In a recent report by the Association of Certified Fraud Examiners (ACFE), Occupational Fraud 2022: A report to the nations, it is estimated that organizations lose about 5% of their revenue each year due to employees committing fraud against their employer. It is estimated that more than USD 4.7 trillion is lost worldwide to occupational fraud.  Of these, most cases were identified through a tip to a hotline and most were not detected until 12 to 18 months later. The longer the fraud was undetected, the higher the loss. But organizations do not only fight fraud internally; external threats are also on the rise. As businesses evolve and processes are automated and digitalized, fraud activities become much more complex.

Data and modeling approach to fraud prevention

To effectively prevent fraud, it first needs to be identified. Traditionally, employees are trained to identify anomalies or inconsistencies in their daily work environment. It is still crucial that your employees know what to look for and how to spot suspicious activities. But due to the complexities and vast amounts of information available, and because fraudsters are becoming more sophisticated, it becomes much more difficult to determine whether a potential customer is a fraudster or a real client.

The good news is that digitalization and increased data availability provides the opportunity for data analytics. It is important to note here that it does not completely replace your current processes; it should be used in addition to your traditional prevention and/or detection methods to be more effective to proactively identify and prevent fraud in your organization.

Benefits of data analytics

Traditionally, sampling was done on a population to test for fraud instances, but this may not be as effective because it only looks at a small population. Because fraud numbers reported usually being small (but with a large monetary impact), it is possible to overlook valuable insights if only samples of populations are investigated. Ideally, all data should be included to identify trends and potential fraudulent activities, and with data analytics that is possible. By analyzing large amounts of data, organizations can identify patterns and trends that may indicate fraudulent activity. It can help to improve the accuracy of fraud detection systems, as they can be trained to recognize these patterns.

Data analytics can increase efficiency by reducing false positives and false negatives and assists organizations to automate parts of the fraud detection processes, which can save time and resources. This allows the business to focus on other important strategic objectives and tasks such as customer service and product development.

By using data analytics to identify and prevent fraudulent activity, organizations can help to protect their customers against financial losses and other harm. Customer trust and loyalty are built when organizations show they are serious about the welfare and safety of their customers.

Detecting and preventing fraud

Reality is that preventing fraud upfront or in an early stage is much more economical and beneficial than having to detect fraud after the fact, as investigations are time-consuming and the fraud is not always easy to proof in court. Moreover, by the time it is detected, a loss may have already been incurred. Using data analytics to identify fraudsters and fraudulent activities earlier, can protect the bottom line by reducing financial losses and improving the organization’s overall financial performance.

By using analytics to detect and prevent fraud, organizations can demonstrate to regulatory bodies that they are taking compliance seriously. Reporting suspicious transactions and activities to regulatory bodies is a key component of complying with anti-money laundering and counter-terrorism financing legislation, and analytics can assist with identifying these transactions and activities more effectively.

Data analytics can be used to prevent fraud at onboarding, detect it in the existing customer base, and to make your operational processes more efficient. More specifically, data analytics can be used and leveraged as follows:

• Identifying outlier trends and hidden patterns can highlight areas and/or transactions that are more vulnerable to fraud.
• Automating identification of exceptions removes manual intervention and makes the identification criteria more consistent.
• Traditional physical reviews using limited resources to investigate is time-consuming. Data analytics can be used to prioritize the ones with the highest impact and risk, e.g. investigate the suspicious transactions with the highest value first.
• Combining data from different data sources to feed into a model provides a more holistic view of a customer or scenario than looking at individual transactions or applications in isolation.
• Both structured and unstructured data can be used to prevent and detect fraud.
• Fraud propensity model scoring can run automatically and generate results to be reviewed and investigated in real-time or near real-time.
• Analyzing relationships between various entities and customers using Social Network Analysis (SNA). Traditionally, networks/links were identified by the investigator while building a case. By using analytics, less time is needed to identify these relationships. Also, it identifies valuable links previously unknown, as additional levels of relationships can be examined.
• Specific modus operandi identified by the organization’s internal fraud team can be translated into data models to automate identification of similar cases. (See Case study below)
• Applying a fraud model to the organization’s bad debt book can assist with your collections strategy. Fraudsters never intended to pay and focusing your collection efforts on them wastes time and valuable resources. Most efforts should be on those cases where money can be collected.

