Model Quality Assurance P&C - Automated Model Comparison Engine
Purpose
Model Quality Assurance P&C - Automated Model Comparison Engine is designed to reduce the time spent on model review by providing a go/no-go recommendation to initiate the review phase, identifying strengths and weaknesses across key components of the model package, and supporting the understanding of differences between consecutive documentation package versions.
Primary users
Primary users are not specified in the provided information. Based on the stated use case, the agent supports stakeholders involved in model quality assurance, model review, validation preparation, and review of model documentation, code, data, methodology, remediation actions, and opinion packages.
Where it fits (process/stage/trigger)
The agent fits before and during the model review phase, especially when a model package is submitted or resubmitted and needs to be assessed for completeness, quality, differences versus a previous version, consistency across related models, and readiness for validation review or committee preparation.
Key capabilities / workflow
The workflow covers file synthesis, identification of missing files, comparison with previous documentation package versions, analysis of products associated with the dataset, data quality review, code analysis, modeling methodology assessment, follow-up on remediation actions, consistency checks between models of similar intent, review of the opinion package, and preparation of the validation deck.
Inputs
Inputs include the MRM framework such as policy, handbook, and gatekeeper materials, model documentation, data documentation including data lineage, data dictionary, and dataset production code, codes and associated documentation, data such as inputs, RDS, MDS, OOS, OOT, parameters, expert judgment, and scenarios, intermediate results such as data quality analysis before or after remediation and data transformation, and model outputs. No dataset is currently specified, although a dataset of modeling regulations could be integrated.
Outputs / Deliverables
Outputs include a go/no-go recommendation to initiate model review, a list of transferred files with an indication of missing files, a list of differences with a previous version to prioritize review work, questions or opinions on the product, business process, and business evolution, questions or opinions on data quality, questions or opinions on code used in the exercise, questions or opinions on the modeling approach used or implemented, questions or opinions on answers to remediation actions, questions on inconsistencies across versions or submissions, and a consistency report on the opinion package, updated opinion package, and presentation deck for committees.
Value
The agent provides value by accelerating model review readiness assessment, helping reviewers focus on material differences and weaknesses, improving consistency across submissions and related models, and supporting preparation of review outputs such as opinions, consistency reports, and committee presentation materials.
