Year: 2025
Type: article
Abstract: From an Internal Audit perspective, the integration of Artificial Intelligence (AI) into credit risk modelling through Machine Learning (ML) algorithms presents significant challenges due to the complexity and multidimensional nature of these models. While AI enhances predictive performance and accuracy, its inherent lack of transparency and explainability increases the risk of control deficiencies, potentially leading to financial losses, misrepresentation of information, unfair discrimination against debtors, and non compliance with EU regulations. This paper introduces a comprehensive audit framework designed to establish robust internal controls over AI-driven credit risk models. Aligned with the Model Risk Management (MRM) lifecycle, we propose a structured set of audit tests and controls, organized by thematic area, to assess key aspects such as model design and performance, governance, reliability, and regulatory compliance. Additionally, we provide practical examples in emerging areas to illustrate their application. These audit procedures aim to identify critical vulnerabilities while ensuring adherence to regulatory standards, including EBA/REP/2023/28 and the evolving requirements of the EU AI Act.
Source: Risk Management Magazine
DOI: 10.47473/2020rmm0149
Authors: Unicredit, Valeria Anna De Palma, Alessandro Di Maria, Unicredit, Daniele Foschini, Unicredit, Vincenzo Frasca, Unicredit, Dario Girardi, Unicredit
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4409285834