Opening the “Black Box”

The Path to Deployment of AI Models in Banking

Emerging Trends for Model Validation in the Age of AI

Recent technological advancements have accelerated the integration of AI and machine learning models into more and more banking processes. In today’s banking industry, institutions not using AI and machine learning risk losing their competitive edge, as competitors are increasingle enhancing their strategic decisions with the powerful analytical capabilities of AI and machine learning.

However, due to an increased reliance on models for everyday business processes and decisions, model risk must be effectively managed. If left unchecked, the consequences of model risk can be severe; where model risk is defined as the risk of financial or reputation loss due to errors in the development, implementation or use of models. Therefore, AI and machine learning models require constant monitoring and effective validation. This is not only a regulatory requirement, but it is also sound business practice.

This white paper presents the cornerstones of effective modern model risk management in the age of AI and machine learning by first providing an overview of AI and machine learning in banking, summarizing the regulatory background and the machine learning model lifecycle, and then finally presenting the challenges and emerging best practice for the validation of models, in an ever-changing world of AI and machine learning.

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