As financial institutions deploy AI to interact directly with customers, proving that these systems deliver appropriate outcomes is becoming a growing compliance challenge. Aveni is addressing this through an approach that combines pre-deployment testing, real-time monitoring and post-interaction quality assurance to help regulated firms identify risks throughout the AI lifecycle.

As part of the AI in Risk and Compliance 2026 report, co-authored by RegTech Analyst and Parker & Lawrence Research, the research team examined how firms can strengthen oversight as AI becomes more deeply embedded in regulated financial services.

Parker & Lawrence Research interviewed Aveni, a provider of AI-powered oversight and assurance for customer interactions, about the challenges of evaluating AI systems that operate directly with customers.

Traditional AI testing can establish whether a model performs against a benchmark or withstands a particular technical failure, but these measures do not necessarily demonstrate how a system behaves during a real customer interaction. An AI agent might perform well in controlled testing while still failing to recognise vulnerability, responding inappropriately to a customer’s circumstances or contributing to an unsuitable outcome.

The challenge is becoming more urgent as deployment accelerates. 3% of institutions said deployment had outpaced their measured control environment. Existing model risk and quality assurance frameworks may struggle to keep pace with AI agents that conduct extended, dynamic conversations. Pre-deployment testing can miss unusual combinations of language and customer circumstances, while manual reviews and sample-based quality assurance may fail to identify serious but infrequent conduct risks.

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