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Credit Risk

The dedicated space to converse with peers and our experts on all aspects of credit risk, from the technicalities of modelling using internal approaches, credit decisioning and underwriting, credit risk appetite, governance and monitoring, provisioning, and regulatory requirements

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  • Our dedicated space to discuss practicalities and technicalities of credit risk modelling using internal modelling approaches

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    As announced as part of the government’s Financial Services Growth and Competitiveness Strategy, the Prudential Regulation Authority (PRA) has introduced a more responsive approach for receiving, reviewing, and approving Internal Ratings Based (IRB) model applications . This new approach is designed to enhance the model approval process for banks with existing internal models. Key elements of the PRA’s updated approach include: Enhanced Pre-Application Engagement: PRA will work more closely with firms before formal submissions to assess readiness and flag complex issues early. Dedicated Submission Slots: Firms will have designated slots for application submission, increasing procedural clarity and predictability on both sides. Accelerated Documentation Quality Checks: The PRA aims to complete thorough checks on application documentation within 4 weeks. Defined Review Timelines: Complete submissions will undergo review within 6 months if no additional information is needed. Final Decision Targets: PRA targets concluding decisions on applications within 18 months. Implications for Banks This transparent and disciplined approach is welcomed by firms. However, it makes banks’ committed model submission dates more important than ever. Firms need to be confident that they will be able to deliver the model in a certain month (with a foresight of a year in advance), having gone through a robust governance and validation process. They will also need to ensure all parts of the submission are complete and of good quality. Failure to deliver on time or to the expected standard will risk putting them ‘at the back of the queue’, resulting in more costly re-developments and potentially supervisory add-ons. We see leading banks taking the opportunity to enhance their IRB model delivery and submission strategies. Conduct a Comprehensive End-to-End Stock-Take of IRB Submissions Across the board, we have observed the following best practices to fully review the current IRB model submission plans. This stock-take includes: Evaluate the feasibility and readiness of each submission relative to the PRA’s timelines and quality expectations. This is done in the light of both previous supervisory feedback and modelling challenges, to come to an honest assessment of whether a model can be delivered in a certain month. Integrate business and strategic priorities—focus should be placed on portfolios that align with the bank’s risk strategy and have the highest business impact. Evaluate levers to shorten delivery timelines – most banks now have elements of parallelization of different model development activities rather than a sequential ‘waterfall’ type approach Incorporate implementation readiness: given the PRA's more certain and shortened review timelines, banks should rigorously assess their ability to implement approved models within the required timeframe. Implementation timelines should be a critical dimension in deciding which models are "ready" for submission, ensuring that operational systems and infrastructures are aligned to support timely deployment post-approval. Enhance planning and regulatory engagement Our experience shows that the following three pillars are critical to ensuring a smooth, timely, and successful approval: Rigorous project management: the more formally and firmly committed timelines demand rigorous project management and discipline to meet deadlines. Late or rushed submissions significantly increase the risk of extensions and requests for additional information Avoid pitfalls from weak or incomplete documentation: all components of the submission package and in particular model documentation need to be planned from the outset to avoid gaps or quality issues that can jeopardise the model review proceeding as planned by ‘stopping the clock’ and having to re-submit Maximize the impact of pre-engagement meetings: the new pre-engagement meetings are an opportunity to present key elements of the model to the PRA end-to-end and provide specialists with the answers to key questions early on. In order to use this valuable time in the most impactful way, banks should prepare materials that directly address the PRA’s key areas of focus, including: Quality and depth of data and historical information used Key judgments and modelling assumptions Evidence of senior management involvement and ownership Thoroughness of internal model validation and challenge processes By preparing high-quality, thoughtful presentations, banks can avoid surprises during the review phase. How We Can Help We recognise that the evolving supervisory approach poses new challenges and have worked with our clients to address these: Ensuring high-quality, complete submissions that meet PRA expectations and pass documentation quality checks first time Providing targeted project support to help banks meet the PRA’s accelerated regulatory timelines without sacrificing rigor Assisting clients in strategically prioritizing IRB submissions to align with both regulatory readiness and broader business goals, maximizing impact and resource efficiency By partnering closely with our clients on these fronts, we help them transform regulatory requirements into competitive advantages and successfully navigate this evolving regulatory landscape.
  • Risk Rating Model Overrides: Supporting evidence

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  • Internal Risk Rating Development Questions

    cre c&i dod segmentation
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    The other C&I related criteria I have seen for segmentation (may or may not be captured by your size / sector view): Rated / quoted vs. not (on the basis these firms have access to additional sources of funding plus an extra source of predictive info, although that can be reflected by other mechanisms) Specialised lending vs not (Basel has some rules re: when should be viewed as specialised – to some extent it comes down to legal form) Legal form e.g. limited liability vs. partnership vs sole trader (at bottom end) Leverage finance / recent transaction e.g. divestiture, M&A … (on the basis that they are more sensitive to changes and historical performance data may be less relevant
  • IRB - Migration Matrix

