Financial institutions rely on models for critical day-to-day activities (e.g. pricing, customer facing tools, financial and regulatory reporting, financial crime, and risk management).
Regulators have increased scrutiny, leading to financial institutions implementing more robust model risk management frameworks. However, our view is that model risk management and data management activities are still not well integrated, which can lead to significant risks for organisations.
As more and more organisations start using machine learning models, it's becoming really important for them to connect how they handle their data with how they manage the risks in their models, as the interdependency between models and data grows stronger. This will help make sure that everything is accurate and prevents biases and ethical issues.
This document describes the main environmental drivers for enhancing model and data management and some of the common challenges faced within the industry.
