The cards payments industry is moving fast on AI. Forecasting costs and revenue, billing management, performance tracking, process automation — the AI use cases are compelling, the vendors are persuasive, and the investment is following.
But there is an old principle that applies here with uncomfortable precision: garbage in, garbage out. Feed an AI model bad inputs and it will produce bad outputs. No model advancement changes that, and no engineering team can engineer around it. When a model is trained on data that is fragmented, inconsistent, or poorly understood, the outputs will be wrong in ways that are hard to detect. In financial services, that is the definition of dangerous.
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