Introduction to IRB


Introduction to IRB

IRB Insights

Internal Rating Based Approach (“IRB”) is well known concept in credit risk community for over 15 years. However, we observe significant changes in this playfield over the last years. On the one hand technological progress such as alternative data sources and modern modelling techniques make credit risk models estimates more precise. Increase of computing power and cloud solutions enable to identify more complex relationships and deploy models faster. On the other hand, recent economic (e.g., high inflation, increasing energy and food prices, hiking interest rates, COVID relief measures), social (e.g., ESG agenda), political (e.g., war in Ukraine, USA – China relationships) and business environment make it more difficult to draw conclusions based on historically observed patterns and trends. To make the picture even more complex, the IRB regulatory landscape has been reshaped substantially with European Banking Authority (“EBA”) IRB repairing program.

Within these series of articles, we will discuss selected topics related to IRB. Our aim is to navigate credit risk community through modified IRB playfield. We distilled topics into eight areas:

  1. Introduction to IRB. We start with formal definition of IRB taking into consideration different views (capital planner, credit risk modeler, Chief Risk Officer etc.) and discuss why banks want to implement IRB.
  2. Data representatives. We will explain how representativeness is understood in the IRB world and what dimensions need to be considered. We will also discuss the recent economic, social, political, and business environment in the contest of data representativeness. A special attention will be put on COVID period.
  3. Rating philosophy. We will analyse different rating philosophies and their implications on PD model development, calibration, and finally model use.
  4. Downturn concept. We will discuss how to identify, quantify, measure, and justify economic downturn for LGD model.
  5. Margin of conservatism (“MoC”). We will deep dive into objective of MoC, types and sources of deficiencies. We will also discuss role of MoC in the model lifecycle.
  6. Model use. We will discuss how rating systems can and should be incorporated into organization DNA to gain long term competitive advantage. We will also explain the concept of use test and experience test.
  7. Benchmarking exercise. We will discuss the aim, objective, scope, and expectations resulted from annual EBA exercise.
  8. Machine Learning for IRB models. We will discuss recent developments in application of Machine Learning methods in capital models. We will also deep dive into challenges posed by ML models, outline potential benefits stem from their application as well as main concerns raised by regulatory authorities.


Today we are taking on our agenda the first topic: Introduction to IRB.

The best and honest answer is “It depends on who you ask”. If you pose the question to an analyst from controlling and capital planning department, you will most likely hear that IRB is an “calculator” of risk weighted assets (“RWA”). He will also substantiate this statement with relevant regulatory extract, for example, Article 143 of CRR:

Where the conditions set out in this Chapter [Internal Ratings Based Approach] are met the competent authority shall permit institutions to calculate their risk-weighted exposure amounts using the Internal Ratings Based Approach (…)

If you ask the exact same question in the same organization but in credit risk department, you will get the answer that IRB means rating system. And again, such definition is backed by regulations. Article 144 of CRR states that:

The competent authority shall grant permission pursuant to Article 143 for an institution to use the IRB Approach, including to use own estimates of LGD and conversion factors, only if the competent authority is satisfied that requirements laid down in this Chapter [Internal Ratings Based Approach are met (…) , and that the systems of the institution for the management and rating of credit risk exposures are sound and implemented with integrity (…) 

Would it be possible that no one and precise definition of IRB exists? Both RWA calculator and rating systems are in scope of broader IRB concept. Given that, IRB can be defined as ecosystem of accompanying tools and systems that are directly or indirectly related to the credit risk management process.

While usually perceived as a method for capital optimization, IRB is a tool for building a long-term competitive advantage. Whilst different organization may prioritize them differently, there are five main reasons for IRB implementation.

1. Capital optimization.

Under well-known accounting formula funding costs compromise of cost of own funding (equity) and external one (debt and/or client deposits in case of bank) weighed by the share of each source.

For a banking institution this formula can be written as follow:

WACCB -Weighted Average Cost of Capital for banking institution
RW – Risk Weight
TCR – Total Capital Ratio
tax – Applicable tax rate
re – cost of equity
rd – cost of debt

IRB implementation would usually result in decreased Risk Weight (RW in formula) compared to Standardized Approach leading to decrease capital needs and overall cost of capital.


2. More accurate risk management

IRB models are calibrated to institution’s historically observed defaults and losses which directly links its risk profile to the level of regulatory and economic capital.

IRB models (being part of rating system) are developed following robust modelling process. They also adhere to strict documentation principles. Finally, they are subject to identification and quantification of all deficiencies, shortcuts or issues related to data, methodology, processes, and model nature (i.e., model is simplification of complex phenomena). Given that, they are simple “better” models and model users are aware of their potential weakness.

IRB requires to use rating systems in wide range of risk management processes in a consistent way.

All above results in better understanding of credit risk profile and more accurate risk management.


3. Improvement of processes and standards

IRB models are embedded into internal risk reporting and portfolio risk monitoring. It increases the risk awareness within organization and therefore supports robust internal governance.

IRB models are fed with wide range of high-quality data both internal and external ones (e.g., client-related, transaction related, collateral related, environment related etc.). Thus, well developed data quality framework and data-related processes are prerequisite for successful IRB implementation.
IRB models are used not only for risk management processes, but also in wide range of banking ones (e.g., underwriting, capital planning, strategy). Therefore, IRB implementation is a catalyst for risk and banking processes integration and alignment.


4. Building a modern organization

Banks compete for talent not only with their peers, but also fintech, payment integrators or internet and shopping marketplaces. Given that IRB implementation is very intellectually challenging project it can serve as a tool for acquisition and retaining of top talent.

More accurate risk management, incorporation of IRB models into different areas and their integration with other banking processes enables to build “the culture of risk and capital” which is the essence of modern bank.
IRB implementation steers bank to be “data driven” and “risk driven” organization.


5. Increase of stakeholders’ value

Shareholders gain higher return on equity and dividend yield. More accurate risk management and improvement of processes and standards would result in better financial performance and lower capital utilization.

Clients gain more precise risk quantification and “price” for risk.

IRB implementation is a strategic long-term complex project with significant impact beyond credit risk domain. Therefore, the implementation decision should be supported with comprehensive and unbiased assessment of organization’s IRB readiness. Such analysis should also provide information on how IRB program can be executed effectively, in line with predefined targets and limited project risk.

Best of art IRB feasibility project compromises of the following pillars:

  • Gap analysis in areas of credit risk models, model use, risk management processes and IT environment. It results in:
    • Diagnosis of fulfilment of IRB requirements (“AS IS”)
    • Identification of required actions (“TO BE”) and their prioritisation.
  • Profitability study taking into consideration cost benefit analysis:
    • Dynamic estimation of capital relief with consideration of potential IRB program schedules, results of regulatory assessment and anticipated changes in prudential regulations
    • Cost estimation of IRB adjustments under different realization scenarios.
  • Education in area of IRB impact on organisation, approval process and project risks.
  • Business case - preparation of implementation plan accompanied by detailed cost benefit estimates that support business decision.

Once the feasibility study is finalized and implementation decision is made, Bank should include IRB program into list of priority programs, prepare a detailed implementation including key milestones and dependencies. It should also decide on regulatory engagement and potential scope of external support.

To summarize, IRB implementation is a positive stimulus for organisation that helps building a long-term competitive advantage. It is also challenging and these challenges will be discussed in next articles.

Author: Piotr Czapski

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