Case study

The Zanders data analytics team has experience with applying data analytics within a company to identify customers who create synthetic profiles at point of application. By working closely with investigators, a model was developed in which one out of every three applications referred for investigation was classified as fraudulent.

The benefit of introducing analytics was twofold – from an onboarding- and existing customer point of view. The number of fraudsters identified before onboarding increased, preventing (potential) losses. Using the positive identified frauds at point of application, and checking the profile against the existing book, helped to identify areas that were more vulnerable where investigation should be prioritized.

The project proved that:

• Data analytics is valuable and combining it with insights from the operational teams is powerful.
• The buy-in from the stakeholders made the model more effective. If the team investigating the alert does not trust the model or does not know what to look for, there will be resistance in investigating the alerts.
• Taking your internal fraud team on a data and analytics journey is a must. They need to understand the impact that their decisions and captured outcomes can have on future models.
• Challenges with false positives (within business appetite and investigation capacity) are a reality, but having a model is better than searching for a needle in a haystack. Learning from the results and outcomes of the investigations, even if they were false positives, will enhance your next model.
• One size does not fit all. Fraud models need to be tailored to the business’ needs.

Conclusion

While using data analytics to identify fraudulent activities is an investment, organizations need to outweigh the benefit of incorporating data analytics in their current processes against the potential losses. Fraud not only results in monetary losses, it can lead to reputational damage and have an impact on the organization’s market share as customers will not do business with an organization where they don’t feel protected. Your customers also expect great customer service and implementing proactive fraud prevention measures increases confidence in your organization.

How can Zanders help your organization?

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After briefly presenting the DP, this article reviews the current Pillar 1 Capital (P1C) requirements. We limit ourselves to the P1C requirements for credit risk as this is by far the largest risk type for banks. Furthermore, we only discuss the interaction of the P1C with climate change risks (as opposed to broader environmental and/or social risk types). After establishing the extent to which the prudential framework takes climate change risks into account, possible amendments to the framework will be considered.

Key take-aways of this article:

• The current prudential framework includes several mechanisms that allow the reflection of climate change risks into the P1C.
• The interaction between P1C and climate change risks is limited to specific parts of the portfolio, and in those cases, it remains to be seen to what extent this is properly accounted for at the moment.
• Amendments to the prudential framework can be considered, but it is important to avoid double counting issues and to take into account differences in time horizons.
• The EBA is expected to publish a final report on the prudential treatment of environmental risks in the first half of this year.
• Financial institutions that are using the internal ratings-based approach are advised to start with the incorporation of climate change risks into PD and LGD models.

EBA’s Discussion Paper

In the introduction of the DP, the EBA mentions the increasing environmental risks – and their interaction with the traditional risk types – as the trigger for the review of the prudential framework. One of the main concerns is whether the current framework is sufficiently capturing the impact of transition risks and the more frequent and severe physical risks expected in the coming decades. In this context, they stress the special characteristics of environmental risks: compared to the traditional risk types, environmental risks tend to have a “multidimensional, non-linear, uncertain and forward-looking nature.”

The EBA also explains that the P1C requirements are not intended to cover all risks a financial institution is exposed to. The P1C represents a baseline capital requirement that is complemented by the Pillar 2 Capital requirement, which is more reflective of a financial institution’s specific business model and risks. Still, it is warranted to assess whether environmental risks are appropriately reflected in the P1C requirements, especially if these lead to systemic risks.

Even though the DP raises more questions than it provides answers, some starting points for the discussion are introduced. One is that the EBA takes a risk-based approach. Their standpoint is that changes to the prudential framework should reflect actual risk differentials compared to other risk types and that it should not be a tool to (unjustly) incentivize the transition to a sustainable economy. The latter lies “in the remit of political authorities.”

The DP also discusses some challenges related to environmental risks. One example is the lack of high-quality, granular historical data, which is needed to support the calibration of the prudential framework. The EBA also mentions the mismatch in the time horizon for the prudential framework (i.e., a business cycle) and the time horizon over which the environmental risks will unfold (i.e., several decades). They wonder whether “the business cycle concepts and assumptions that are used in estimating risk weights and capital requirements are sufficient to capture the emergence of these risks.”

