Model Development Document Template (Credit and Market Risk Models)

Model Development Document: [Model name] [version]

Developer documentation for a quantitative risk model (for example, a probability of default model for the internal ratings-based approach, an IFRS 9 component, or a value-at-risk model): purpose and use, design choices, data, methodology, calibration, assumptions and limitations, developmental testing, implementation and the ongoing monitoring plan. It is written so that an independent validator can understand and reproduce the work, as expected by the US interagency Revised Guidance on Model Risk Management of 2026-04-17 (Federal Reserve SR 26-2, OCC Bulletin 2026-13, FDIC FIL-15-2026), PRA SS1/23 Principle 3 (cited by number and name), the ECB guide to internal models (revised 28 July 2025) and, for EU IRB models, Regulation (EU) No 575/2013 as amended (CRR) with EBA/GL/2017/16. Replace every [bracketed] value and every row marked Example. The worked example is the fictional Fernhollow Bank retail mortgage PD model. This is a document format, not legal, regulatory or risk advice. Obtain the guidance or regulation for the normative text.

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Revision history

Contents

• 1. Executive summary
• 2. Purpose, scope and intended use
• 3. Design choices and theory
• 4. Data
• 5. Methodology
• 6. Calibration
• 7. Developmental testing
• 8. Assumptions, expert judgement and limitations
• 9. Implementation
• 10. Ongoing monitoring plan
• 11. Regulatory requirements traceability
• 12. Adaptations for market risk models
• 13. Glossary and references
• Standard and licence note

1. Executive summary

[Summary in five to eight sentences.]

Example: This document describes version 3.0 of Fernhollow Bank's retail mortgage probability of default model (MOD-001), used for IRB own funds requirements, pricing and IFRS 9 staging inputs. Version 3.0 replaces a 2019 scorecard whose discrimination had declined and whose data did not reflect the 2022 to 2024 interest rate rises. The new model is a logistic regression scorecard on six risk drivers, mapped to an eight-grade master scale and calibrated to a long-run average default rate of 1.35% with a margin of conservatism of 0.15 percentage points. Out-of-time testing on 2023 to 2024 data gives a Gini coefficient of 0.62 and the 2025 confirmatory cohort 0.61, with no grade with a statistically significant calibration deviation at the 95% level. The main limitations are the short history of high-rate environments and thin data for buy-to-let loans, both addressed by conservatism and monitoring. The model is submitted for independent validation and, because it is a material change to an IRB model, for the supervisory approval process.

2. Purpose, scope and intended use

Describe how users were involved in setting the purpose, which questions they raised about the previous model, and how those questions shaped this version. SR 26-2 notes that input from users strengthens both model understanding and model quality. Record the meeting dates or sign-offs as evidence.

Example: Retail credit underwriting and pricing teams took part in three workshops in 2025. They reported that the old model ranked recently originated high loan-to-value loans too favourably. This led the team to test a loan-to-value variable updated with a house price index and an interaction with time on book.

3. Design choices and theory

State the economic or statistical theory that links the inputs to the output and the reason the chosen model class fits the data and the use. For a PD model, explain the rating philosophy (point-in-time, through-the-cycle or hybrid), because it determines how grades migrate over the cycle and how the model interacts with capital and IFRS 9.

Example rating philosophy: The scorecard ranks borrowers on mostly point-in-time drivers, but grade PDs are calibrated to a long-run average default rate. The result is a hybrid philosophy: borrowers migrate between grades as their circumstances change, while grade PDs stay stable across the cycle. The IFRS 9 team adds a forward-looking adjustment in the ECL Methodology (finance/ecl-methodology), not in this model.

4. Data

4.1 Sources and lineage

Describe the lineage from source to modelling dataset, including joins, keys and the extraction date. Reference the data dictionary entries (architecture/data-dictionary) for each variable used.

4.2 Default definition

State the default definition and how it is applied in the data. For EU IRB models, the definition must follow CRR Article 178 and the EBA Guidelines on the application of the definition of default (EBA/GL/2016/07, amended by EBA/GL/2026/05, published 2026-05-07, applicable from 2026-10-19; check the consolidated version). Describe how historical defaults recorded under earlier definitions were adjusted, and the effect of the adjustment on default rates.

Example: Defaults are identified when a loan is more than 90 days past due on a material amount or when the bank considers the borrower unlikely to pay, including distressed restructurings. Defaults before 2021 were re-derived from the arrears history under the current definition; this raised the 2014 to 2020 average default rate by 0.06 percentage points.

