Credit risk controlling
In turbulent economies, credit risk controlling is crucial for the successful management of a bank. The core functions of credit risk controlling include regulatory equity backing, economic risk-bearing capacity, balance sheet risk provisioning, non-performing loan and forbearance management, and the comprehensive asset allocation and limitation. These functions must be integrated into the overall bank management to provide a complete picture of credit risk. Currently, external influences are necessitating adjustments to Conditional Value at Risk (CVaR) management within banking institutions.
In dieser Collection enthalten:
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Bilanzoptimierung durch Verbriefung
Operational Excellence in Asset Management and Bank Treasury
Anwenderkonferenz Meldewesen & Risikomanagement - Rückblick auf eine erfolgreiche Veranstaltung
Operational Excellence im Asset Management und Banken Treasury: Flexible Workflows als Schlüssel zum Erfolg
Zinsbuchsteuerung mit langlaufenden Benchmarks – Auswirkungen der aktuellen Marktentwicklungen
Impuls “Risk & Regulatory Reporting” – 8. MaRisk-Novelle und CSRBB
8. MaRisk-Novelle – Neue Anforderungen im Risiko-Reporting
Impuls “Risk & Regulatory Reporting” – ESG im Risikomanagement
Interview mit Dr. Frank Schlottmann über die wichtigsten Einflussfaktoren auf die Bankenbranche
Rekalibrierung der IRRBB-Zinsschocks – BCBS hat das finale Papier veröffentlicht
CVaR management: A success factor in the banking industry in the light of current developments
With a recession looming, the issue of insolvency is increasingly becoming the focus of risk management and supervision. In turbulent economies, credit risk controlling is crucial for the successful management of a bank. The core functions of credit risk controlling include regulatory equity backing, economic risk-bearing capacity, balance sheet risk provisioning, non-performing loan and forbearance management, and comprehensive asset allocation and limitation. These functions must be integrated into the overall bank management to provide a complete picture of credit risk. Currently, external influences are necessitating adjustments to Conditional Value at Risk (CVaR) management within banking institutions.
In addition to the current economic developments, three key aspects necessitate the need for adjustment:
1. New regulatory requirements due to the ICAAP
According to the regulatory risk-bearing capacity guidelines, there is a change in credit risk controlling within economic risk-bearing capacity: the old-style ‘going-concern’ approaches will be discontinued as of 31 December 2022 and institutions will switch to the economic ICAAP. Traditional portfolio risk models for CVaR calculation, often based on period-oriented exposures, must now be adequately analyzed for their appropriateness in risk quantification. At a minimum, it is necessary to validate that these models, which rely on periodic variables, sufficiently approximate economic credit risk measurement. Depending on the size, complexity and risk content of their business activities, institutions may be required to operate a present value credit risk portfolio model, accounting for size and sector concentration effects. Additionally, the prevalent separation between customer and proprietary business in institutions must also be re-examined in terms of both concentration effects and the present value perspective.
2. Sustainability
Current developments in climate change and sustainability are shedding new light on credit risk. In the upcoming crisis management, credit risk management has a dual function: ensuring the institution’s risk-bearing capacity while supporting necessary investment and transition activities for individuals and companies. The ECB has emphasized climate risk stress tests this year as part of the EBA stress tests. While all classic risk types are considered within the scope of climate risks, this year’s ECB climate stress tests specifically focus on credit and realization risk, alongside operational risk. It is anticipated that climate risks will also be included in the upcoming LSI stress tests with a primary focus on credit risk.
In credit risk controlling, the observation period for climate risk stress tests and requirements will be extended. For institutions supervised by the ECB, forecast periods may extend up to 30 years. Scenario-based new business planning is crucial for longer-term credit risk forecasts and stress scenarios. Traditionally, new business planning has rarely been integrated into CVaR portfolio risk models, which have primarily been used for one-year economic risk-bearing capacity in accordance with the Single Supervisory Process. However, as climate-related transition risks become fundamental for banking activities, longer-term economic risk forecasts will be essential to manage significant shifts in business areas – for example transitioning from the oil/natural gas sector to wind power/photovoltaics, from lithium to hydrogen, from automotive to autonomous driving.
3. Change in IT requirements
Current developments in IT should also be reflected in the implementation of credit risk controlling. With BCBS 239, supervisory authorities initiated a movement towards central data warehouses in institutions, now supplemented by efficient, cloud-capable solutions with extended data analysis capabilities.
Combining data mining and AI methods with traditional mathematical risk controlling tools, such as simulations, regressions and cluster methods, along with user-friendly and easy-to-use counterparty risk content apps, increasingly enables risk controllers to perform complex stress test requirements and long-term forecasts. SaaS (Software-as-a-Service) environments provide cost-efficient operating concepts with automatic deployment on heterogeneous systems. Big data methods facilitate high-performance systems via document-orientated persistence.