Finally, the EBA does not favor supporting and/or penalizing factors, i.e., the introduction of adjustments to the existing risk weights based on a (green) taxonomy-based classification of the exposures¹. They are right to argue that there is no direct relationship between an exposure’s sustainability profile and its credit risk. In addition, there is a risk of double counting if environmental risk drivers have already been reflected in the current prudential framework. Consequently, the EBA concludes that targeted amendments to the framework may be more appropriate. An example would be to ensure that environmental risks are properly included in external credit ratings and the credit risk models of financial institutions. We explain this in more detail in the following paragraphs.

Pillar 1 Capital requirements

The assessment to what extent climate change risks are properly captured in the current prudential framework requires at least a high-level understanding of the framework. Figure 1 presents a schematic overview of the P1C requirements.

The P1C (at the top of Figure 1) depends on the total amount of Risk-Weighted Assets (RWAs; on the row below)². RWAs are determined separately for each (traditional) risk type. As mentioned, we only focus on credit risk in this article. The RWAs for credit risk are approximately 80% of the average bank’s total RWAs³. Financial institutions can choose between two methodologies for determining their credit risk RWAs: the Standardized Approach (SA)⁴ and the internal ratings-based (IRB) approach⁵ . In Europe, on average 40% of the total RWAs for credit risk are based on the SA, while the rest is based on the IRB approach⁶ :

Figure 1 – Schematic overview of the P1C requirements and the interaction with climate change risks

Standardized Approach

In the SA, risk weights (RWs) are assigned to individual exposures, depending on their exposure class. About 50% of the RWAs for credit risk in the SA stem from the Corporates exposure class⁷. Generally speaking, there are three possible RW drivers: the RWAs depend on the external credit rating for the exposure, a fixed RW applies, or the RW depends on the Loan-to-Value⁸ (LtV) of the (real estate) exposure. The RW for an exposure to a sovereign bond for example, is either equal to 100% if no external credit rating is available (a fixed RW) or it ranges between 0% (for an AAA to AA-rated bond) and 150% (for a below B-rated bond).

Internal Ratings-Based Approach

Within the IRB approach, a distinction is made between Foundation IRB (F-IRB) and Advanced IRB (A-IRB). In both cases, a financial institution is allowed to use its internal models to determine the Probability of Default (PD) for the exposure. In the A-IRB approach, the financial institution in addition is allowed to use internal models to determine the Loss Given Default (LGD), Exposure at Default (EAD), and the Effective Maturity (M).

Interaction with climate change risks

The overview of the P1C requirements introduced in the previous section allows us to investigate the interaction between climate change risks and the P1C requirement. This is done separately for the SA and the IRB approach.

Standardized Approach

In the SA, there are two elements that allow for interaction between climate change risks and the resulting P1C. Climate change risks could be reflected in the P1C if the RW depends on an external credit rating, and this rating in turn properly accounts for climate change risks in the assessment of the counterparty’s creditworthiness (see 1 in Figure 1). The same holds if the RW depends on the LtV and in turn, the collateral valuation properly accounts for climate change risks (see 2 in Figure 1). This raises several concerns:

First, it can be questioned whether external credit ratings are properly capturing all climate change risks. In a report from the Network for Greening the Financial System (NGFS) [3], which was published at the same time as EBA’s DP, it is stated that credit rating agencies (CRAs) have so far not attempted to determine the credit impact of environmental risk factors (through back-testing for example). Also, the lack of high-quality historical data is mentioned as an explanation that statistical relationships between environmental risks and credit ratings have not been quantified. Further, a paper published by the ECB [4] concludes that, given the current level of disclosures, it is impossible for users of credit ratings to establish the magnitude of adjustments to the credit rating stemming from ESG-related risks. Nevertheless, they state that credit rating agencies “have made significant progress with their disclosures and methodologies around ESG in recent years.” The need for this is supported by academic research. An example is a study [5] from 2021 in which a correlation between credit default swap (CDS) spreads and ESG performance was demonstrated, and a study from 2020 [6] which demonstrated that high emitting companies have a shorter distance-to-default.

Secondly, the EBA has reported in the DP that less than 10% of the SA’s total RWAs is derived based on external credit ratings. This implies that a large share of the total RWAs is assigned a fixed RW. Obviously, in those cases there is no link between the P1C and the climate change risks involved in those exposures.

