A new report from the Alan Turing Institute and the Partnership on AI and Finance (PAIF) identifies emerging risks associated with generative AI in financial services and outlines practical approaches for managing them within established governance frameworks. Drawing on workshops, case studies, and extensive literature review, the research provides guidance for institutions seeking to integrate AI technologies while maintaining reliability, transparency, and regulatory compliance.
The study notes that generative AI introduces risks that traditional model risk management frameworks—such as SR 11-7 and SS1/23—do not fully address. Key areas of concern include the handling of unstructured document content, legal and compliance exposures, and the inherent uncertainty of AI outputs where definitive ground truth may not exist. Researchers highlight that conventional accuracy and validation checks must evolve to incorporate versioning, sensitivity detection, and structured qualitative assessment to ensure robust oversight.
Vendor dependence emerges as a critical operational consideration. Financial institutions increasingly rely on complex external AI ecosystems, which can influence system performance, cost, and operational resilience. Frequent, opaque vendor updates challenge existing review processes, making version tracking, change impact assessments, and continuous vendor oversight essential components of governance.
The report also emphasizes architectural complexity and workflow-level risk. Minor specification gaps or behavioral shifts in a single module can propagate across AI systems, producing performance inconsistencies and unreliable metrics. End-to-end monitoring, dependency inventories, and impact testing between modules are recommended to maintain stability and auditability.
Human-AI interaction constitutes another layer of risk. Misinterpretation or overreliance on AI outputs can create operational vulnerabilities. Integrating human-factor controls—including training, interface design, and operational guardrails—with technical validation is essential for aligning human decision-making with AI capabilities.
The research concludes that the central challenge lies in managing the AI workflow rather than the AI models themselves. Institutions should adopt lifecycle-based approaches that extend model inventories to cover full AI workflows, integrate vendor oversight, strengthen monitoring processes, and embed cross-functional governance spanning risk, data, engineering, and business teams.
According to Lukasz Szpruch, Principal Investigator, the report leverages PAIF members’ deployment experience with state-of-the-art academic research to provide actionable guidance for operationalizing AI model risk management. It offers financial institutions a framework to balance innovation and productivity benefits with reliability, auditability, and regulatory alignment, ensuring that AI adoption remains controlled and transparent.