4.3 Sample construction and exclusions

The final modelling dataset holds 1,860,000 loan-year observations with 22,320 defaults, a pooled default rate of 1.20%. The development and out-of-time samples add up to that total (1,512,000 plus 348,000 observations; 18,450 plus 3,870 defaults). A separate 2025 cohort of 172,000 loans, which became available after development, is used for the confirmatory test in section 7.3.

4.4 Data quality

Report missing values, outliers, inconsistencies and how each was treated. Show the effect of treatment on the default rate where it is material. Record known data limitations here and carry them to section 8.

5. Methodology

5.1 Model form

The model estimates the probability of default over the next twelve months with a logistic regression:

where $x_{ik}$ is the transformed value of risk driver $k$ for loan $i$, $\beta_k$ are the estimated coefficients and $K$ is the number of risk drivers. The linear part is converted to a score and the score is mapped to rating grades in section 5.4.

5.2 Variable selection

Describe the long list, the univariate analysis, correlation checks, the multivariate selection procedure and the business review. Explain each variable's expected direction and confirm that the estimated sign matches it.

Variables considered and rejected, with reasons: [list].

Example: Region was tested but dropped because its effect disappeared once indexed loan-to-value was included, and it raised concerns about fair treatment. Current account balance trends were dropped because data covered only 40% of borrowers.

5.3 Estimation

Record the software, version, estimation settings, treatment of class imbalance and the coefficient table with standard errors. Attach the full output as an annex, and reference the code path that produces it.

5.4 Rating scale

Map scores to grades on the bank's master scale. Each grade has a calibrated PD (section 6). Explain the number of grades and the boundaries, and show that no grade holds an excessive concentration of exposures.

6. Calibration

Explain the calibration target, the period used to compute it and why the period is representative of the likely range of variability of default rates. For EU IRB models, the target is a long-run average default rate, and EBA/GL/2017/16 (applicable from 2021-01-01) describes how to estimate it and how to quantify a margin of conservatism by category of deficiency. Obtain the guidelines for the normative text.

Example: The long-run average default rate is the average of annual default rates from 2010 to 2024, which includes the tail of the euro-area debt crisis and the 2022 to 2024 rate rises. It equals 1.35%, higher than the 1.20% pooled default rate of the modelling sample because the sample starts in 2014. Loan-level servicing data (DS-01) start in 2014, so the 2010 to 2013 annual default rates use default counts from the default database (DS-03) and portfolio counts reconciled to the finance ledger; the annex documents the denominators and the reconciliation. The calibration scales the score-to-PD relationship so that the portfolio's average predicted PD equals 1.35% on the reference portfolio, then adds a margin of conservatism.

The calibrated portfolio-level PD including the margin is 1.35% plus 0.15 percentage points, which is 1.50%. The grade PDs in section 5.4 are the calibrated PDs before the margin of conservatism: on the reference portfolio (the grade mix of the 2010 to 2024 calibration period) they average 1.35%. For own funds each grade PD is scaled by 1.50 / 1.35 = 1.111 to include the margin (for example, grade 4 moves from 1.00% to 1.11% and grade 6 from 4.00% to 4.44%), subject to the regulatory PD input floor. IFRS 9 staging and pricing use the grade PDs without the margin, because the margin is a regulatory conservatism add-on, not a best estimate.

7. Developmental testing

7.1 Discrimination

The Gini coefficient measures how well the model ranks defaulters above non-defaulters. It relates to the area under the ROC curve (AUC) as:

Each Gini equals twice the AUC minus one (for example, 2 times 0.805 minus 1 equals 0.610).

7.2 Stability

The population stability index (PSI) compares the distribution of a recent population across score bands with the development distribution:

where $e_j$ is the share of the development population in band $j$ and $a_j$ is the share of the recent population.

A PSI of 0.014 is well below the bank's amber threshold of 0.10, so the 2025 population is similar to the development population.

7.3 Calibration test

Compare predicted and observed defaults by grade. A simple test for each grade uses the normal approximation to the binomial distribution:

where $n$ is the number of loans in the grade, $PD$ the grade PD and $O$ the observed number of defaults. Values of $|z|$ above 1.96 indicate a deviation at the 95% level. The table uses the grade PDs before the margin of conservatism, as is common for the accuracy test, and the 2025 cohort of 172,000 loans. The cohort's average predicted PD is 1,967.5 / 172,000 = 1.14%, below the 1.35% central tendency, because the 2025 book holds fewer loans in the high-risk grades than the reference portfolio.