Finally, climate change risks only impact the P1C maintained for real estate exposures to the extent that these risks have been reflected in collateral valuations. Although climate change risks are priced in financial markets according to academic literature, many papers and institutions indicate that these risks are not (yet) fully reflected. In a survey held by Stroebel and Wurgler in 2021 [7], it is shown that a large majority of the respondents (consisting of finance academics, professionals and public sector regulators, among others) is of the opinion that climate change risks have insufficiently been priced in financial markets. A nice overview of this and related literature is presented in a publication from the Bank for International Settlements (BIS) [8]. The EBA DP itself lists some research papers in chapter 5.1 that indicate a relationship between a home’s sales price and its energy efficiency, or with the occurrence of physical risk events. It is unclear though if climate change risks are fully captured in the collateral valuations. For example, research is presented that information on flood risk is not priced into residential property prices. Recent research by ABN AMRO [9] also shows this.

Internal Ratings-Based Approach

In the IRB approach, financial institutions have more flexibility to include climate change risks in their internal models (see 3 in Figure 1). In the F-IRB approach this is limited to PD models, but in the A-IRB approach also LGD models can be adjusted.

A complicating factor is the forward-looking nature of climate change risks. In recent years, the competent authorities have pressured financial institutions to use historical data as much as possible in their model calibration and to back-test the performance of their models. As climate change risks will unfold over the next couple of decades, these are not (yet) reflected in historical data. To incorporate climate change risk, expert judgement would therefore be required. This has been discouraged over the past years (e.g., through the ECB’s Targeted Review of Internal Models (TRIM)) and it will probably trigger a discussion with the competent authorities. A possible deterioration of model performance (due to higher estimated risks compared to historically observations) is just one example that may attract attention.

Another complicating factor is that under the IRB approach, the PD of an obligor is estimated based on long-run average one-year default rates. While this may be an appropriate approach if there are no clear indications that the overall risk level will change, this does not hold if climate change risks increase in the future, and possibly increase systemic risks. By continuing to base a PD model on historical data only, especially for exposures with a time to maturity beyond a couple of years, the credit risk may be understated.

Are amendments to the prudential framework needed?

We have explained that there are several mechanisms in the prudential framework that allow environmental risks to be included in the P1C: the use of external credit ratings, the valuation of collateral, and the PD and LGD models used in the IRB approach. We have also seen, however, that it is questionable whether these mechanisms are fully effective. External credit ratings may not properly reflect all environmental risks and these risks may not be fully priced in on capital markets, leading to incorrect collateral values. Finally, a large share of the RWAs for credit risk depends on fixed RWs that are not (environmentally) risk-sensitive.

Consequently, it can be argued that amendments or enhancements to the prudential framework are needed. One must be careful, however, as the risk of double counting is just around the corner. Therefore, the following amendments or actions should be considered:

• Further research should be undertaken to investigate the relationship between climate change risk and the creditworthiness of counterparties. If there is more clarity on this relationship, it should also be assessed to what extent this relationship is sufficiently reflected in external ratings. Requiring more advanced disclosures from credit rating agencies could help to understand whether these risks are sufficiently captured in the prudential framework. One should be cautious to amend the ratings-based RWs in the SA, since credit rating agencies are continuously working on the inclusion of environmental risks into their credit assessments; there would be a real risk of double counting.
• The potential negative impact of climate change risks on collateral value should be further investigated. Financial institutions are already required by the ECB⁹ to consider environmental risks in their collateral valuations but this is not at a sufficient level yet. It will be important to consider the possibility of sudden value changes due to transition risks like shifting consumer sentiment or awareness.
• To improve the risk-sensitivity of the framework, a dependency on the carbon emissions of the counterparty could be introduced in the fixed RWs, possibly only for the most carbon-intensive sectors. It could be argued that there are other factors that have a more significant relationship with the default risk of a certain counterparty that could be included in the SA. Climate change risks, however, differ in the sense that they can lead to a systemic risk (as opposed to an idiosyncratic risk) that is currently not captured in the overall level of the RWs.
• In the SA, a distinction could be introduced based on the exposure’s time to maturity. For relatively short-term exposures, the current calibrations are probably fine. For longer-term exposures, however, the risks stemming from climate change may be underestimated as these are expected to increase over time.
• In the IRB approach, a reflection of climate change risk would require the regulator to allow for forward-looking expert judgment in the (re)calibration of PD and LGD models. Further guidance from the competent authorities on the potentially negative impact on model performance based on historical data would also be useful.

