing (SAP) landscape, as well as providing the option of a future migration into a single S/4HANA environment and mapping treasury to the new General Ledger. This means that there should be no strategic or technical reason not to consider SAP.

We also looked at the latest SAP Treasury developments regarding the cloud-based SAP S/4HANA Treasury components from a time perspective. We specifically looked at the Private and Public Cloud Solutions from two angles: What is the minimum implementation time considering that SAP Best Practices are applied and secondly what are the consequences when looking at the integration into either an existing SAP ERP Central Component (SAP ECC) environment or a future S/4HANA platform? What our evaluation revealed is quite astonishing.

Public or Private Cloud?
When looking at the tight timeline argument against SAP we should examine the two main alternatives: the public cloud solution and the private cloud alternative. The private cloud can be considered similar to an on-premise installation, with the main difference being that the private cloud solution can be hosted by SAP or built within in your own data center. A private cloud could also use one of the Infrastructure-as-a-Service (IaaS) providers, such as AWS, Azure, GCP, or Alibaba Cloud.

These alternatives have the advantage of a fixed timeline (3-6 Months) for their implementation based on SAP’s Best Practices approach, together with SAP’s Activate. A requirements gathering is still the starting point to call out what is included in the first phase during its 3 months of implementation. This phase delivers a comfortable solution based on an 80/20 approach and covers what is usually required on Day-1.

Pros & Cons of the Cloud Options
A typical question is: what can’t we do if we opt for either the public or the private cloud? The answer is quite simple: The public cloud is a common environment with restrictions on building what is not already there – i.e. a gap identified is a real gap (and remains so). The private cloud is much like an on-premise solution with certain variations – like the SAP hosted option – where additional restrictions apply in terms of how far you can go about extending functionality.

Building a treasury side car and applying SAP Best Practice and the SAP Activate methodology, and following them closely, leads to similar implementation times for both public and private cloud (or on-premise) options, given that the same functionalities are to be implemented. This means, that implementation duration is not a major decision point. This leaves one key decision element: the question is, what type of flexibility & functional requirements – beyond what’s available in the public cloud solution – are required in the future?

The answer to this may lead to adopting an implementation approach very much like public cloud but in a private cloud (or on-premise) environment in order to get the flexibility needed to extend and expand for the future.

So yes, there is a new kid on the block, and it has to be taken seriously.