Innovating Model Validation Processes for Retail Credit Risk Models: Embracing Automation and Efficiency

Automated Steps and Efficiency

1. Data Quality Assessment Codes and Templates

The foundation of any robust model validation process lies in the quality of the underlying data. By developing and implementing multiple data quality checks, financial institutions can quickly identify and report common data issues. These automated checks ensure that the data fed into the models is accurate, consistent, and reliable, thereby enhancing the overall validity of the model outcomes. When validating retail credit risk models, ensuring high data quality is crucial. Here is a non-exhaustive list of data quality checks:

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By systematically applying these checks, financial institutions can ensure that the underlying data driving their retail credit risk models is reliable and robust, ultimately leading to more accurate risk assessments.

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2.) Model Validation Codes and Templates

Automating statistical tests and saving results in ready-to-use templates can significantly expedite the model validation process. The process starts with verifying the source and origin of data from upstream systems, followed by validating it against predefined rules and standards such as data types, formats and logic.

This ensures data completeness, as discussed in Section 1 and helps structure the data into the correct format. Additionally, it aligns the data from multiple upstream sources for seamless post-processing.

Data output validation evaluates the quality and reliability of statistical tests by comparing outputs against expected results and known benchmarks, enabling identification of errors, anomalies and outliers. Results can be documented in a predefined template highlighting key validation metrics.

Relevant statistical tests can be executed with minimal manual intervention, and the results, along with validation summaries and charts, can be readily incorporated into comprehensive reports. This automation not only reduces the potential for human error but also ensures that validation processes are consistently applied across different models.

Retail credit risk models are typically validated by assessing both the ability to distinguish between defaulters and non-defaulters and their accuracy of predicting probabilities. There are numerous tests that can be employed to determine model accuracy. This table should provide a clear at-a-glance reference for the standard tests used in retail credit risk model validation.
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In essence, while there is some overlap in the statistical tools used, the specific tests and validation techniques are tailored to the unique nature and objectives of each model type. For example, many application/behavioural scorecards ultimately map scores to a PD. As a result, the same discrimination and calibration tests (AUC, KS, Hosmer-Lemeshow, etc.) used for PD models also apply to scorecards.

As LGD & EAD Models predict continuous outcomes (loss rate or exposure amount), classification-based discrimination tests (ROC, KS) are not standard. Goodness-of-fit metrics (e.g., MSE, MAE, distribution tests) dominate the validation as well as comparing average or segment-level predictions to actuals and checking for systematic bias is particularly important.

Tracking changes in population characteristics or score distributions over time is critical for all models. Large shifts can signal model drift, requiring recalibration or redevelopment.
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3.) Senior Management Reporting

Preparing dashboards of validations is a time-consuming task that involves copying results from Excel templates to presentation slides. This process can be automated using new technologies, thus saving valuable time for analysts and allowing them to focus on more critical aspects of model validation. Automated tools can extract relevant data from validation reports and generate presentation slides, ensuring that presentations are not only accurate but also visually appealing.


4.) Documentation

Although most financial institutions have or are in the process of standardizing model validation templates the documentation process still consumes large parts of validation analysts’ time. This approach ensures that documentation remains consistent, comprehensive, and easily updatable.

The first two iterations of using the template usually leads to time savings. These savings can increase with the use of automated tooling that picks up “refreshed” data and statistical analysis at every iteration. Finally, GenAI tooling can be wrapped around this process to produce such documents almost instantaneously.

Conclusion:

Innovating the model validation process for retail credit risk models by embracing automation and efficiency can yield substantial benefits. Current challenges explored include data inconsistencies, inaccuracies and aligning data from multiple reference/ upstream systems.

Automated data quality assessments, model validation codes, senior management dashboarding, and standardized document templates not only enhance the accuracy and reliability of the models but also reduce the time and effort required for validation. By adopting these innovative strategies, financial institutions can stay ahead in the ever-evolving landscape of credit risk management, ensuring that their models remain robust and compliant with regulatory standards.