Generative AI and model risk management: new potential for the financial sector

With growing data volumes, increasing computing power and a growing focus on artificial intelligence (AI), AI is expected to play an even more central role in the financial sector. One of the biggest hurdles in the use of AI is model risk management (MRM).

This is because AI models harbour a higher risk of algorithmic bias, data protection offences and discrimination. If a model makes incorrect assumptions, uses inaccurate data or contains methodological errors, this can result in significant financial losses and penalties.

Our white paper "MRM for AI" provides a comprehensive overview of the most relevant governance principles and shows how organizations can deal with AI risks, new requirements regarding transparency, fairness and regulations.

Global regulations such as the EU AI Regulation recommend that financial institutions revise their MRM frameworks. The regulations focus on transparency, fairness and data protection. This means that AI systems must be demonstrably secure. Financial institutions that do not fulfil these requirements not only risk penalties, but also a loss of trust among customers and stakeholders.

The introduction of AI models requires adjustments in many phases of the model risk management life cycle. Important steps include model validation, deployment in controlled environments and ongoing monitoring. Under these conditions, AI applications can be integrated into various business areas, such as lending, investment management and the fulfilment of regulatory reporting obligations.

Our white paper provides detailed insights into the integration of AI into model risk management. It highlights best practices, regulatory requirements and how institutions are using innovative approaches to minimise risk and increase efficiency.