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Eval matrix proposed for financial-services voice AI agents to catch compliance failures

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A practical evaluation framework has been proposed for financial-services voice AI agents used in banking, lending, insurance, and fintech. The matrix argues that such agents pose risks not because they speak, but because they can sound confident while making operational or compliance errors that generic chatbot evaluations miss. It recommends scoring four layers: conversation behavior, policy boundaries, tool and trace behavior, and handoff evidence. The framework covers ten scenarios, including identity verification, debt disputes, hardship handling, prompt-injection attempts, and CRM note accuracy, each with defined pass conditions and high-severity failure markers. The author emphasizes that a polite transcript and a correct system trace must both be reviewed together, as either alone can conceal a failure.

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Eval matrix proposed for financial-services voice AI agents to catch compliance failures · ShortSingh