• Models built from scratch, duplicate efforts across teams working on different portfolios (e.g., Mortgages and Credit cards)

• Hard-coded and brittle model architecture makes it difficult to adopt to changing business and regulatory requirements

• Reliance on legacy tools (Excel, SAS) hinders building modelling pipelines and implementation speed

• Manual processes in data preparation, model documentation and validation increase error risk, reduce quality

However, banks which demanded more from their analytical teams have invested more into getting the programme right to get more out of it. The question they asked was "How will investment help us to develop analytics in an efficient and effective way in the future?". We have worked with UK and EU banks to develop effective approaches to build sustainable and integrated modelling capabilities that span the entire lifecycle

Figure 1: Summary of efficiency levers across the model development cycle

Three key focus areas of banks have been the following

1. New data operating model for the regulatory models
In the past, modelling and analytical teams were usually responsible for sourcing and preparing the data alongside their full-time modelling job. Given the scale of the regulatory requirements for data for IRB modelling (i.e., need to go back to 1990s downturn), this took significant time and effort from modelling teams. Additionally, modelling teams do not always possess data engineering skillset, which can lead to sub-optimal quality of the datasets produced making them not reusable going forward.

With the new data operating model we observe Data Engineers leading the data sourcing process and the new Data Development team acting as an intermediary between the two teams. This approach allows not only for better quality data which results in more robust models but also creates strategic data assets to ensure replicability of the process in the future and makes implementation of the models easier.

Figure 2: Revised way of working between business and data engineers

2. Development of a centralized toolkit
When working with the UK banks of IRB remediation programmes, we have brought in the centralized modelling and engineering teams that have created reusable modularized codebases. We have also worked with the internal validations teams to use this as an opportunity to centralize the 2nd LoD effort (from a model risk perspective) – for example, if certain modules of the codebase (e.g., linear regression, SFA, MFA) are centrally reviewed and approved by the internal validation team, this reduces the time required for review and validation of each individual model. This is resulting in significant gains in end-to-end model development timelines and avoiding the risk of low quality / inconsistent analytics code. Additionally, this centralized codebase can be deployed to a broad range of modelling and analytical projects beyond IRB work (e.g., IFRS9, Credit decisioning).

There are still challenges in maintaining this codebase as a continuous operating model. Industry should be open to thinking of novel ways to get this challenge right, that could bring in cost savings, the ability to embed new technologies (like AI) with less friction and better analytics outcomes (e.g., analytics utilities, centralized services).

3. Implementation of the models
As a result of the challenges described above, we observe model implementation costs increasing and the speed of adoption decreasing over recent years. However, developing strategic data assets (as described in 1) and centralized modular model codebase (as described in 2) makes model implementation and deployment faster and more cost-efficient. Additionally, as proven by our work with the UK banks, using these approaches allows the reduction of ongoing maintenance costs (e.g., Cloud storage and data querying costs) significantly.

Whilst we have SICR criteria and a rudimentary EWS, we're also looking for guidance how specifically in the risk report to display and explain:

• Besides overviews on the EWS, what to include into the regular reports
• Any focus on non-EWS metrics (and reasoning around why they are not in the EWS but still reported)
• Name level listings of flagged clients? If so, what are the thresholds on borrower size?
• For above - what are the follow-ups once in the report, i.e. what are the criteria so that it is not included in the next one (besides the EWS not triggering again on a flagged client)
• Any broader implications (escalation to credit risk mgmt. / board risk committees)?

We would be grateful for any inputs on the above, or any other pointers on best practices. Many thanks in advance!

The above would be used for indicative default correlations – anyways there would be expert judgement overlaid on top, so what we are looking for are any readily available numbers or similar analyses

For this, we would be looking for any pointers on figures available/ approaches for

• Correlation of default rates across industries
• Something roughly proxying above, e.g. sector equity correlations, EDF correlations

Many thanks in advance!

We're looking to improve the credit process for our SME segment to ultimately speed up time to yes and time to money while maintaining the same risk management rigour. One of the key aspects we are looking at is sequencing of credit and broader operational processes (e.g. fraud checks, site visits, KYC, name screening) to ensure that more operationally intensive processes or those requiring specialist teams (and therefore handoffs) are staged later to avoid wasted resources if a credit application is ultimately rejected. We would appreciate any input you might have on:

• Guiding principles for sequencing of the credit process from an efficiency perspective
• Examples of similar work looking at the efficiency of the credit process
• Any guidance from similar work on aspects of the credit process that use the most/ least resources
• Any analysis looking at aspects of the credit process that can be performed in branches without RM involvement

Thanks in advance!