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    Might also be worth taking a look at the detail in sections 2.5.5.1 - 2.5.5.2 dealing with customer migrations and migration matrix stability respectively, in the ECB's Instructions for reporting the validation results of internal models, pgs. 23 - 24
  • Wholesale Credit Risk PD Model

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    A number of UK banks have their wholesale PD models produce both “TTC” and “PIT” ratings – with the latter typically being driven by some sort of adjustment based on Moody’s KMV EDF’s or equivalents – you also have a number of other banks that for IFRS 9 will apply macro-economic model outputs on top of a largely TTC rating model to produce a PIT PD for IFRS 9 provisioning/ allowances purposes. The dual rating approach at big UK banks was heavily driven by a couple of modellers by the names of Scott Aguais and Lawrence Forest, and they have published a few papers describing their approach. On the question of splitting by “investment Grade” vs “non-investment grade”, I’ve never seen this, although a split between leveraged and non-leveraged is common historically (when we built the model for one of the large German banks, we were able to reintegrate them). But size r whether a customer is rated/ quoted/ listed is a common basis for segmentation in the commercial space. For EU banks, qualitative questions still tend to be included, although people are working to make them objective where possible, but not sure many have entirely removed them (again Scott Aguais was wanting to do this for one of the big UK banks – not sure whether he succeeded) – the issue with qualitatives is whether you can find the sweet-spot – at bottom end SME, often I don’t think credit officers have any real insights into their customers given how many they cover; at top-end, question is whether the credit officer has real insight beyond what can be captured by tools such as Factiva Sentiment Signals Historically, it has been difficult to remove size segmentation from the entire corporate customer base, in part because you tend to see some factors have different relationships for small vs large firms (e.g. you might want a fair bit of cash on balance sheet of an SME, but for a large firm, this would be inefficient and you’d perhaps worry if management wasn’t trying to make their cash work hard )
  • Are aeroplanes just ships of the sky?

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    Airplanes are ships of the sky from an LGD perspective, but not from a PD perspective. Railcars are also ships of the land by the same token From a PD perspective, they are generally separate. The maritime companies and airlines have very different obligor dynamics From an LGD perspective, the main drivers are downtime (i.e. how long the asset sits idle after a default) and shortfall (i.e. the decrease in the new lease after a default since usually periods of default coincides with pressure on asset prices and lease rates), but not the value on the plane. It is seldom that you’d actually lose the asset in any meaningful way, so that does not affect the LGD. There are international treaties that lessors would seek the jurisdiction of the lessee be a part of, before sending an expensive plane over with a long lifetime left on the asset. The other jurisdictions get the older planes where the lessor does not care all that much, whether they get it back or not To be clear, you’d parameterize the LGD model differently for different assets as the downtime and shortfall dynamics are different. But the model structure is the same. So, it becomes a bit of an optical choice on whether you call that a single model or not
  • Credit risk rating models for FIs - use of structural models

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    This may just be a rumour and so worth trying to Google to verify it, but I recall a long time ago that the MKMV model didn’t work well for banks, this was a known shortcoming of the model It’s not at all clear what other type of structural model (other than something like KMV) one could even attempt for a bank…
  • Using external model for PD modelling?

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  • Inclusion of COVID period for credit modelling

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  • Seasoning effects in IRB model development

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    Hi there, Based on previous experience, for PD this is often not relevant: PDs are 12-month and the seasoning tends to be generally captured by the scoring model itself. A qualitative explanation of each scoring model and which characteristics it is considering that relate to seasoning may be enough, especially if complemented with quantitative analyses on the seasoning effect. For a more quantitative approach, suggest testing time since origination and time until maturity as potential risk drivers using the general risk driver assessment framework during PD calibration - in the past I've observed this not to be significant but again, this is anecdotal evidence. On LGD it may be relevant. However it should be understood that seasoning actually correlates with other significant risk drivers, particularly LTV and outstanding exposure amount. Here a deeper analysis of these parameters' significance should help "paint the broader picture". Regards
  • PD Calibration - Applying Bayes theorem

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    A couple of thoughts on this subject, from one of our experts: The discrepancy is caused by the adjustment implicitly assuming that a Bank would have had more defaults and lower scores (and so a worse average score) – while applying the theorem to a population which still has the same set of defaulted cases. This means the average scores are not worse, and hence you predicted PD will be lower. There are at least two approaches to deal with this effect: Adjust the constant term in the logistic until it hits the 2% target Run a “goal seek analysis" so that the average PD after mapping scores to the Bank grades, and applying the appropriate post-rating adjustments so the PD reaches 2% Especially for European banks IRB models are actually required to be quite conservative unless Banks have "perfect" data, so the long-run average can become a moot point to a certain extent On the topic of perfect data: if the Bank has enough data and the PD model is really powerful, it should find that there is no straight-line relationship between PD from logistic model vs. observed default rate. This is actually caused by the fact that whilst the errors are broadly normally distributed in logOdds space, when the distribution is converted to PD/default rate space the expectation will be closer to the mean than the original prediction.
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