Trust, Risk and Quality in Agentic Finance [Testμ 2026]

At Testμ Conf 2026, financial-services practitioners argued that AI agents should receive autonomy only when it is earned through evidence, testing, supervision, and controls proportionate to risk. Current uses focus largely on operational and reversible tasks, such as low-value duplicate-transaction reversals, account maintenance, adviser preparation, first-pass compliance reviews, and software engineering support, while consequential or irreversible financial decisions remain subject to human approval. Panelists recommended defining an agent’s prohibited actions and escalation rules before building capabilities, deploying agents in shadow mode alongside human workflows, evaluating non-deterministic outputs with representative golden datasets and rubrics, and maintaining detailed records of prompts, inputs, model versions, outputs, oversight, changes, and failures. They emphasized that existing financial regulations do not yet specifically govern autonomous agents, so institutions should treat agents as regulated actors and build systems that can be supervised, explained, defended, rolled back, and stopped. The discussion also highlighted risks of reviewer fatigue and overtrust, suggesting adversarial AI review, deliberate error-injection exercises, continuous monitoring, and training data that reflects messy real-world customer behavior rather than idealized cases.

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