A UK bank are in active discussions on what is covered by revolving loan commitments for PRA B3.1 statement below. This is within Wholesale exposure context.

Do you know if other banks are capturing Overdrafts under the revolving loan commitments? They internally classify overdrafts as unconditionally cancellable, thus there are people who argue that 166D.1.a (bolded below) will not capture these

Article 166D EXPOSURE VALUE FOR CORPORATES, INSTITUTIONS AND RETAIL: THE ADVANCED IRB APPROACH 1. An institution shall, subject to paragraph 3, determine the exposure value for off-balance sheet items in respect of which it uses the Advanced IRB Approach in accordance with Article 147A by multiplying the item’s nominal value by: (a) for revolving loan commitments which would not be subject to a 100% conversion factor under Credit Risk: Standardised Approach (CRR) Part Article 111: an own estimate of conversion factor that the institution shall provide in accordance with Section 6; (b) for all other off-balance sheet items: the conversion factor that would be applicable to the off balance sheet item under the Standardised Approach, as set out in Credit Risk: Standardised Approach (CRR) Part Article 111

Keen for views from what we have seen at different banks / any challenges from regulators

Thanks

It's against this backdrop that Oliver Wyman have conducted a Credit Risk Modelling survey of more than 20+ banks that provides a snapshot of

• Comparative views of Basel III implementation impacts across peers
• Benchmark the efficiency of credit risk modelling capabilities in terms of operational costs and RWAs
• Better understand opportunities and challenges in use of external / pooled data
• Gain insights on emerging industry best-practices in the integration of climate and environmental risks into the prudential framework
• Gauge the automation and AI maturity of your peers to better inform investment decisions

Get in touch for further insights, and post reactions, questions or comments with the RB community below

It would be beneficial to ascertain (i) the specific rates other banks are utilizing (e.g., cost of capital as they do for IRB) and (ii) their methodologies for justifying the use of a different rate to regulators and external auditors

Our client used to lend to a particular sector for many years – however 2-3 years ago they sold the portfolio.

Since then though, they have decided to re-enter the market and hope to develop an IRB model (they retain access to their own historical data).

Has anyone seen a similar case, and how long did the bank have to wait until they were able to achieve IRB compliance?

Bonus points for UK regulated bank answers

Ask for this group: Could you provide color on what additional supporting evidence should be uploaded to justify an underwriter’s decision to override a PD and/or LGD grade? How have you seen the required documentation vary between pre-selected override reasons and the “Other” bucket?

Appreciate your guidance on this topic.

PD or LGD?

Category

Description

Sub-category

PD

Change in interim financial performance

Override reason used in instances where:

• The prior year financials are believed to not be representative of the forward-looking cash flows (typical as evidenced by more recent performance)
• An alternative financial statement (e.g., rolling 12 months) is not used (as this will be captured outside of overrides)

Cash flow (+/-)

Liquidity (+/-)

Other balance sheet strength (+/-)

Other (Free text required)

Obligor specific non-financial factors not captured in the model

Override reason used for non-financial factors that were neither captured in the model nor as warning signals (i.e., prescriptive rules)

Change in management quality

Extraordinary event (e.g., lawsuit)

Performance relative to pre-existing benchmarks (e.g., construction timelines)

Other (Free text required)

Systemic risk events

Override reason used for downgrades or upgrades associated with non-obligor specific (e.g., industry, geography) risk factors that would not have been present in the prior year financials

Industry-specific demand fluctuation

Industry-specific supply fluctuation

Price fluctuations in key direct costs

Geographic risk

Operational risk

Other (Free text required)

Other

Used for all other override reasons, including strength or weakness in financial variables that were not included in the model (note – assuming robust models with testing of a wide variety of variables, this should be used sparingly)

Free text required

LGD

Unforeseen disruption to collateral value (typically market level)

Override attributed to external factors such as systemic risk events as well as changes in legal and regulatory environments

Deprioritized as part of this phase

Abnormal expected appreciation / depreciation due to borrower / facility characteristics

Override reflecting idiosyncratic factors with the specific attributes of a range of similar facility loans

Change in recoverability patterns

Override driven by either changes in the liquidity of the collateral or the timing associated with recoverability

Other

Override reason used for considerations that are not captured within the initial LGD, as part of the structural adjustments or other override reasons; requires details/justification to allow for approval and monitoring of usage***