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Hallucination Risk in Finance: The Three Most Expensive Mistakes
The cost of AI hallucination in financial scenarios is severely underestimated. This article dissects the three most expensive mistakes — fabricating numbers, confusing periods and companies, and filling gaps with inference — explains what causes each one, and gives four implementable lines of defense: mandatory citation, separate statistics, low-confidence abstention, and human review.
When Not to Use AI: Six Scenarios That Will Crash and Burn
The most expensive mistake in AI adoption is deploying it where it never belonged. This article covers six high-probability failure scenarios — from accuracy requirements with no human review, to rule-certain repetitive work, sensitive data, legal liability, ill-defined tasks, and volumes too small to justify a pipeline — explaining for each why it fails, what to use instead, and under what conditions to revisit the decision.