No grade shows a deviation at the 95% level. The portfolio's observed default rate in 2025 (1.13%) is below the long-run average of 1.35%, which is expected in a year with falling unemployment and supports the choice to calibrate to the long-run average rather than to recent years. Note that the per-grade test ignores default correlation, so it tends to flag deviations too often in a year with a common shock; the validator may apply tests that allow for correlation, such as a Vasicek-based confidence interval.

7.4 Sensitivity and benchmarking

Test how outputs change when key inputs and assumptions change, and compare results with an alternative model or external benchmark.

Example: A 10% fall in house prices raises the average predicted PD of the reference portfolio, including the margin, from 1.50% to 1.71% through the indexed loan-to-value driver. Removing the margin of conservatism lowers own funds requirements for the portfolio by about 6% (CRR retail mortgage formula with a 15% asset correlation and constant LGD; the capital engine gives the exact figure). The challenger gradient boosted model ranks 91% of loans within one grade of the scorecard.

8. Assumptions, expert judgement and limitations

Carry the limitations to the Model Card (ai-data/model-card) and the inventory entry, so that users see them before relying on the output.

9. Implementation

Describe the production platform, the data feeds, the scoring frequency and the controls on changes. Record the implementation tests and their results.

10. Ongoing monitoring plan

A red result triggers investigation within the period set in the Model Risk Management Policy (finance/model-risk-management-policy) and a documented response: no action with reasons, overlay, recalibration or redevelopment. Use the Model Monitoring Plan (ai-data/model-monitoring-plan) for the detailed design, data sources and report layout.

11. Regulatory requirements traceability

12. Adaptations for market risk models

For a market risk model, replace the credit-specific content as follows, keeping the same section structure.

• Purpose and scope: desks, books and products covered; risk measure (for example, one-day 99% VaR or 97.5% expected shortfall), holding period and use (limits, own funds, or both).
• Data: risk factor time series, sources, cleaning, proxies for missing factors, and the observation window and weighting.
• Methodology: historical simulation, Monte Carlo or parametric approach; full revaluation or sensitivities; scaling from one day to longer horizons, with the justification.
• Testing: back-testing of actual and hypothetical P&L against the risk measure, P&L attribution where required, risk factor coverage analysis and stress period selection. Back-testing for the Basel framework is set out in chapter MAR32 (cited by name) and, for EU institutions, in the CRR; record live results in the VaR Backtesting Report (finance/var-backtesting-report).
• Limitations: risks not in VaR, illiquid positions, proxy risk factors, and their treatment under the Market Risk Limit Framework (finance/market-risk-limit-framework).

Example: A one-day 99% historical simulation VaR uses 500 days of equally weighted risk factor returns. Over 250 trading days, the expected number of exceptions at 99% is 2.5; observing four exceptions would be within the range the developer should anticipate and explain, not by itself proof of a defect.

13. Glossary and references

References:

• Board of Governors of the Federal Reserve System, FDIC and OCC, Revised Guidance on Model Risk Management (SR 26-2, OCC Bulletin 2026-13, FDIC FIL-15-2026), 2026-04-17 (public domain).
• Prudential Regulation Authority, SS1/23 Model risk management principles for banks, effective 2024-05-17 (cited by name and principle number only).
• European Central Bank, ECB guide to internal models, revised version published 28 July 2025 (replaces the February 2024 version).
• Regulation (EU) No 575/2013 as amended by Regulation (EU) 2024/1623, Articles 178 and 185. EUR-Lex.
• European Banking Authority, Guidelines on PD estimation, LGD estimation and treatment of defaulted assets (EBA/GL/2017/16, published 2017-11-20, applicable from 2021-01-01).
• European Banking Authority, Guidelines on the application of the definition of default (EBA/GL/2016/07), as amended by EBA/GL/2026/05 (applicable from 2026-10-19).
• Basel Committee on Banking Supervision, Basel Framework chapter MAR32 (back-testing), cited by name only.

Standard and licence note

Based on public-domain US federal guidance (SR 26-2, OCC Bulletin 2026-13, FDIC FIL-15-2026) and EU public law and guidelines (Regulation (EU) No 575/2013 as amended, EUR-Lex; EBA guidelines, used with attribution to the European Banking Authority). Structure also aligned with PRA SS1/23 and the ECB guide to internal models, cited by name and number only, and the Basel Framework, cited by chapter name only; no text from these sources is reproduced. All text, formulas presentation and worked examples are original; every number in the example has been recomputed. Fernhollow Bank and its model are fictional. Check the current official version of each guidance and regulation before use. Template licence: MIT.