Decoupling the architecture into different layers, such as the data lake layer, content app layer and presentation layer, allows for versatile use in different IT contexts. For example
- The isolated content app can be easily integrated into the institution’s IT architecture as best of breed.
- Configurations for scenarios and portfolios as well as process control, can be conveniently managed in the presentation layer.
- Results data can be integrated into central data warehouse architectures, making it available in such a way that specialized BI tools for aggregation, selection and visualization can be used efficiently, ensuring compliance with BCBS 239 and becoming an integral part of the overall risk report generation.
To provide flexible analysis using slice and dice logic, it is essential to calculate key figures on a single transaction basis for exposures, collateral values, creditworthiness premiums, expected losses, lifetime expected losses and CVaR risk contributions. These transaction-based key figures can be applied in economic asset allocation and limitation. Segment-based limitation, in accordance with MaRisk (BTR 1.1 and BTR 1.6) should be based on marginal, segment-based risk contributions and integrated into the risk report.
In asset allocation or business division management, identifying current and future attractive business segments from a risk/return perspective is crucial. Return on Risk-Adjusted Capital (RORAC), a central business parameter, depends on credit risk indicators, particularly in forecasting economic capital requirements. For present value RORAC, where the numerator reflects a contribution margin III after deducting liquidity costs, standard risk costs, standard unit costs, fixed costs, and equity costs, the denominator for the present value equity should show the progression of the share of unexpected loss. The challenge lies in calculation of the trend, as new business planning must also be taken into account for segment based RORAC forecasts.
In addition to traditional customer and proprietary transactions, derivatives, guarantees, ABS, and other items exposed to credit risk must also be taken into account when calculating key figures based on single transactions.
New ORRP Credit Risk Solution from msg for banking
We have responded to the current developments by adding address risk to the platform-based solution ORRP (Open Risk and Reporting Platform). The new platform architecture enables both the operation and utilization of existing interfaces to the predecessor software generation THINC, as well as the transition to a web-based SaaS architecture with fully document-based JSON interfaces. A standardized logical INPUT data architecture (ELIDA) allows the main risk controlling core components to operate with a standardized data supply for risk controlling and reporting.
An adapter for direct connection to SAP FSDP is currently being developed in collaboration with another well-known SAP co-operation partner. The ELIDA data interface serves as an open platform interface for the direct connection of operational systems or for the integration of a single point of truth data architecture.
The ORRP content apps currently available in credit risk include risk provisioning in accordance with HGB, the CVaR portfolio model for economic risk-bearing capacity and an RWA content app in accordance with CRR III. With the ORRP-CVaR portfolio model, proprietary business and customer business can be fully valued at present value, allowing for the presentation of an integrated economic ICAAP.
The ORRP credit risk solution can be used to map migration risk, realization risk and country risk in addition to counterparty default risk. The methods used to measure counterparty risk have been established in the market for many years and are based on the recognized credit portfolio model CreditMetricsTM, which msg for banking expanded years ago to include various illustrations to fulfil regulatory requirements. This credit portfolio model was also implemented in the predecessor module GCPM, which has been tried and tested in numerous institutions for around 20 years and has been regularly and successfully confirmed in regulatory audits.
The solution is multi-currency capable and maps a wide range of product types, such as fixed-interest customer business, current accounts, guarantees, and plain vanilla derivatives. It integrates a concentration risk analysis for groups of related borrowers directly in the simulation model with a correlation assumption in CreditMetricsTM.
The ORRP-CVaR model is a flexible service solution that can be integrated with our predecessor software generation and used with other credit risk controlling content apps. The content apps are service-orientated Java components designed for lean operation with a focus on robustness, maintainability, fault tolerance, and logging. Cloud capability with elastic utilization of cloud resources and configurable processing speed, both vertically and horizontally, is an integral part of the architecture. It can be operated on-premises in Java runtime environments or in the cloud in IaaS or SaaS environments. The software solution features a modern, web-based GUI and is ISO 27001 certified. The IT architecture supports common cross-sectional methods, such as calculating cash values, credit rating premium calculation, and the Expected Loss (EL) calculation. The underlying credit portfolio model is designed for present value measures and can be integrated with ORRP new business planning.
Conclusion
Due to the current regulatory developments regarding the abolition of the going-concern approach and the ongoing changes in overall bank management and economic processes, precise analysis of credit risk is essential. The flexible ORRP credit risk solution, with its future-proof, cloud-ready technology, supports institutions in creating the necessary evaluations and analysis in a technically sound and regulatory-compliant manner. It enables them to react to new regulatory requirements, (such as climate stress tests) with flexible stress scenarios, and to focus on the longer-term, forward-looking management of the portfolio.
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