Journal of Operational Risk
ISSN:
1755-2710 (online)
Editor-in-chief: Marcelo Cruz
Model validation of a generative-artificial-intelligence-based avatar for customer support in banking
Jochen Gerhard, Martin Gombert, Björn Henrich and James Smith
Need to know
- Model risk management is key for ensuring trustworthiness in AI-based virtual assistants. It plays a strategic role by engaging in independent validation and managing risks specific to AI applications.
- Traditional model validation approaches fall short for generative AI. We introduce a systematic framework with guardrails focusing on human oversight, fairness, transparency, and reliability, tailored the challenges of GenAI.
- Our framework aligns the theoretical dimension with practical processes like rigorous testing, continuous monitoring, lifecycle management, and risk scenario assessment.
- Vendor-provided GenAI systems inherently carry higher residual risks due to opacity, probabilistic outputs, and complexity.
Abstract
This study presents an innovative validation approach for a generative-artificial-intelligence-based avatar named Ava, designed to handle customer inquiries in the banking sector. Classical model validation frameworks fall short when applied to generative artificial intelligence models due to the opaque, black-box nature of such models. This paper introduces a systematic framework of guardrails that emphasize trustworthiness, including rigorous testing, real-time monitoring, scenario assessment and effective governance. Our validation approach ensures Ava adheres to principles of human oversight, fairness, transparency and reliability, which are vital for safe use in financial services. Using Commerzbank’s Ava as a case study, the framework provides insights into achieving trustworthy artificial intelligence in highly regulated sectors, balancing innovation and compliance.
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