Conclusion

Based on the schematic overview of the P1C requirements and the (potential) interaction with climate change risks, we conclude that several mechanisms in the prudential framework allow for climate change risks to be incorporated into the P1C. At the same time, we conclude that this interaction is limited to specific parts of the portfolio, and that in those cases it remains to be seen to what extent this is properly accounted for. To remedy this, amendments to the prudential framework could be considered. It is important, however, to avoid double counting issues and to be mindful of time horizon differences.

It is expected that the EBA will publish a final report on the prudential treatment of environmental risks in the first half of this year. However, especially financial institutions that are using the IRB approach should not take a wait-and-see approach. Given the complexity of modeling climate change risks, it is prudent to start incorporating climate change risks into PD and LGD models sooner rather than later.

With Zanders’ extensive experience covering both credit risk modeling and climate change risk, we are well suited to support with this process. If you are looking for support, please reach out to us.

¹ Supporting factors are currently in place for SMEs and infrastructure projects, but the EBA advocated their removal.

² See RBC20.1 in the Basel Framework.

³ See for example the results from the EBA’s EU-wide transparency exercise. This is reflected in Figure 1 by the percentage in the grey link between P1C and RWAs for credit risk.

⁴ See CRE20 to CRE22 in the Basel Framework.

⁵ See CRE30 to CRE36 in the Basel Framework.

⁶ In the Netherlands, less than 20% of the total RWAs is based on the SA. See the EBA’s EU-wide transparency exercise for more information. The percentages in the grey link between ‘Risk-weighted assets’ and ‘Methodology’ in Figure 1 are based on the European average.

⁷ See the EBA’s Risk assessment of the European banking system [2]. The percentages in the grey link between ‘Standardized Approach’ and the ‘Exposure class’ in Figure 1 reflect the share of RWAs in the SA for each of the different exposure classes.

⁸ The LtV is defined as the ratio between the loan amount and the value of the property that serves as collateral.

⁹ See expectation 8.3 in the ECB’s Guide on climate-related and environmental risks.

References

1. The role of environmental risks in the prudential framework, European Banking Authority, Discussion Paper, 2 May 2022
2. Risk assessment of the European banking system, European Banking Authority, December 2022
3. Capturing risk differentials from climate-related risks, Network for Greening the Financial System, Progress Report, May 2022
4. Disclosure of climate change risk in credit ratings, European Central Bank, Occasional Paper Series, No. 303, September 2022
5. Pricing ESG risk in credit markets, Federated Hermes, March 2021
6. Climate change and credit risk, Capasso, Gianfrate, and Spinelli, Journal of Cleaner Production, Volume 266, September 2020
7. What do you think about climate finance?, Stroebel and Wurgler, Journal of Financial Economics, vol 142, no 2, November 2021
8. Pricing of climate risks in financial markets, Bank for International Settlements, Monetary and Economic Department, December 2022
9. Is flood risk already affecting house prices?, ABN AMRO, 11 February 2022
10. Guide on climate-related and environmental risks, European Central Bank, November 2020

Since the first sustainability-linked derivative was executed in 2019, market participants have entered into a variety of ESG-related derivatives and products. In this article we provide you with an overview of the different ESG derivatives. We will touch upon the regulatory and valuation implications of this relatively new derivative class in a subsequent article, which will be published later this year.

Types of ESG-related derivatives products

Driven by regulatory pressure and public scrutiny, corporates have been increasingly looking for ways to manage their sustainability footprint. As a result of a blooming ESG funding market, the role of derivatives to help meet sustainability goals has grown. ESG-related derivatives cover a broad spectrum of derivative products such as forwards, futures and swaps. Five types (see figure 1) of derivatives related to ESG can be identified; of which three are currently deemed most relevant from an ESG perspective.

The first category consists of traditional derivatives such as interest rate swaps or cross currency swaps that are linked to a sustainable funding instrument. The derivative as such does not contain a sustainability element.

