A new automated model validation tool for financial institutions

Lingling Fan, Alex Schneider and Mazin Joumaa

Journal of Risk Model Validation

Abstract: We present a new automated validation tool to validate predictive models for financial organizations based on the regulatory guidance of the US Federal Reserve and the Office of the Comptroller of the Currency. This automated tool is designed to help validate linear and logistic regression models. It automatically completes validation processes for seven areas: data sets, model algorithm assumptions, model coefficients and performance, model stability, backtesting, sensitivity testing and stress testing. The tool is packaged as a PYTHON library and can validate models developed in any language, such as PYTHON, R and the SAS language. Further, it can automatically generate a validation report as a portable document format (PDF) file while saving all the generated tables and charts in separate EXCEL and portable network graphic (PNG) files. With this automated tool, validators can standardize model validation procedures, improve efficiency and reduce human error. The tool can also be used during model development.

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