Sustainability-linked derivatives

Sustainability-linked derivatives are agreements between two counterparties (let’s assume a bank and a corporate) which contain a commitment of the corporate counterparty to achieve specific sustainability performance targets. When the sustainability performance targets are met by the corporate during the lifetime of the derivative, a discount is applied by the bank to the hedging instrument. When the targets are not met, a premium is added. Usually, banks invest the premium they receive in sustainable projects or investments. Sustainability-linked derivative transactions are highly customizable and use tailor-made KPIs to determine sustainability goals. Sustainability-linked derivatives provide market participants with a financial incentive to improve their ESG performance. An example is Enel’s sustainability-linked cross currency swap, which was executed in July 2021 to hedge their USD/EUR exchange rate and interest rate exposures.

Emission trading derivatives

Other ESG-related derivatives support meeting sustainable business models and consist of trading carbon offsets, emission trading derivatives, and renewable energy and renewable fuels derivatives, amongst others. Contrary to sustainability-linked derivatives, the use of proceeds of ESG-related derivatives are allocated to specific ESG-related purposes. For example, emissions trading is a market-based approach to reduce pollution by setting a (geographical) limit on the amount of greenhouse gases that can be emitted. It consists of a limit or cap on pollution and tradable instruments that authorize holders to emit a specific quantity of the respective greenhouse gas. Market participants can trade derivatives based on emission allowances on exchanges or OTC markets as spots, forwards, futures and option contracts. The market consists of mandatory compliance schemes and voluntary emission reduction programs.

Renewable energy and fuel derivatives

Another type of ESG-related derivatives are renewable energy and renewable fuel hedging transactions, which are a valuable tool for market participants to hedge risks associated with fluctuations in renewable energy production. These ESG-related credit derivatives encourage more capital to be contributed to renewable energy projects. Examples are Power Purchase Agreements (PPAs), Renewable Energy Certificate (REC) futures, wind index futures and low carbon fuel standard futures.

ESG related credit derivatives

ESG-related CDS products can be used to manage the credit risk of a counterparty when financial results may be impacted by climate change or, more indirectly, if results are affected due to substitution of a specific product/service. An example of this could be in the airline industry where short-haul flights may be replaced by train travel. Popularity of ESG-related CDS products will probably increase with the rising perception that companies with high ESG ratings exhibit low credit risk.

Catastrophe and weather derivatives

Catastrophe and weather derivatives are insurance-like products as well. Both markets have existed for several decades and are used to hedge exposures to weather or natural disasters. Catastrophe derivatives are financial instruments that allow for transferral of natural disaster risk between market participants. These derivatives are traded on OTC markets and enable protection from enormous potential losses following from natural disasters such as earthquakes to be obtained. The World Bank has designed catastrophe swaps that support the transfer of risks related to natural disasters by emerging countries to capital markets. An example if this is the swap issued for the Philippines in 2017. Weather derivatives are financial instruments that derive their value from weather-related factors such as temperature and wind. There derivatives are used to mitigate risks associated with adverse or unexpected weather conditions and are most commonly used in the food and agriculture industry.

What’s old, what’s new and what’s next?

ESG-related credit derivatives would be best applied by organizations with credit exposures to certain industries and financial institutions. Despite the link to an environmental element, we do not consider catastrophe bonds and weather derivatives as a sustainability-linked derivative. Neither is it an innovative, new product that is applicable to corporates in various sectors.

Truly innovative products are sustainability-linked derivatives, voluntary emissions trading and renewable energy and fuel derivatives. These products strengthen a corporate’s commitment to meet sustainability targets or support investments in sustainable initiatives. A lack of sustainability regulation for derivatives raises the question to what extent these innovative products are sustainable on their own? An explicit incentive for financial institutions to execute ESG-related derivatives, such as a capital relief, is currently absent. This implies that any price advantage will be driven by supply and demand.

Corporate Treasury should ensure they consider the implications of using ESG-related derivatives that affect the cashflows of derivatives transactions. Examples of possible regulatory obligations consist of valuation requirements, dispute resolution and reporting requirements. Since ESG-related derivatives and products are here to stay, Zanders recommends that corporate treasurers closely monitor the added value of specific instruments, as well as the regulatory, tax and accounting implications. Part II of this series, later in the year, will focus on the regulatory and valuation implications of this relatively new derivative class.

For more information on ESG issues, please contact Sander van Tol.